Deploy render worker from GitHub Actions

Dockerfile

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+# Same runtime as API; runs health endpoint + Celery worker (see worker_health.py)
+FROM python:3.11-slim-bookworm
+
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_ROOT_USER_ACTION=ignore \
+    NO_ALBUMENTATIONS_UPDATE=1 \
+    OMP_NUM_THREADS=1 \
+    MKL_NUM_THREADS=1 \
+    OPENBLAS_NUM_THREADS=1
+
+WORKDIR /app
+ENV PYTHONPATH=/app
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ffmpeg \
+    pkg-config \
+    cmake \
+    libcairo2 \
+    libcairo2-dev \
+    libpango-1.0-0 \
+    libpango1.0-dev \
+    libpangocairo-1.0-0 \
+    libgdk-pixbuf-2.0-0 \
+    libffi-dev \
+    python3-dev \
+    texlive-latex-base \
+    texlive-fonts-recommended \
+    texlive-latex-extra \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+COPY . .
+
+RUN python scripts/prewarm_models.py
+
+ENV PORT=7860 \
+    CELERY_WORKER_QUEUES=render
+EXPOSE 7860
+
+ENTRYPOINT []
+CMD ["sh", "-c", "exec python3 -u worker_health.py"]

Dockerfile.worker.ocr

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+# Celery worker: OCR queue only (no Manim / LaTeX / Cairo stack).
+FROM python:3.11-slim-bookworm
+
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_ROOT_USER_ACTION=ignore \
+    NO_ALBUMENTATIONS_UPDATE=1 \
+    OMP_NUM_THREADS=1 \
+    MKL_NUM_THREADS=1 \
+    OPENBLAS_NUM_THREADS=1
+
+WORKDIR /app
+ENV PYTHONPATH=/app
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    cmake \
+    pkg-config \
+    python3-dev \
+    libglib2.0-0 \
+    libgomp1 \
+    libgl1 \
+    libsm6 \
+    libxext6 \
+    libxrender1 \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.worker-ocr.txt .
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.worker-ocr.txt
+
+COPY . .
+
+RUN python scripts/prewarm_ocr_worker.py
+
+ENV PORT=7860 \
+    CELERY_WORKER_QUEUES=ocr
+EXPOSE 7860
+
+ENTRYPOINT []
+CMD ["sh", "-c", "exec python3 -u worker_health.py"]

README.md

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+---
+title: Math Solver Render Worker
+emoji: 👷
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+app_port: 7860
+---
+
+# Math Solver — Render worker (Manim)
+
+This Space runs **Celery** via `worker_health.py` and consumes **only** queue **`render`** (`render_geometry_video`). Image sets `CELERY_WORKER_QUEUES=render` by default (`Dockerfile.worker`).
+
+**Solve** (orchestrator, agents, OCR-in-request when `OCR_USE_CELERY` is off) runs on the **API** Space, not on this worker.
+
+## OCR offload (separate Space)
+
+Queue **`ocr`** is handled by a **dedicated OCR worker** (`Dockerfile.worker.ocr`, `README_HF_WORKER_OCR.md`, workflow `deploy-worker-ocr.yml`). On the API, set `OCR_USE_CELERY=true` and deploy an OCR Space that listens on `ocr`.
+
+## Secrets
+
+Same broker as the API: `REDIS_URL` / `CELERY_BROKER_URL`, Supabase, OpenRouter (renderer may use LLM paths), etc.
+
+**GitHub Actions:** repository secrets `HF_TOKEN` and `HF_WORKER_REPO` (`owner/space-name`) for this workflow (`deploy-worker.yml`).

README_HF_WORKER_OCR.md

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+---
+title: Math Solver OCR Worker
+emoji: 👁️
+colorFrom: gray
+colorTo: blue
+sdk: docker
+app_port: 7860
+---
+
+# Math Solver — OCR-only worker
+
+This Space runs **Celery** (`worker_health.py`) consuming **only** the `ocr` queue.
+
+Set environment:
+
+- `CELERY_WORKER_QUEUES=ocr` (default in `Dockerfile.worker.ocr`)
+- Same `REDIS_URL` / `CELERY_BROKER_URL` / `CELERY_RESULT_BACKEND` as the API
+
+This Space runs **raw OCR only** (YOLO, PaddleOCR, Pix2Tex). **OpenRouter / LLM tinh chỉnh** không chạy ở đây; API Space gọi `refine_with_llm` sau khi nhận kết quả từ queue `ocr`.
+
+On the **API** Space, set `OCR_USE_CELERY=true` so `run_ocr_from_url` tasks are sent to this worker instead of running Paddle/Pix2Tex on the API process.
+
+Optional: `OCR_CELERY_TIMEOUT_SEC` (default `180`).
+
+**Manim / video** uses a different Celery queue (`render`) and Space — see `README_HF_WORKER.md` and workflow `deploy-worker.yml`.
+
+GitHub Actions: repository secrets `HF_TOKEN` and `HF_OCR_WORKER_REPO` (`owner/space-name`) enable workflow `deploy-worker-ocr.yml`.

agents/geometry_agent.py

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+import os
+import json
+import logging
+from openai import AsyncOpenAI
+from typing import Dict, Any
+from dotenv import load_dotenv
+
+load_dotenv()
+logger = logging.getLogger(__name__)
+
+from app.url_utils import openai_compatible_api_key, sanitize_env
+from app.llm_client import get_llm_client
+
+
+class GeometryAgent:
+    def __init__(self):
+        self.llm = get_llm_client()
+
+    async def generate_dsl(self, semantic_data: Dict[str, Any], previous_dsl: str = None) -> str:
+        logger.info("==[GeometryAgent] Generating DSL from semantic data==")
+        if previous_dsl:
+            logger.info(f"[GeometryAgent] Using previous DSL context (len={len(previous_dsl)})")
+
+        system_prompt = """
+You are a Geometry DSL Generator. Convert semantic geometry data into a precise Geometry DSL program.
+
+=== MULTI-TURN CONTEXT ===
+If a PREVIOUS DSL is provided, your job is to UPDATE or EXTEND it.
+1. DO NOT remove existing points unless the user explicitly asks to "redefine" or "move" them.
+2. Ensure new segments/points connect correctly to existing ones.
+3. Your output should be the ENTIRE updated DSL, not just the changes.
+
+=== DSL COMMANDS ===
+POINT(A)                    — declare a point
+POINT(A, x, y, z)           — declare a point with explicit coordinates
+LENGTH(AB, 5)               — distance between A and B is 5 (2D/3D)
+ANGLE(A, 90)                — interior angle at vertex A is 90° (2D/3D)
+PARALLEL(AB, CD)            — segment AB is parallel to CD (2D/3D)
+PERPENDICULAR(AB, CD)       — segment AB is perpendicular to CD (2D/3D)
+MIDPOINT(M, AB)             — M is the midpoint of segment AB
+SECTION(E, A, C, k)         — E satisfies vector AE = k * vector AC (k is decimal)
+LINE(A, B)                  — infinite line passing through A and B
+RAY(A, B)                   — ray starting at A and passing through B
+CIRCLE(O, 5)                — circle with center O and radius 5 (2D)
+SPHERE(O, 5)                — sphere with center O and radius 5 (3D)
+SEGMENT(M, N)               — auxiliary segment MN to be drawn
+POLYGON_ORDER(A, B, C, D)   — the order in which vertices form the polygon boundary
+TRIANGLE(ABC)               — equilateral/arbitrary triangle
+PYRAMID(S_ABCD)             — pyramid with apex S and base ABCD
+PRISM(ABC_DEF)              — triangular prism
+
+=== RULES ===
+1. 3D Coordinates: Use POINT(A, x, y, z) if specific coordinates are given in the problem.
+2. Space Geometry: For pyramids/prisms, use the specialized commands.
+3. Primary Vertices: Always declare the main vertices of the shape (e.g., A, B, C, D) using POINT(X).
+4. POLYGON_ORDER: Always emit POLYGON_ORDER(...) for the main shape using ONLY these primary vertices.
+5. All Points: EVERY point mentioned (A, B, C, H, M, etc.) MUST be declared with POINT(Name) first.
+6. Altitudes/Perpendiculars: For an altitude AH to BC, use POINT(H) + PERPENDICULAR(AH, BC).
+7. Format: Output ONLY DSL lines — NO explanation, NO markdown, NO code blocks.
+
+=== SHAPE EXAMPLES ===
+
+--- Case: Square Pyramid S.ABCD with side 10, height 15 ---
+PYRAMID(S_ABCD)
+POINT(A, 0, 0, 0)
+POINT(B, 10, 0, 0)
+POINT(C, 10, 10, 0)
+POINT(D, 0, 10, 0)
+POINT(S)
+POINT(O)
+SECTION(O, A, C, 0.5)
+LENGTH(SO, 15)
+PERPENDICULAR(SO, AC)
+PERPENDICULAR(SO, AB)
+POLYGON_ORDER(A, B, C, D)
+
+--- Case: Right Triangle ABC at A, AB=3, AC=4, altitude AH ---
+POLYGON_ORDER(A, B, C)
+POINT(A)
+POINT(B)
+POINT(C)
+POINT(H)
+LENGTH(AB, 3)
+LENGTH(AC, 4)
+ANGLE(A, 90)
+PERPENDICULAR(AH, BC)
+SEGMENT(A, H)
+
+--- Case: Rectangle ABCD with AB=5, AD=10 ---
+POLYGON_ORDER(A, B, C, D)
+POINT(A)
+POINT(B)
+POINT(C)
+POINT(D)
+LENGTH(AB, 5)
+LENGTH(AD, 10)
+PERPENDICULAR(AB, AD)
+PARALLEL(AB, CD)
+PARALLEL(AD, BC)
+
+[Circle with center O radius 7]
+POINT(O)
+CIRCLE(O, 7)
+"""
+
+        user_content = f"Semantic Data: {json.dumps(semantic_data, ensure_ascii=False)}"
+        if previous_dsl:
+            user_content = f"PREVIOUS DSL:\n{previous_dsl}\n\nUPDATE WITH NEW DATA: {json.dumps(semantic_data, ensure_ascii=False)}"
+
+        logger.debug("[GeometryAgent] Calling LLM (Multi-Layer)...")
+        content = await self.llm.chat_completions_create(
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_content}
+            ]
+        )
+        dsl = content.strip() if content else ""
+        logger.info(f"[GeometryAgent] DSL generated ({len(dsl.splitlines())} lines).")
+        logger.debug(f"[GeometryAgent] DSL output:\n{dsl}")
+        return dsl

agents/knowledge_agent.py

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+import logging
+from typing import Dict, Any
+
+logger = logging.getLogger(__name__)
+
+
+# ─── Shape rule registry ────────────────────────────────────────────────────
+# Each entry: keyword list → augmentation function
+# Augmentation receives (values: dict, text: str) and returns updated values dict.
+
+class KnowledgeAgent:
+    """Knowledge Agent: Stores geometric theorems and common patterns to augment Parser output."""
+
+    def augment_semantic_data(self, semantic_data: Dict[str, Any]) -> Dict[str, Any]:
+        logger.info("==[KnowledgeAgent] Augmenting semantic data==")
+        text = str(semantic_data.get("input_text", "")).lower()
+        logger.debug(f"[KnowledgeAgent] Input text for matching: '{text[:200]}'")
+
+        shape_type = self._detect_shape(text, semantic_data.get("type", ""))
+        if shape_type:
+            semantic_data["type"] = shape_type
+            values = semantic_data.get("values", {})
+            values = self._augment_values(shape_type, values, text)
+            semantic_data["values"] = values
+        else:
+            logger.info("[KnowledgeAgent] No special rule matched. Returning data unchanged.")
+
+        logger.debug(f"[KnowledgeAgent] Output semantic data: {semantic_data}")
+        return semantic_data
+
+    # ─── Shape detection ────────────────────────────────────────────────────
+    def _detect_shape(self, text: str, llm_type: str) -> str | None:
+        """Detect shape from text keywords. LLM type provides a hint."""
+        checks = [
+            (["hình vuông", "square"],                      "square"),
+            (["hình chữ nhật", "rectangle"],                "rectangle"),
+            (["hình thoi", "rhombus"],                      "rhombus"),
+            (["hình bình hành", "parallelogram"],            "parallelogram"),
+            (["hình thang vuông"],                           "right_trapezoid"),
+            (["hình thang", "trapezoid", "trapezium"],       "trapezoid"),
+            (["tam giác vuông", "right triangle"],           "right_triangle"),
+            (["tam giác đều", "equilateral triangle", "equilateral"], "equilateral_triangle"),
+            (["tam giác cân", "isosceles"],                  "isosceles_triangle"),
+            (["tam giác", "triangle"],                       "triangle"),
+            (["đường tròn", "circle"],                       "circle"),
+        ]
+        for keywords, shape in checks:
+            if any(kw in text for kw in keywords):
+                logger.info(f"[KnowledgeAgent] Rule MATCH: '{shape}' detected (keyword match).")
+                return shape
+
+        # Fallback: trust LLM-detected type if it's a known type
+        known = {
+            "rectangle", "square", "rhombus", "parallelogram",
+            "trapezoid", "right_trapezoid", "triangle", "right_triangle",
+            "equilateral_triangle", "isosceles_triangle", "circle",
+        }
+        if llm_type in known:
+            logger.info(f"[KnowledgeAgent] Using LLM-detected type '{llm_type}'.")
+            return llm_type
+
+        return None
+
+    # ─── Value augmentation ──────────────────────────────────────────────────
+    def _augment_values(self, shape: str, values: dict, text: str) -> dict:
+        ab = values.get("AB")
+        ad = values.get("AD")
+        bc = values.get("BC")
+        cd = values.get("CD")
+
+        if shape == "rectangle":
+            if ab and ad:
+                values.setdefault("CD", ab)
+                values.setdefault("BC", ad)
+                values.setdefault("angle_A", 90)
+                logger.info(f"[KnowledgeAgent] Rectangle: AB=CD={ab}, AD=BC={ad}, angle_A=90°")
+            else:
+                values.setdefault("angle_A", 90)
+
+        elif shape == "square":
+            side = ab or ad or bc or cd or values.get("side")
+            if side:
+                values.update({"AB": side, "AD": side, "angle_A": 90})
+                logger.info(f"[KnowledgeAgent] Square: side={side}, angle_A=90°")
+            else:
+                values.setdefault("angle_A", 90)
+
+        elif shape == "rhombus":
+            side = ab or values.get("side")
+            if side:
+                values.update({"AB": side, "BC": side, "CD": side, "DA": side})
+                logger.info(f"[KnowledgeAgent] Rhombus: all sides={side}")
+
+        elif shape == "parallelogram":
+            if ab:
+                values.setdefault("CD", ab)
+            if ad:
+                values.setdefault("BC", ad)
+            logger.info(f"[KnowledgeAgent] Parallelogram: AB||CD, AD||BC")
+
+        elif shape == "trapezoid":
+            logger.info("[KnowledgeAgent] Trapezoid: AB||CD (bottom||top)")
+
+        elif shape == "right_trapezoid":
+            logger.info("[KnowledgeAgent] Right trapezoid: AB||CD, AD⊥AB")
+            values.setdefault("angle_A", 90)
+
+        elif shape == "equilateral_triangle":
+            side = ab or values.get("side")
+            if side:
+                values.update({"AB": side, "BC": side, "CA": side, "angle_A": 60})
+                logger.info(f"[KnowledgeAgent] Equilateral triangle: all sides={side}, angle_A=60°")
+
+        elif shape == "right_triangle":
+            # Try to infer which vertex is the right angle
+            rt_vertex = _detect_right_angle_vertex(text)
+            values.setdefault(f"angle_{rt_vertex}", 90)
+            logger.info(f"[KnowledgeAgent] Right triangle: angle_{rt_vertex}=90°")
+
+        elif shape == "isosceles_triangle":
+            logger.info("[KnowledgeAgent] Isosceles triangle: AB=AC (default, LLM may override)")
+
+        elif shape == "circle":
+            logger.info("[KnowledgeAgent] Circle detected — no side augmentation needed.")
+
+        return values
+
+
+def _detect_right_angle_vertex(text: str) -> str:
+    """Heuristic: detect which vertex is right angle from text."""
+    for vertex in ["A", "B", "C", "D"]:
+        patterns = [f"vuông tại {vertex}", f"góc {vertex} vuông", f"right angle at {vertex}"]
+        if any(p.lower() in text for p in patterns):
+            return vertex
+    return "A"  # default

agents/ocr_agent.py

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+import os
+import uuid
+import logging
+import asyncio
+from typing import List, Dict, Any, Optional, Tuple
+
+import cv2
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+_OCR_MAX_EDGE = 2000
+_CROP_PAD = 4
+
+
+class ImprovedOCRAgent:
+    """
+    Advanced OCR Agent using a hybrid pipeline:
+    1. YOLO for layout analysis (text vs formula).
+    2. PaddleOCR for Vietnamese text extraction.
+    3. Pix2Tex for LaTeX formula extraction.
+    4. Optional MegaLLM for semantic correction and formatting (skipped when ``skip_llm_refinement`` is True,
+       e.g. on the dedicated OCR Celery worker; the API Space runs ``refine_with_llm`` on the raw text).
+    """
+
+    def __init__(self, skip_llm_refinement: bool = False):
+        self._skip_llm_refinement = bool(skip_llm_refinement)
+        logger.info("[ImprovedOCRAgent] Initializing engines (skip_llm_refinement=%s)...", self._skip_llm_refinement)
+
+        if self._skip_llm_refinement:
+            self.llm = None
+            logger.info("[ImprovedOCRAgent] LLM client skipped (raw OCR only).")
+        else:
+            from app.llm_client import get_llm_client
+
+            self.llm = get_llm_client()
+            logger.info("[ImprovedOCRAgent] Multi-Layer LLM Client initialized.")
+
+        try:
+            from agents.torch_ultralytics_compat import allow_ultralytics_weights
+            from ultralytics import YOLO
+
+            allow_ultralytics_weights()
+            logger.info("[ImprovedOCRAgent] Loading YOLO...")
+            self.layout_model = YOLO("yolov8n.pt")
+            logger.info("[ImprovedOCRAgent] YOLO initialized.")
+        except Exception as e:
+            logger.error("[ImprovedOCRAgent] YOLO init failed: %s", e)
+            self.layout_model = None
+
+        try:
+            from paddleocr import PaddleOCR
+
+            logger.info("[ImprovedOCRAgent] Loading PaddleOCR...")
+            self.text_model = PaddleOCR(use_angle_cls=True, lang="vi")
+            logger.info("[ImprovedOCRAgent] PaddleOCR (vi) initialized.")
+        except Exception as e:
+            logger.error("[ImprovedOCRAgent] PaddleOCR init failed: %s", e)
+            self.text_model = None
+
+        try:
+            from pix2tex.cli import LatexOCR
+
+            logger.info("[ImprovedOCRAgent] Loading Pix2Tex...")
+            self.math_model = LatexOCR()
+            logger.info("[ImprovedOCRAgent] Pix2Tex initialized.")
+        except Exception as e:
+            logger.error("[ImprovedOCRAgent] Pix2Tex init failed: %s", e)
+            self.math_model = None
+
+    def _preprocess_image_for_ocr(self, src_path: str) -> Tuple[str, bool]:
+        """Resize large images, CLAHE on luminance; returns path (may be new temp file)."""
+        img = cv2.imread(src_path, cv2.IMREAD_COLOR)
+        if img is None:
+            g = cv2.imread(src_path, cv2.IMREAD_GRAYSCALE)
+            if g is None:
+                logger.warning("[ImprovedOCRAgent] OpenCV could not read %s; using original.", src_path)
+                return src_path, False
+            img = cv2.cvtColor(g, cv2.COLOR_GRAY2BGR)
+        h, w = img.shape[:2]
+        max_dim = max(h, w)
+        if max_dim > _OCR_MAX_EDGE:
+            scale = _OCR_MAX_EDGE / max_dim
+            img = cv2.resize(
+                img, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA
+            )
+            logger.info("[ImprovedOCRAgent] Resized for OCR to max edge %s", _OCR_MAX_EDGE)
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        gray = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)).apply(gray)
+        den = cv2.fastNlMeansDenoising(gray, None, 8, 7, 21)
+        out = f"temp_ocr_prep_{uuid.uuid4().hex}.png"
+        cv2.imwrite(out, den)
+        return out, True
+
+    def _load_bgr_for_crops(self, path: str) -> Optional[np.ndarray]:
+        im = cv2.imread(path, cv2.IMREAD_COLOR)
+        if im is None:
+            g = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
+            if g is None:
+                return None
+            im = cv2.cvtColor(g, cv2.COLOR_GRAY2BGR)
+        return im
+
+    def _crop_from_quad(self, img_bgr: np.ndarray, bbox) -> Optional[np.ndarray]:
+        try:
+            pts = np.array(bbox, dtype=np.float32)
+            xs = pts[:, 0]
+            ys = pts[:, 1]
+            H, W = img_bgr.shape[:2]
+            x1 = max(0, int(xs.min()) - _CROP_PAD)
+            y1 = max(0, int(ys.min()) - _CROP_PAD)
+            x2 = min(W, int(xs.max()) + _CROP_PAD)
+            y2 = min(H, int(ys.max()) + _CROP_PAD)
+            if x2 <= x1 or y2 <= y1:
+                return None
+            return img_bgr[y1:y2, x1:x2].copy()
+        except Exception as e:
+            logger.debug("[ImprovedOCRAgent] crop failed: %s", e)
+            return None
+
+    def _latex_from_crop_bgr(self, crop_bgr: np.ndarray) -> Optional[str]:
+        if self.math_model is None or crop_bgr is None or crop_bgr.size == 0:
+            return None
+        ch, cw = crop_bgr.shape[:2]
+        if ch < 10 or cw < 10:
+            return None
+        try:
+            from PIL import Image
+
+            rgb = cv2.cvtColor(crop_bgr, cv2.COLOR_BGR2RGB)
+            pil = Image.fromarray(rgb)
+            out = self.math_model(pil)
+            if isinstance(out, str) and out.strip():
+                return out.strip()
+        except Exception as e:
+            logger.debug("[ImprovedOCRAgent] Pix2Tex on crop failed: %s", e)
+        return None
+
+    def _maybe_math_from_crop(self, img_bgr: Optional[np.ndarray], bbox, text: str) -> str:
+        if img_bgr is None or not self.math_model:
+            return text
+        is_math_hint = any(
+            c in text for c in ["\\", "^", "_", "{", "}", "=", "+", "-", "*", "/"]
+        )
+        if not is_math_hint:
+            return text
+        crop = self._crop_from_quad(img_bgr, bbox)
+        latex = self._latex_from_crop_bgr(crop) if crop is not None else None
+        if latex:
+            logger.info("[ImprovedOCRAgent] Pix2Tex replaced line fragment (len=%s)", len(latex))
+            return f"${latex}$"
+        return text
+
+    async def process_image(self, image_path: str) -> str:
+        logger.info("==[ImprovedOCRAgent] Processing: %s==", image_path)
+
+        if not os.path.exists(image_path):
+            return f"Error: File {image_path} not found."
+
+        prep_path, prep_cleanup = self._preprocess_image_for_ocr(image_path)
+        paddle_path = prep_path if prep_cleanup else image_path
+        img_bgr = self._load_bgr_for_crops(prep_path if prep_cleanup else image_path)
+
+        raw_fragments: List[Dict[str, Any]] = []
+
+        try:
+            if self.text_model:
+                logger.info("[ImprovedOCRAgent] Running PaddleOCR on %s...", paddle_path)
+                result = self.text_model.ocr(paddle_path)
+                logger.info("[ImprovedOCRAgent] PaddleOCR raw result: %s", result)
+
+                if not result:
+                    logger.warning("[ImprovedOCRAgent] PaddleOCR returned no results.")
+                    return ""
+
+                if isinstance(result[0], dict):
+                    res_dict = result[0]
+                    rec_texts = res_dict.get("rec_texts", [])
+                    rec_scores = res_dict.get("rec_scores", [])
+                    rec_polys = res_dict.get("rec_polys", [])
+
+                    for i in range(len(rec_texts)):
+                        text = rec_texts[i]
+                        bbox = rec_polys[i]
+                        score = rec_scores[i] if i < len(rec_scores) else None
+                        if score is not None and float(score) < 0.45:
+                            logger.debug(
+                                "[ImprovedOCRAgent] Low-confidence line (score=%s): %s",
+                                score,
+                                text[:80],
+                            )
+
+                        y_top = int(min(p[1] for p in bbox)) if hasattr(bbox, "__iter__") else 0
+                        content = self._maybe_math_from_crop(img_bgr, bbox, text)
+                        raw_fragments.append({"y": y_top, "content": content, "type": "text"})
+                elif isinstance(result[0], list):
+                    for line in result[0]:
+                        bbox = line[0]
+                        text = line[1][0]
+                        score = line[1][1] if len(line[1]) > 1 else None
+                        if score is not None and float(score) < 0.45:
+                            logger.debug(
+                                "[ImprovedOCRAgent] Low-confidence line (score=%s): %s",
+                                score,
+                                text[:80],
+                            )
+
+                        y_top = int(bbox[0][1])
+                        content = self._maybe_math_from_crop(img_bgr, bbox, text)
+                        raw_fragments.append({"y": y_top, "content": content, "type": "text"})
+        finally:
+            if prep_cleanup and os.path.exists(prep_path):
+                try:
+                    os.remove(prep_path)
+                except OSError:
+                    pass
+
+        raw_fragments.sort(key=lambda x: x["y"])
+        combined_text = "\n".join([f["content"] for f in raw_fragments])
+
+        logger.info(
+            "[ImprovedOCRAgent] Raw OCR output assembled:\n---\n%s\n---", combined_text
+        )
+
+        if not combined_text.strip():
+            logger.warning("[ImprovedOCRAgent] No text detected to refine.")
+            return ""
+
+        if self._skip_llm_refinement or self.llm is None:
+            logger.info("[ImprovedOCRAgent] Skipping MegaLLM refinement (raw OCR output).")
+            return combined_text
+
+        try:
+            logger.info("[ImprovedOCRAgent] Sending to MegaLLM for refinement...")
+            refined_text = await asyncio.wait_for(
+                self.refine_with_llm(combined_text), timeout=30.0
+            )
+            return refined_text
+        except asyncio.TimeoutError:
+            logger.error("[ImprovedOCRAgent] MegaLLM refinement timed out.")
+            return combined_text
+        except Exception as e:
+            logger.error("[ImprovedOCRAgent] MegaLLM refinement failed: %s", e)
+            return combined_text
+
+    async def refine_with_llm(self, text: str) -> str:
+        if not text.strip():
+            return ""
+        if self.llm is None:
+            logger.warning("[ImprovedOCRAgent] refine_with_llm: no LLM client; returning raw text.")
+            return text
+
+        prompt = f"""Bạn là một chuyên gia số hóa tài liệu toán học.
+Dưới đây là kết quả OCR thô từ một trang sách toán Tiếng Việt.
+Kết quả này có thể chứa lỗi chính tả, lỗi định dạng mã LaTeX, hoặc bị ngắt quãng không logic.
+
+Nhiệm vụ của bạn:
+1. Sửa lỗi chính tả tiếng Việt.
+2. Đảm bảo các công thức toán học được viết đúng định dạng LaTeX và nằm trong cặp dấu $...$.
+3. Giữ nguyên cấu trúc logic của bài toán.
+4. Trả về nội dung đã được làm sạch dưới dạng Markdown.
+
+Nội dung OCR thô:
+---
+{text}
+---
+
+Kết quả làm sạch:"""
+
+        try:
+            refined = await self.llm.chat_completions_create(
+                messages=[{"role": "user", "content": prompt}],
+                temperature=0.1,
+            )
+            logger.info("[ImprovedOCRAgent] LLM refinement complete.")
+            return refined
+        except Exception as e:
+            logger.error("[ImprovedOCRAgent] LLM refinement failed: %s", e)
+            return text
+
+    async def process_url(self, url: str) -> str:
+        import httpx
+
+        from app.url_utils import sanitize_url
+
+        url = sanitize_url(url)
+        if not url:
+            return "Error: Empty image URL after cleanup."
+
+        async with httpx.AsyncClient() as client:
+            resp = await client.get(url)
+            if resp.status_code == 200:
+                temp_path = "temp_url_image.png"
+                with open(temp_path, "wb") as f:
+                    f.write(resp.content)
+                try:
+                    return await self.process_image(temp_path)
+                finally:
+                    if os.path.exists(temp_path):
+                        os.remove(temp_path)
+            return f"Error: Failed to fetch image from URL {url}"
+
+
+class OCRAgent(ImprovedOCRAgent):
+    """Alias for compatibility with existing code."""
+
+    pass

agents/orchestrator.py

@@ -0,0 +1,219 @@
+import json
+import logging
+from typing import Any, Dict
+
+from agents.geometry_agent import GeometryAgent
+from agents.knowledge_agent import KnowledgeAgent
+from agents.ocr_agent import OCRAgent
+from agents.parser_agent import ParserAgent
+from agents.renderer_agent import RendererAgent
+from agents.solver_agent import SolverAgent
+from app.logutil import log_step
+from app.ocr_celery import ocr_from_image_url
+from solver.dsl_parser import DSLParser
+from solver.engine import GeometryEngine
+
+logger = logging.getLogger(__name__)
+
+_CLIP = 2000
+
+
+def _clip(val: Any, n: int = _CLIP) -> str | None:
+    if val is None:
+        return None
+    if isinstance(val, str):
+        s = val
+    else:
+        s = json.dumps(val, ensure_ascii=False, default=str)
+    return s if len(s) <= n else s[:n] + "…"
+
+
+def _step_io(step: str, input_val: Any = None, output_val: Any = None) -> None:
+    """Debug: chỉ input/output (đã cắt), tránh dump dài dòng không cần thiết."""
+    log_step(step, input=_clip(input_val), output=_clip(output_val))
+
+
+class Orchestrator:
+    def __init__(self):
+        self.parser_agent = ParserAgent()
+        self.geometry_agent = GeometryAgent()
+        self.ocr_agent = OCRAgent()
+        self.knowledge_agent = KnowledgeAgent()
+        self.renderer_agent = RendererAgent()
+        self.solver_agent = SolverAgent()
+        self.solver_engine = GeometryEngine()
+        self.dsl_parser = DSLParser()
+
+    def _generate_step_description(self, semantic_json: Dict[str, Any], engine_result: Dict[str, Any]) -> str:
+        """Tạo mô tả từng bước vẽ dựa trên kết quả của engine."""
+        analysis = semantic_json.get("analysis", "")
+        if not analysis:
+            analysis = f"Giải bài toán về {semantic_json.get('type', 'hình học')}."
+
+        steps = ["\n\n**Các bước dựng hình:**"]
+        drawing_phases = engine_result.get("drawing_phases", [])
+        
+        for phase in drawing_phases:
+            label = phase.get("label", f"Giai đoạn {phase['phase']}")
+            points = ", ".join(phase.get("points", []))
+            segments = ", ".join([f"{s[0]}{s[1]}" for s in phase.get("segments", [])])
+            
+            step_text = f"- **{label}**:"
+            if points:
+                step_text += f" Xác định các điểm {points}."
+            if segments:
+                step_text += f" Vẽ các đoạn thẳng {segments}."
+            steps.append(step_text)
+
+        circles = engine_result.get("circles", [])
+        for c in circles:
+            steps.append(f"- **Đường tròn**: Vẽ đường tròn tâm {c['center']} bán kính {c['radius']}.")
+
+        return analysis + "\n".join(steps)
+
+    async def run(
+        self,
+        text: str,
+        image_url: str = None,
+        job_id: str = None,
+        session_id: str = None,
+        status_callback=None,
+        history: list = None,
+    ) -> Dict[str, Any]:
+        """
+        Run the full pipeline. Optional history allows context-aware solving.
+        """
+        _step_io(
+            "orchestrate_start",
+            input_val={
+                "job_id": job_id,
+                "text_len": len(text or ""),
+                "image_url": image_url,
+                "history_len": len(history or []),
+            },
+            output_val=None,
+        )
+
+        if status_callback:
+            await status_callback("processing")
+
+        # 1. Extract context from history (if any)
+        previous_context = None
+        if history:
+            # Look for the last assistant message with geometry data
+            for msg in reversed(history):
+                if msg.get("role") == "assistant" and msg.get("metadata", {}).get("geometry_dsl"):
+                    previous_context = {
+                        "geometry_dsl": msg["metadata"]["geometry_dsl"],
+                        "coordinates": msg["metadata"].get("coordinates", {}),
+                        "analysis": msg.get("content", ""),
+                    }
+                    break
+        
+        if previous_context:
+            _step_io("context_found", input_val=None, output_val={"dsl_len": len(previous_context["geometry_dsl"])})
+
+        # 2. Gather input text (OCR or direct)
+        input_text = text
+        if image_url:
+            input_text = await ocr_from_image_url(image_url, self.ocr_agent)
+            _step_io("step1_ocr", input_val=image_url, output_val=input_text)
+        else:
+            _step_io("step1_ocr", input_val="(no image)", output_val=text)
+
+        feedback = None
+        MAX_RETRIES = 2
+
+        for attempt in range(MAX_RETRIES + 1):
+            _step_io(
+                "attempt",
+                input_val=f"{attempt + 1}/{MAX_RETRIES + 1}",
+                output_val=None,
+            )
+            if status_callback:
+                await status_callback("solving")
+
+            # Parser with context
+            _step_io("step2_parse", input_val=f"{input_text[:50]}...", output_val=None)
+            semantic_json = await self.parser_agent.process(input_text, feedback=feedback, context=previous_context)
+            semantic_json["input_text"] = input_text
+            _step_io("step2_parse", input_val=None, output_val=semantic_json)
+
+            # Knowledge augmentation
+            _step_io("step3_knowledge", input_val=semantic_json, output_val=None)
+            semantic_json = self.knowledge_agent.augment_semantic_data(semantic_json)
+            _step_io("step3_knowledge", input_val=None, output_val=semantic_json)
+
+            # Geometry DSL with context (passing previous DSL to guide generation)
+            _step_io("step4_geometry_dsl", input_val=semantic_json, output_val=None)
+            dsl_code = await self.geometry_agent.generate_dsl(
+                semantic_json, 
+                previous_dsl=previous_context["geometry_dsl"] if previous_context else None
+            )
+            _step_io("step4_geometry_dsl", input_val=None, output_val=dsl_code)
+
+            _step_io("step5_dsl_parse", input_val=dsl_code, output_val=None)
+            points, constraints, is_3d = self.dsl_parser.parse(dsl_code)
+            _step_io(
+                "step5_dsl_parse",
+                input_val=None,
+                output_val={
+                    "points": len(points),
+                    "constraints": len(constraints),
+                    "is_3d": is_3d,
+                },
+            )
+
+            _step_io("step6_solve", input_val=f"{len(points)} pts / {len(constraints)} cons (is_3d={is_3d})", output_val=None)
+            import anyio
+            engine_result = await anyio.to_thread.run_sync(self.solver_engine.solve, points, constraints, is_3d)
+
+            if engine_result:
+                coordinates = engine_result.get("coordinates")
+                _step_io("step6_solve", input_val=None, output_val=coordinates)
+                break
+
+            feedback = "Geometry solver failed to find a valid solution for the given constraints. Parallelism or lengths might be inconsistent."
+            _step_io(
+                "step6_solve",
+                input_val=f"attempt {attempt + 1}",
+                output_val=feedback,
+            )
+            if attempt == MAX_RETRIES:
+                _step_io(
+                    "orchestrate_abort",
+                    input_val=None,
+                    output_val="solver_exhausted_retries",
+                )
+                return {
+                    "error": "Solver failed after multiple attempts.",
+                    "last_dsl": dsl_code,
+                }
+
+        _step_io("orchestrate_done", input_val=job_id, output_val="success")
+
+        # 8. Solution calculation (New in v5.1)
+        solution = None
+        if engine_result:
+            _step_io("step8_solve_math", input_val=semantic_json.get("target_question"), output_val=None)
+            solution = await self.solver_agent.solve(semantic_json, engine_result)
+            _step_io("step8_solve_math", input_val=None, output_val=solution.get("answer"))
+
+        final_analysis = self._generate_step_description(semantic_json, engine_result)
+
+        status = "success"
+        return {
+            "status": status,
+            "job_id": job_id,
+            "geometry_dsl": dsl_code,
+            "coordinates": coordinates,
+            "polygon_order": engine_result.get("polygon_order", []),
+            "circles": engine_result.get("circles", []),
+            "lines": engine_result.get("lines", []),
+            "rays": engine_result.get("rays", []),
+            "drawing_phases": engine_result.get("drawing_phases", []),
+            "semantic": semantic_json,
+            "semantic_analysis": final_analysis,
+            "solution": solution,
+            "is_3d": is_3d,
+        }

agents/parser_agent.py

@@ -0,0 +1,106 @@
+import os
+import json
+import logging
+from openai import AsyncOpenAI
+from typing import Dict, Any
+from dotenv import load_dotenv
+
+load_dotenv()
+logger = logging.getLogger(__name__)
+
+from app.url_utils import openai_compatible_api_key, sanitize_env
+
+
+from app.llm_client import get_llm_client
+
+
+class ParserAgent:
+    def __init__(self):
+        self.llm = get_llm_client()
+
+    async def process(self, text: str, feedback: str = None, context: Dict[str, Any] = None) -> Dict[str, Any]:
+        logger.info(f"==[ParserAgent] Processing input (len={len(text)})==")
+        if feedback:
+            logger.warning(f"[ParserAgent] Feedback from previous attempt: {feedback}")
+        if context:
+            logger.info(f"[ParserAgent] Using previous context (dsl_len={len(context.get('geometry_dsl', ''))})")
+
+        system_prompt = """
+        You are a Geometry Parser Agent. Extract geometric entities and constraints from Vietnamese/LaTeX math problem text.
+        
+        === CONTEXT AWARENESS ===
+        If previous context is provided, it means this is a follow-up request.
+        - Combine old entities with new ones.
+        - Update 'analysis' to reflect the entire problem state.
+        
+        Output ONLY a JSON object with this EXACT structure (no extra keys, no markdown):
+        {
+            "entities": ["Point A", "Point B", ...],
+            "type": "pyramid|prism|sphere|rectangle|triangle|circle|parallelogram|trapezoid|square|rhombus|general",
+            "values": {"AB": 5, "SO": 15, "radius": 3},
+            "target_question": "Câu hỏi cụ thể cần giải (ví dụ: 'Tính diện tích tam giác ABC'). NẾU KHÔNG CÓ CÂU HỎI THÌ ĐỂ null.",
+            "analysis": "Tóm tắt ngắn gọn toàn bộ bài toán sau khi đã cập nhật các yêu cầu mới bằng tiếng Việt."
+        }
+        Rules:
+        - "analysis" MUST be a meaningful and UP-TO-DATE summary of the problem in Vietnamese. 
+        - "target_question" must be concise.
+        - Include midpoints, auxiliary points in "entities" if mentioned.
+        - If feedback is provided, correct your previous output accordingly.
+        """
+
+        user_content = f"Text: {text}"
+        if context:
+            user_content = f"PREVIOUS ANALYSIS: {context.get('analysis')}\nNEW REQUEST: {text}"
+        
+        if feedback:
+            user_content += f"\nFeedback from previous attempt: {feedback}. Please correct the constraints."
+
+        logger.debug("[ParserAgent] Calling LLM (Multi-Layer)...")
+        raw = await self.llm.chat_completions_create(
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_content}
+            ],
+            response_format={"type": "json_object"}
+        )
+        
+        # Pre-process raw string: extract the JSON block if present
+        import re
+        clean_raw = raw.strip()
+        # Handle potential markdown code blocks
+        if clean_raw.startswith("```"):
+            import re
+            match = re.search(r"```(?:json)?\s*(.*?)\s*```", clean_raw, re.DOTALL)
+            if match:
+                clean_raw = match.group(1).strip()
+            
+        try:
+            result = json.loads(clean_raw)
+        except json.JSONDecodeError as e:
+            logger.error(f"[ParserAgent] JSON Parse Error: {e}. Attempting regex fallback...")
+            import re
+            json_match = re.search(r'(\{.*\})', clean_raw, re.DOTALL)
+            if json_match:
+                try:
+                    # Handle single quotes if present (common LLM failure)
+                    json_str = json_match.group(1)
+                    if "'" in json_str and '"' not in json_str:
+                         json_str = json_str.replace("'", '"')
+                    result = json.loads(json_str)
+                except:
+                    result = None
+            else:
+                result = None
+
+            if not result:
+                # Fallback for critical failure
+                result = {
+                    "entities": [],
+                    "type": "general",
+                    "values": {},
+                    "target_question": None,
+                    "analysis": text
+                }
+        logger.info(f"[ParserAgent] LLM response received.")
+        logger.debug(f"[ParserAgent] Parsed JSON: {json.dumps(result, ensure_ascii=False, indent=2)}")
+        return result

agents/renderer_agent.py

@@ -0,0 +1,265 @@
+import os
+import subprocess
+import glob
+import string
+import logging
+from typing import Dict, Any, List
+
+logger = logging.getLogger(__name__)
+
+
+class RendererAgent:
+    """
+    Renderer Agent — generates Manim scripts from geometry data.
+
+    Drawing happens in phases:
+      Phase 1: Main polygon (base shape with correct vertex order)
+      Phase 2: Auxiliary points and segments (midpoints, derived segments)
+      Phase 3: Labels for all points
+    """
+
+    def generate_manim_script(self, data: Dict[str, Any]) -> str:
+        coords: Dict[str, List[float]] = data.get("coordinates", {})
+        polygon_order: List[str] = data.get("polygon_order", [])
+        circles_meta: List[Dict] = data.get("circles", [])
+        lines_meta: List[List[str]] = data.get("lines", [])
+        rays_meta: List[List[str]] = data.get("rays", [])
+        drawing_phases: List[Dict] = data.get("drawing_phases", [])
+        semantic: Dict[str, Any] = data.get("semantic", {})
+        shape_type = semantic.get("type", "").lower()
+
+        # ── Detect 3D Context ────────────────────────────────────────────────
+        is_3d = False
+        for pos in coords.values():
+            if len(pos) >= 3 and abs(pos[2]) > 0.001:
+                is_3d = True
+                break
+        if shape_type in ["pyramid", "prism", "sphere"]:
+            is_3d = True
+
+        # ── Fallback: infer polygon_order from coords keys (alphabetical uppercase) ──
+        if not polygon_order:
+            base = sorted(
+                [pid for pid in coords if pid in string.ascii_uppercase],
+                key=lambda p: string.ascii_uppercase.index(p)
+            )
+            polygon_order = base
+
+        # Separate base points from derived (multi-char or lowercase)
+        base_ids = [pid for pid in polygon_order if pid in coords]
+        derived_ids = [pid for pid in coords if pid not in polygon_order]
+
+        scene_base = "ThreeDScene" if is_3d else "MovingCameraScene"
+        lines = [
+            "from manim import *",
+            "",
+            f"class GeometryScene({scene_base}):",
+            "    def construct(self):",
+        ]
+
+        if is_3d:
+            lines.append("        # 3D Setup")
+            lines.append("        self.set_camera_orientation(phi=75*DEGREES, theta=-45*DEGREES)")
+            lines.append("        axes = ThreeDAxes(axis_config={'stroke_width': 1})")
+            lines.append("        axes.set_opacity(0.3)")
+            lines.append("        self.add(axes)")
+            lines.append("        self.begin_ambient_camera_rotation(rate=0.1)")
+            lines.append("")
+
+        # ── Declare all dots and labels ───────────────────────────────────────
+        for pid, pos in coords.items():
+            x, y, z = 0, 0, 0
+            if len(pos) >= 1: x = round(pos[0], 4)
+            if len(pos) >= 2: y = round(pos[1], 4)
+            if len(pos) >= 3: z = round(pos[2], 4)
+            
+            dot_class = "Dot3D" if is_3d else "Dot"
+            lines.append(f"        p_{pid} = {dot_class}(point=[{x}, {y}, {z}], color=WHITE, radius=0.08)")
+            
+            if is_3d:
+                lines.append(
+                    f"        l_{pid} = Text('{pid}', font_size=20, color=WHITE)"
+                    f".move_to(p_{pid}.get_center() + [0.2, 0.2, 0.2])"
+                )
+                # Ensure labels follow camera in 3D (fixed orientation)
+                lines.append(f"        self.add_fixed_orientation_mobjects(l_{pid})")
+            else:
+                lines.append(
+                    f"        l_{pid} = Text('{pid}', font_size=22, color=WHITE)"
+                    f".next_to(p_{pid}, UR, buff=0.15)"
+                )
+
+        # ── 3D Shape Special: Pyramid/Prism Faces ────────────────────────────
+        if is_3d and shape_type == "pyramid" and len(base_ids) >= 3:
+            # Find apex (usually 'S')
+            apex_id = "S" if "S" in coords else derived_ids[0] if derived_ids else None
+            if apex_id:
+                # Draw base face
+                base_pts = ", ".join([f"p_{pid}.get_center()" for pid in base_ids])
+                lines.append(f"        base_face = Polygon({base_pts}, color=BLUE, fill_opacity=0.1)")
+                lines.append("        self.play(Create(base_face), run_time=1.0)")
+                
+                # Draw side faces
+                for i in range(len(base_ids)):
+                    p1 = base_ids[i]
+                    p2 = base_ids[(i+1)%len(base_ids)]
+                    face_pts = f"p_{apex_id}.get_center(), p_{p1}.get_center(), p_{p2}.get_center()"
+                    lines.append(f"        side_{i} = Polygon({face_pts}, color=BLUE, stroke_width=1, fill_opacity=0.05)")
+                    lines.append(f"        self.play(Create(side_{i}), run_time=0.5)")
+
+        # ── Circles ──────────────────────────────────────────────────────────
+        for i, c in enumerate(circles_meta):
+            center = c["center"]
+            r = c["radius"]
+            if center in coords:
+                cx, cy, cz = 0, 0, 0
+                pos = coords[center]
+                if len(pos) >= 1: cx = round(pos[0], 4)
+                if len(pos) >= 2: cy = round(pos[1], 4)
+                if len(pos) >= 3: cz = round(pos[2], 4)
+                lines.append(
+                    f"        circle_{i} = Circle(radius={r}, color=BLUE)"
+                    f".move_to([{cx}, {cy}, {cz}])"
+                )
+
+        # ── Infinite Lines & Rays ────────────────────────────────────────────
+        # (Standard Line works for 3D coordinates in Manim)
+        for i, (p1, p2) in enumerate(lines_meta):
+            if p1 in coords and p2 in coords:
+                lines.append(
+                    f"        line_ext_{i} = Line(p_{p1}.get_center(), p_{p2}.get_center(), color=GRAY_D, stroke_width=2)"
+                    f".scale(20)"
+                )
+
+        for i, (p1, p2) in enumerate(rays_meta):
+            if p1 in coords and p2 in coords:
+                lines.append(
+                    f"        ray_{i} = Line(p_{p1}.get_center(), p_{p1}.get_center() + 15 * (p_{p2}.get_center() - p_{p1}.get_center()),"
+                    f" color=GRAY_C, stroke_width=2)"
+                )
+
+        # ── Camera auto-fit group (Only for 2D) ──────────────────────────────
+        if not is_3d:
+            all_dot_names = [f"p_{pid}" for pid in coords]
+            all_names_str = ", ".join(all_dot_names)
+            lines.append(f"        _all = VGroup({all_names_str})")
+            lines.append("        self.camera.frame.set_width(max(_all.width * 2.0, 8))")
+            lines.append("        self.camera.frame.move_to(_all)")
+        lines.append("")
+
+        # ── Phase 1: Base polygon ─────────────────────────────────────────────
+        if len(base_ids) >= 3:
+            pts_str = ", ".join([f"p_{pid}.get_center()" for pid in base_ids])
+            lines.append(f"        poly = Polygon({pts_str}, color=BLUE, fill_color=BLUE, fill_opacity=0.15)")
+            lines.append("        self.play(Create(poly), run_time=1.5)")
+        elif len(base_ids) == 2:
+            p1, p2 = base_ids
+            lines.append(f"        base_line = Line(p_{p1}.get_center(), p_{p2}.get_center(), color=BLUE)")
+            lines.append("        self.play(Create(base_line), run_time=1.0)")
+
+        # Draw base points
+        if base_ids:
+            base_dots_str = ", ".join([f"p_{pid}" for pid in base_ids])
+            lines.append(f"        self.play(FadeIn(VGroup({base_dots_str})), run_time=0.5)")
+        lines.append("        self.wait(0.5)")
+
+        # ── Phase 2: Auxiliary points and segments ────────────────────────────
+        if derived_ids:
+            derived_dots_str = ", ".join([f"p_{pid}" for pid in derived_ids])
+            lines.append(f"        self.play(FadeIn(VGroup({derived_dots_str})), run_time=0.8)")
+
+        # Segments from drawing_phases
+        segment_lines = []
+        for phase in drawing_phases:
+            if phase.get("phase") == 2:
+                for seg in phase.get("segments", []):
+                    if len(seg) == 2 and seg[0] in coords and seg[1] in coords:
+                        p1, p2 = seg[0], seg[1]
+                        seg_var = f"seg_{p1}_{p2}"
+                        lines.append(
+                            f"        {seg_var} = Line(p_{p1}.get_center(), p_{p2}.get_center(),"
+                            f" color=YELLOW)"
+                        )
+                        segment_lines.append(seg_var)
+
+        if segment_lines:
+            segs_str = ", ".join([f"Create({sv})" for sv in segment_lines])
+            lines.append(f"        self.play({segs_str}, run_time=1.2)")
+
+        if derived_ids or segment_lines:
+            lines.append("        self.wait(0.5)")
+
+        # ── Phase 3: All labels ───────────────────────────────────────────────
+        all_labels_str = ", ".join([f"l_{pid}" for pid in coords])
+        lines.append(f"        self.play(FadeIn(VGroup({all_labels_str})), run_time=0.8)")
+
+        # ── Circles phase ─────────────────────────────────────────────────────
+        for i in range(len(circles_meta)):
+            lines.append(f"        self.play(Create(circle_{i}), run_time=1.5)")
+
+        # ── Lines & Rays phase ────────────────────────────────────────────────
+        if lines_meta or rays_meta:
+            lr_anims = []
+            for i in range(len(lines_meta)): lr_anims.append(f"Create(line_ext_{i})")
+            for i in range(len(rays_meta)): lr_anims.append(f"Create(ray_{i})")
+            lines.append(f"        self.play({', '.join(lr_anims)}, run_time=1.5)")
+
+        lines.append("        self.wait(2)")
+
+        return "\n".join(lines)
+
+    def run_manim(self, script_content: str, job_id: str) -> str:
+        import subprocess
+        import os
+        import glob
+
+        script_file = f"{job_id}.py"
+        with open(script_file, "w") as f:
+            f.write(script_content)
+
+        try:
+            if os.getenv("MOCK_VIDEO") == "true":
+                logger.info(f"MOCK_VIDEO is true. Skipping Manim for job {job_id}")
+                # Create a dummy file if needed, or just return a path that exists
+                dummy_path = f"videos/{job_id}.mp4"
+                os.makedirs("videos", exist_ok=True)
+                with open(dummy_path, "wb") as f:
+                    f.write(b"dummy video content")
+                return dummy_path
+
+            # Determine manim executable path
+            manim_exe = "manim"
+            venv_manim = os.path.join(os.getcwd(), "venv", "bin", "manim")
+            if os.path.exists(venv_manim):
+                manim_exe = venv_manim
+            
+            # Prepare environment with homebrew paths
+            custom_env = os.environ.copy()
+            brew_path = "/opt/homebrew/bin:/usr/local/bin"
+            custom_env["PATH"] = f"{brew_path}:{custom_env.get('PATH', '')}"
+
+            logger.info(f"Running {manim_exe} for job {job_id}...")
+            result = subprocess.run(
+                [manim_exe, "-ql", "--media_dir", ".", "-o", f"{job_id}.mp4", script_file, "GeometryScene"],
+                capture_output=True,
+                text=True,
+                env=custom_env
+            )
+            logger.info(f"Manim STDOUT: {result.stdout}")
+            if result.returncode != 0:
+                logger.error(f"Manim STDERR: {result.stderr}")
+
+            for pattern in [f"**/videos/**/{job_id}.mp4", f"**/{job_id}*.mp4"]:
+                found = glob.glob(pattern, recursive=True)
+                if found:
+                    logger.info(f"Manim Success: Found {found[0]}")
+                    return found[0]
+
+            logger.error(f"Manim file not found for job {job_id}. Return code: {result.returncode}")
+            return ""
+        except Exception as e:
+            logger.exception(f"Manim Execution Error: {e}")
+            return ""
+        finally:
+            if os.path.exists(script_file):
+                os.remove(script_file)

agents/solver_agent.py

@@ -0,0 +1,107 @@
+import json
+import logging
+import sympy as sp
+from typing import Dict, Any, List
+from app.llm_client import get_llm_client
+
+logger = logging.getLogger(__name__)
+
+class SolverAgent:
+    def __init__(self):
+        self.llm = get_llm_client()
+
+    async def solve(self, semantic_data: Dict[str, Any], engine_result: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Solves the geometric problem based on coordinates and the target question.
+        Returns a 'solution' dictionary with answer, steps, and symbolic_expression.
+        """
+        target_question = semantic_data.get("target_question")
+        if not target_question:
+            # If no question, just return an empty solution structure
+            return {
+                "answer": None,
+                "steps": [],
+                "symbolic_expression": None
+            }
+
+        logger.info(f"==[SolverAgent] Solving for: '{target_question}'==")
+
+        input_text = semantic_data.get("input_text", "")
+        coordinates = engine_result.get("coordinates", {})
+        
+        # We provide the coordinates and semantic context to the LLM to help it reason.
+        # The LLM is tasked with generating the solution structure directly.
+        
+        system_prompt = """
+        You are a Geometry Solver Agent. Your goal is to provide a step-by-step solution for a specific geometric question.
+        
+        === DATA PROVIDED ===
+        1. Target Question: The specific question to answer.
+        2. Geometry Data: Entities and values extracted from the problem.
+        3. Coordinates: Calculated coordinates for all points.
+        
+        === REQUIREMENTS ===
+        - Provide the solution in the SAME LANGUAGE as the user's input.
+        - Use SymPy concepts if appropriate.
+        - Steps should be clear, concise, and logical.
+        - The final answer should be numerically or symbolically accurate based on the coordinates and geometric properties.
+        - For geometric proofs (e.g., "Is AB perpendicular to AC?"), explain the reasoning based on the data.
+        
+        Output ONLY a JSON object with this structure:
+        {
+            "answer": "Chuỗi văn bản kết quả cuối cùng (kèm đơn vị nếu có)",
+            "steps": [
+                "Bước 1: ...",
+                "Bước 2: ...",
+                ...
+            ],
+            "symbolic_expression": "Biểu thức toán học rút gọn (LaTeX format optional)"
+        }
+        """
+
+        user_content = f"""
+        INPUT_TEXT: {input_text}
+        TARGET_QUESTION: {target_question}
+        SEMANTIC_DATA: {json.dumps(semantic_data, ensure_ascii=False)}
+        COORDINATES: {json.dumps(coordinates)}
+        """
+
+        logger.debug("[SolverAgent] Requesting solution from LLM...")
+        try:
+            raw = await self.llm.chat_completions_create(
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_content}
+                ],
+                response_format={"type": "json_object"}
+            )
+            
+            clean_raw = raw.strip()
+            # Handle potential markdown code blocks if the response_format wasn't strictly honored
+            if clean_raw.startswith("```"):
+                import re
+                match = re.search(r"```(?:json)?\s*(.*?)\s*```", clean_raw, re.DOTALL)
+                if match:
+                    clean_raw = match.group(1).strip()
+            
+            try:
+                solution = json.loads(clean_raw)
+            except json.JSONDecodeError:
+                # Last resort: try to find anything between { and }
+                import re
+                json_match = re.search(r'(\{.*\})', clean_raw, re.DOTALL)
+                if json_match:
+                    solution = json.loads(json_match.group(1))
+                else:
+                    raise
+            
+            logger.info("[SolverAgent] Solution generated successfully.")
+            return solution
+        except Exception as e:
+            logger.error(f"[SolverAgent] Error generating solution: {e}")
+            logger.debug(f"[SolverAgent] Raw LLM output was: \n{raw if 'raw' in locals() else 'N/A'}")
+            return {
+                "answer": "Không thể tính toán lời giải tại thời điểm này.",
+                "steps": ["Đã xảy ra lỗi trong quá trình xử lý lời giải."],
+                "symbolic_expression": None
+            }

agents/torch_ultralytics_compat.py

@@ -0,0 +1,33 @@
+"""PyTorch 2.6+ defaults weights_only=True; Ultralytics YOLO .pt checkpoints unpickle full nn graphs (trusted official weights)."""
+
+from __future__ import annotations
+
+import functools
+
+_torch_load_patched = False
+
+
+def allow_ultralytics_weights() -> None:
+    """
+    Official yolov8n.pt is a trusted checkpoint. PyTorch 2.6+ safe unpickling would require
+    allowlisting many torch.nn globals; loading with weights_only=False matches Ultralytics
+    upstream behavior for local .pt files.
+    """
+    global _torch_load_patched
+    if _torch_load_patched:
+        return
+    try:
+        import torch
+
+        _orig = torch.load
+
+        @functools.wraps(_orig)
+        def _load(*args, **kwargs):
+            if "weights_only" not in kwargs:
+                kwargs["weights_only"] = False
+            return _orig(*args, **kwargs)
+
+        torch.load = _load
+        _torch_load_patched = True
+    except Exception:
+        pass

app/chat_image_upload.py

@@ -0,0 +1,206 @@
+"""Validate and upload chat/solve attachment images to Supabase Storage (image bucket)."""
+
+from __future__ import annotations
+
+import logging
+import os
+import uuid
+from typing import Any, Dict, Tuple
+
+from fastapi import HTTPException
+
+logger = logging.getLogger(__name__)
+
+
+def _get_next_image_version(session_id: str) -> int:
+    """Same logic as worker.asset_manager.get_next_version for asset_type image."""
+    from app.supabase_client import get_supabase
+
+    supabase = get_supabase()
+    try:
+        res = (
+            supabase.table("session_assets")
+            .select("version")
+            .eq("session_id", session_id)
+            .eq("asset_type", "image")
+            .order("version", desc=True)
+            .limit(1)
+            .execute()
+        )
+        if res.data:
+            return res.data[0]["version"] + 1
+        return 1
+    except Exception as e:
+        logger.error("Error fetching image version: %s", e)
+        return 1
+
+_MAX_BYTES_DEFAULT = 10 * 1024 * 1024
+
+_EXT_TO_MIME: dict[str, str] = {
+    ".png": "image/png",
+    ".jpg": "image/jpeg",
+    ".jpeg": "image/jpeg",
+    ".webp": "image/webp",
+    ".gif": "image/gif",
+    ".bmp": "image/bmp",
+}
+
+
+def _max_bytes() -> int:
+    raw = os.getenv("CHAT_IMAGE_MAX_BYTES")
+    if raw and raw.isdigit():
+        return min(int(raw), 50 * 1024 * 1024)
+    return _MAX_BYTES_DEFAULT
+
+
+def _magic_ok(ext: str, body: bytes) -> bool:
+    if len(body) < 12:
+        return False
+    if ext == ".png":
+        return body.startswith(b"\x89PNG\r\n\x1a\n")
+    if ext in (".jpg", ".jpeg"):
+        return body.startswith(b"\xff\xd8\xff")
+    if ext == ".webp":
+        return body.startswith(b"RIFF") and body[8:12] == b"WEBP"
+    if ext == ".gif":
+        return body.startswith(b"GIF87a") or body.startswith(b"GIF89a")
+    if ext == ".bmp":
+        return body.startswith(b"BM")
+    return False
+
+
+def validate_chat_image_bytes(
+    filename: str | None,
+    body: bytes,
+    declared_content_type: str | None,
+) -> Tuple[str, str]:
+    """
+    Validate size, extension, and magic bytes.
+    Returns (extension_with_dot, content_type).
+    """
+    max_b = _max_bytes()
+    if not body:
+        raise HTTPException(status_code=400, detail="Empty file.")
+    if len(body) > max_b:
+        raise HTTPException(
+            status_code=413,
+            detail=f"Image too large (max {max_b // (1024 * 1024)} MB).",
+        )
+
+    ext = os.path.splitext(filename or "")[1].lower()
+    if not ext:
+        ext = ".png"
+    if ext not in _EXT_TO_MIME:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Unsupported image type: {ext}. Allowed: {', '.join(sorted(_EXT_TO_MIME))}",
+        )
+
+    if not _magic_ok(ext, body):
+        raise HTTPException(
+            status_code=400,
+            detail="File content does not match declared image type.",
+        )
+
+    mime = _EXT_TO_MIME[ext]
+    if declared_content_type:
+        decl = declared_content_type.split(";")[0].strip().lower()
+        if decl and decl not in ("application/octet-stream", mime) and decl != mime:
+            logger.warning(
+                "Content-Type mismatch (declared=%s, inferred=%s); using inferred.",
+                declared_content_type,
+                mime,
+            )
+    return ext, mime
+
+
+def upload_session_chat_image(
+    session_id: str,
+    job_id: str,
+    file_bytes: bytes,
+    ext_with_dot: str,
+    content_type: str,
+) -> Dict[str, Any]:
+    """
+    Upload to SUPABASE_IMAGE_BUCKET (default: image), insert session_assets row.
+    Returns dict with public_url, storage_path, version, session_asset_id (if returned).
+    """
+    from app.supabase_client import get_supabase
+
+    supabase = get_supabase()
+    bucket_name = os.getenv("SUPABASE_IMAGE_BUCKET", "image")
+    raw_ext = ext_with_dot.lstrip(".").lower()
+    version = _get_next_image_version(session_id)
+    file_name = f"image_v{version}_{job_id}.{raw_ext}"
+    storage_path = f"sessions/{session_id}/{file_name}"
+
+    supabase.storage.from_(bucket_name).upload(
+        path=storage_path,
+        file=file_bytes,
+        file_options={"content-type": content_type},
+    )
+    public_url = supabase.storage.from_(bucket_name).get_public_url(storage_path)
+    if isinstance(public_url, dict):
+        public_url = public_url.get("publicUrl") or public_url.get("public_url") or str(public_url)
+
+    row = {
+        "session_id": session_id,
+        "job_id": job_id,
+        "asset_type": "image",
+        "storage_path": storage_path,
+        "public_url": public_url,
+        "version": version,
+    }
+    ins = supabase.table("session_assets").insert(row).select("id").execute()
+    asset_id = None
+    if ins.data and len(ins.data) > 0:
+        asset_id = ins.data[0].get("id")
+
+    log_data = {
+        "public_url": public_url,
+        "storage_path": storage_path,
+        "version": version,
+        "session_asset_id": str(asset_id) if asset_id else None,
+    }
+    logger.info("Uploaded chat image: %s", log_data)
+    return {
+        "public_url": public_url,
+        "storage_path": storage_path,
+        "version": version,
+        "session_asset_id": str(asset_id) if asset_id else None,
+    }
+
+
+def upload_ephemeral_ocr_blob(
+    file_bytes: bytes,
+    ext_with_dot: str,
+    content_type: str,
+) -> Tuple[str, str]:
+    """
+    Upload bytes to image bucket under _ocr_temp/ for worker-only OCR (no session_assets row).
+    Returns (storage_path, public_url). Caller must delete_storage_object when done.
+    """
+    from app.supabase_client import get_supabase
+
+    bucket_name = os.getenv("SUPABASE_IMAGE_BUCKET", "image")
+    raw_ext = ext_with_dot.lstrip(".").lower() or "png"
+    name = f"_ocr_temp/{uuid.uuid4().hex}.{raw_ext}"
+    supabase = get_supabase()
+    supabase.storage.from_(bucket_name).upload(
+        path=name,
+        file=file_bytes,
+        file_options={"content-type": content_type},
+    )
+    public_url = supabase.storage.from_(bucket_name).get_public_url(name)
+    if isinstance(public_url, dict):
+        public_url = public_url.get("publicUrl") or public_url.get("public_url") or str(public_url)
+    return name, public_url
+
+
+def delete_storage_object(bucket_name: str, storage_path: str) -> None:
+    try:
+        from app.supabase_client import get_supabase
+
+        get_supabase().storage.from_(bucket_name).remove([storage_path])
+    except Exception as e:
+        logger.warning("delete_storage_object failed path=%s: %s", storage_path, e)

app/dependencies.py

@@ -0,0 +1,69 @@
+from fastapi import HTTPException, Header
+
+from app.supabase_client import get_supabase, get_supabase_for_user_jwt
+
+
+async def get_current_user_id(authorization: str | None = Header(None)):
+    """
+    Authenticate user using Supabase JWT.
+    Expected Header: Authorization: Bearer <token>
+    """
+    import os
+
+    if not authorization:
+        raise HTTPException(
+            status_code=401,
+            detail="Authorization header missing or invalid. Use 'Bearer <token>'",
+        )
+
+    if os.getenv("ALLOW_TEST_BYPASS") == "true" and authorization.startswith("Test "):
+        return authorization.split(" ")[1]
+
+    if not authorization.startswith("Bearer "):
+        raise HTTPException(
+            status_code=401,
+            detail="Authorization header missing or invalid. Use 'Bearer <token>'",
+        )
+
+    token = authorization.split(" ")[1]
+    supabase = get_supabase()
+
+    try:
+        user_response = supabase.auth.get_user(token)
+        if not user_response or not user_response.user:
+            raise HTTPException(status_code=401, detail="Invalid session or token.")
+
+        return user_response.user.id
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=401, detail=f"Authentication failed: {str(e)}")
+
+
+async def get_authenticated_supabase(authorization: str = Header(...)):
+    """
+    Supabase client that carries the user's JWT (anon key + Authorization header).
+    Use for routes that should respect Row Level Security; pair with app logic as needed.
+    """
+    if not authorization or not authorization.startswith("Bearer "):
+        raise HTTPException(
+            status_code=401,
+            detail="Authorization header missing or invalid. Use 'Bearer <token>'",
+        )
+
+    token = authorization.split(" ")[1]
+    supabase = get_supabase()
+
+    try:
+        user_response = supabase.auth.get_user(token)
+        if not user_response or not user_response.user:
+            raise HTTPException(status_code=401, detail="Invalid session or token.")
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=401, detail=f"Authentication failed: {str(e)}")
+
+    try:
+        return get_supabase_for_user_jwt(token)
+    except RuntimeError as e:
+        raise HTTPException(status_code=503, detail=str(e))

app/errors.py

@@ -0,0 +1,59 @@
+"""Map exceptions to short, user-visible messages (avoid leaking HTML bodies from 404 proxies)."""
+
+from __future__ import annotations
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def _looks_like_html(text: str) -> bool:
+    t = text.lstrip()[:500].lower()
+    return t.startswith("<!doctype") or t.startswith("<html") or "<html" in t[:200]
+
+
+def format_error_for_user(exc: BaseException) -> str:
+    """
+    Produce a safe message for chat/UI. Full detail stays in server logs via logger.exception.
+    """
+    # httpx: wrong URL often returns 404 HTML; don't show body
+    try:
+        import httpx
+
+        if isinstance(exc, httpx.HTTPStatusError):
+            req = exc.request
+            code = exc.response.status_code
+            url_hint = ""
+            try:
+                url_hint = str(req.url.host) if req and req.url else ""
+            except Exception:
+                pass
+            logger.warning(
+                "HTTPStatusError %s for %s (response not shown to user)",
+                code,
+                url_hint or "?",
+            )
+            return (
+                "Kiểm tra URL API, khóa bí mật và biến môi trường (OpenRouter/Supabase/Redis)."
+            )
+
+        if isinstance(exc, httpx.RequestError):
+            return "Không kết nối được tới dịch vụ ngoài (mạng hoặc URL sai)."
+    except ImportError:
+        pass
+
+    raw = str(exc).strip()
+    if not raw:
+        return "Đã xảy ra lỗi không xác định."
+
+    if _looks_like_html(raw):
+        logger.warning("Suppressed HTML error body from user-facing message")
+        return (
+            "Dịch vụ trả về trang lỗi (thường là URL API sai hoặc endpoint không tồn tại — HTTP 404). "
+            "Kiểm tra OPENROUTER_MODEL và khóa API trên server."
+        )
+
+    if len(raw) > 800:
+        return raw[:800] + "…"
+
+    return raw

app/llm_client.py

@@ -0,0 +1,100 @@
+import os
+import json
+import asyncio
+import logging
+from openai import AsyncOpenAI
+from typing import List, Dict, Any, Optional
+from app.url_utils import openai_compatible_api_key, sanitize_env
+
+logger = logging.getLogger(__name__)
+
+class MultiLayerLLMClient:
+    def __init__(self):
+        # 1. Models sequence loading
+        self.models = []
+        for i in range(1, 4):
+            model = os.getenv(f"OPENROUTER_MODEL_{i}")
+            if model:
+                self.models.append(model)
+        
+        # Fallback to legacy OPENROUTER_MODEL if no numbered models found
+        if not self.models:
+            legacy_model = os.getenv("OPENROUTER_MODEL", "google/gemini-2.0-flash-001")
+            self.models = [legacy_model]
+
+        # 2. Key selection (No rotation, always use the first available key)
+        api_key = os.getenv("OPENROUTER_API_KEY_1") or os.getenv("OPENROUTER_API_KEY")
+        
+        if not api_key:
+            logger.error("[LLM] No OpenRouter API key found.")
+            self.client = None
+        else:
+            self.client = AsyncOpenAI(
+                api_key=openai_compatible_api_key(api_key),
+                base_url="https://openrouter.ai/api/v1",
+                timeout=60.0,
+                default_headers={
+                    "HTTP-Referer": "https://mathsolver.ai",
+                    "X-Title": "MathSolver Backend",
+                }
+            )
+
+    async def chat_completions_create(
+        self, 
+        messages: List[Dict[str, str]], 
+        response_format: Optional[Dict[str, str]] = None,
+        **kwargs
+    ) -> str:
+        """
+        Implements Model Fallback Sequence: Model 1 -> Model 2 -> Model 3.
+        Always starts from Model 1 for every new call.
+        """
+        if not self.client:
+            raise ValueError("No API client configured. Check your API keys.")
+
+        MAX_ATTEMPTS = len(self.models)
+        RETRY_DELAY = 1.0 # second
+        
+        for attempt_idx in range(MAX_ATTEMPTS):
+            current_model = self.models[attempt_idx]
+            attempt_num = attempt_idx + 1
+            
+            try:
+                logger.info(f"[LLM] Attempt {attempt_num}/{MAX_ATTEMPTS} using Model: {current_model}...")
+                
+                response = await self.client.chat.completions.create(
+                    model=current_model,
+                    messages=messages,
+                    response_format=response_format,
+                    **kwargs
+                )
+                
+                if not response or not getattr(response, "choices", None):
+                     raise ValueError(f"Invalid response structure from model {current_model}")
+
+                content = response.choices[0].message.content
+                if content:
+                    logger.info(f"[LLM] SUCCESS on attempt {attempt_num} ({current_model}).")
+                    return content
+                
+                raise ValueError(f"Empty content from model {current_model}")
+
+            except Exception as e:
+                err_msg = f"{type(e).__name__}: {str(e)}"
+                logger.warning(f"[LLM] FAILED on attempt {attempt_num} ({current_model}): {err_msg}")
+                
+                if attempt_num < MAX_ATTEMPTS:
+                    logger.info(f"[LLM] Retrying next model in {RETRY_DELAY}s...")
+                    await asyncio.sleep(RETRY_DELAY)
+                else:
+                    logger.error(f"[LLM] FINAL FAILURE after {attempt_num} models.")
+                    raise e
+
+# Global instance for easy reuse (singleton-ish)
+_llm_client = None
+
+def get_llm_client() -> MultiLayerLLMClient:
+    global _llm_client
+    if _llm_client is None:
+        _llm_client = MultiLayerLLMClient()
+    return _llm_client

app/logging_setup.py

@@ -0,0 +1,112 @@
+"""Logging theo một biến LOG_LEVEL: debug | info | warning | error."""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import Final
+
+_SETUP_DONE = False
+
+PIPELINE_LOGGER_NAME: Final = "app.pipeline"
+CACHE_LOGGER_NAME: Final = "app.cache"
+STEPS_LOGGER_NAME: Final = "app.steps"
+ACCESS_LOGGER_NAME: Final = "app.access"
+
+
+def _normalize_level() -> str:
+    raw = os.getenv("LOG_LEVEL", "info").strip().lower()
+    if raw in ("debug", "info", "warning", "error"):
+        return raw
+    return "info"
+
+
+def setup_application_logging() -> None:
+    """Idempotent; gọi khi khởi động process (uvicorn, celery, worker_health)."""
+    global _SETUP_DONE
+    if _SETUP_DONE:
+        return
+    _SETUP_DONE = True
+
+    mode = _normalize_level()
+
+    level_map = {
+        "debug": logging.DEBUG,
+        "info": logging.INFO,
+        "warning": logging.WARNING,
+        "error": logging.ERROR,
+    }
+    root_level = level_map[mode]
+
+    fmt_named = "%(asctime)s | %(levelname)-8s | %(name)s | %(message)s"
+    fmt_short = "%(asctime)s | %(levelname)-8s | %(message)s"
+
+    logging.basicConfig(
+        level=root_level,
+        format=fmt_named if mode == "debug" else fmt_short,
+        datefmt="%H:%M:%S",
+        force=True,
+    )
+
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+    logging.getLogger("httpcore").setLevel(logging.WARNING)
+    logging.getLogger("openai").setLevel(logging.WARNING)
+    logging.getLogger("uvicorn.access").setLevel(logging.WARNING)
+    logging.getLogger("uvicorn.error").setLevel(logging.INFO)
+    # HTTP/2 stack (httpx/httpcore) — khi LOG_LEVEL=debug root=DEBUG sẽ tràn log hpack; không cần cho debug app
+    for _name in ("hpack", "h2", "hyperframe", "urllib3"):
+        logging.getLogger(_name).setLevel(logging.WARNING)
+
+    if mode == "debug":
+        logging.getLogger("agents").setLevel(logging.DEBUG)
+        logging.getLogger("solver").setLevel(logging.DEBUG)
+        logging.getLogger("app").setLevel(logging.DEBUG)
+        logging.getLogger(CACHE_LOGGER_NAME).setLevel(logging.DEBUG)
+        logging.getLogger(STEPS_LOGGER_NAME).setLevel(logging.DEBUG)
+        logging.getLogger(PIPELINE_LOGGER_NAME).setLevel(logging.INFO)
+        logging.getLogger(ACCESS_LOGGER_NAME).setLevel(logging.INFO)
+        logging.getLogger("app.main").setLevel(logging.INFO)
+        logging.getLogger("worker").setLevel(logging.INFO)
+    elif mode == "info":
+        # Chỉ HTTP access (app.access) + startup; ẩn chi tiết agents/orchestrator/pipeline SUCCESS
+        logging.getLogger("agents").setLevel(logging.INFO)
+        logging.getLogger("solver").setLevel(logging.WARNING)
+        logging.getLogger("app").setLevel(logging.INFO)
+        logging.getLogger(CACHE_LOGGER_NAME).setLevel(logging.WARNING)
+        logging.getLogger(STEPS_LOGGER_NAME).setLevel(logging.WARNING)
+        logging.getLogger(PIPELINE_LOGGER_NAME).setLevel(logging.WARNING)
+        logging.getLogger(ACCESS_LOGGER_NAME).setLevel(logging.INFO)
+        logging.getLogger("app.main").setLevel(logging.INFO)
+        logging.getLogger("worker").setLevel(logging.WARNING)
+    elif mode == "warning":
+        logging.getLogger("agents").setLevel(logging.WARNING)
+        logging.getLogger("solver").setLevel(logging.WARNING)
+        logging.getLogger("app.routers").setLevel(logging.WARNING)
+        logging.getLogger(CACHE_LOGGER_NAME).setLevel(logging.WARNING)
+        logging.getLogger(STEPS_LOGGER_NAME).setLevel(logging.WARNING)
+        logging.getLogger(PIPELINE_LOGGER_NAME).setLevel(logging.WARNING)
+        logging.getLogger(ACCESS_LOGGER_NAME).setLevel(logging.WARNING)
+        logging.getLogger("app.main").setLevel(logging.WARNING)
+        logging.getLogger("worker").setLevel(logging.WARNING)
+    else:  # error
+        logging.getLogger("agents").setLevel(logging.ERROR)
+        logging.getLogger("solver").setLevel(logging.ERROR)
+        logging.getLogger("app.routers").setLevel(logging.ERROR)
+        logging.getLogger(CACHE_LOGGER_NAME).setLevel(logging.ERROR)
+        logging.getLogger(STEPS_LOGGER_NAME).setLevel(logging.ERROR)
+        logging.getLogger(PIPELINE_LOGGER_NAME).setLevel(logging.ERROR)
+        logging.getLogger(ACCESS_LOGGER_NAME).setLevel(logging.ERROR)
+        logging.getLogger("app.main").setLevel(logging.ERROR)
+        logging.getLogger("worker").setLevel(logging.ERROR)
+
+    logging.getLogger(__name__).debug(
+        "LOG_LEVEL=%s root=%s", mode, logging.getLevelName(root_level)
+    )
+
+
+def get_log_level() -> str:
+    return _normalize_level()
+
+
+def is_debug_level() -> bool:
+    return _normalize_level() == "debug"

app/logutil.py

@@ -0,0 +1,67 @@
+"""log_step (debug), pipeline (debug), access log ở middleware."""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+from typing import Any
+
+from app.logging_setup import PIPELINE_LOGGER_NAME, STEPS_LOGGER_NAME
+
+_pipeline = logging.getLogger(PIPELINE_LOGGER_NAME)
+_steps = logging.getLogger(STEPS_LOGGER_NAME)
+
+
+def is_debug_mode() -> bool:
+    """Chi tiết từng bước chỉ khi LOG_LEVEL=debug."""
+    return os.getenv("LOG_LEVEL", "info").strip().lower() == "debug"
+
+
+def _truncate(val: Any, max_len: int = 2000) -> Any:
+    if val is None:
+        return None
+    if isinstance(val, (int, float, bool)):
+        return val
+    s = str(val)
+    if len(s) > max_len:
+        return s[:max_len] + f"... (+{len(s) - max_len} chars)"
+    return s
+
+
+def log_step(step: str, **fields: Any) -> None:
+    """Chỉ khi LOG_LEVEL=debug: DB / cache / orchestrator."""
+    if not is_debug_mode():
+        return
+    safe = {k: _truncate(v) for k, v in fields.items()}
+    try:
+        payload = json.dumps(safe, ensure_ascii=False, default=str)
+    except Exception:
+        payload = str(safe)
+    _steps.debug("[step:%s] %s", step, payload)
+
+
+def log_pipeline_success(operation: str, **fields: Any) -> None:
+    """Chỉ hiện khi debug (pipeline SUCCESS không dùng ở info — đã có app.access)."""
+    if not is_debug_mode():
+        return
+    safe = {k: _truncate(v, 500) for k, v in fields.items()}
+    _pipeline.info(
+        "SUCCESS %s %s",
+        operation,
+        json.dumps(safe, ensure_ascii=False, default=str),
+    )
+
+
+def log_pipeline_failure(operation: str, error: str | None = None, **fields: Any) -> None:
+    """Thất bại pipeline: luôn dùng WARNING để vẫn thấy khi LOG_LEVEL=warning."""
+    if is_debug_mode():
+        safe = {k: _truncate(v, 500) for k, v in fields.items()}
+        _pipeline.warning(
+            "FAIL %s err=%s %s",
+            operation,
+            _truncate(error, 300),
+            json.dumps(safe, ensure_ascii=False, default=str),
+        )
+    else:
+        _pipeline.warning("FAIL %s", operation)

app/main.py

@@ -0,0 +1,140 @@
+from __future__ import annotations
+
+import logging
+import os
+import time
+import uuid
+import warnings
+
+from dotenv import load_dotenv
+from fastapi import Depends, FastAPI, File, HTTPException, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from starlette.requests import Request
+
+load_dotenv()
+
+from app.runtime_env import apply_runtime_env_defaults
+
+apply_runtime_env_defaults()
+
+os.environ["NO_ALBUMENTATIONS_UPDATE"] = "1"
+warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
+warnings.filterwarnings("ignore", category=UserWarning, module="albumentations")
+
+from app.logging_setup import ACCESS_LOGGER_NAME, get_log_level, setup_application_logging
+
+setup_application_logging()
+
+# Routers (after logging)
+from app.dependencies import get_current_user_id
+from app.ocr_local_file import ocr_from_local_image_path
+from app.routers import auth, sessions, solve
+from agents.ocr_agent import OCRAgent
+from app.routers.solve import get_orchestrator
+from app.supabase_client import get_supabase
+from app.websocket_manager import register_websocket_routes
+
+logger = logging.getLogger("app.main")
+_access = logging.getLogger(ACCESS_LOGGER_NAME)
+
+app = FastAPI(title="Visual Math Solver API v5.1")
+
+
+@app.middleware("http")
+async def access_log_middleware(request: Request, call_next):
+    """LOG_LEVEL=info/debug: mọi request; warning: chỉ 4xx/5xx; error: chỉ 4xx/5xx ở mức error."""
+    start = time.perf_counter()
+    response = await call_next(request)
+    ms = (time.perf_counter() - start) * 1000
+    mode = get_log_level()
+    method = request.method
+    path = request.url.path
+    status = response.status_code
+
+    if mode in ("debug", "info"):
+        _access.info("%s %s -> %s (%.0fms)", method, path, status, ms)
+    elif mode == "warning":
+        if status >= 500:
+            _access.error("%s %s -> %s (%.0fms)", method, path, status, ms)
+        elif status >= 400:
+            _access.warning("%s %s -> %s (%.0fms)", method, path, status, ms)
+    elif mode == "error":
+        if status >= 400:
+            _access.error("%s %s -> %s", method, path, status)
+
+    return response
+
+
+from worker.celery_app import BROKER_URL
+
+_broker_tail = BROKER_URL.split("@")[-1] if "@" in BROKER_URL else BROKER_URL
+if get_log_level() in ("debug", "info"):
+    logger.info("App starting LOG_LEVEL=%s | Redis: %s", get_log_level(), _broker_tail)
+else:
+    logger.warning(
+        "App starting LOG_LEVEL=%s | Redis: %s", get_log_level(), _broker_tail
+    )
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=[
+        "http://localhost:3000",
+        "http://127.0.0.1:3000",
+        "http://localhost:3005",
+    ],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.include_router(auth.router)
+app.include_router(sessions.router)
+app.include_router(solve.router)
+
+register_websocket_routes(app)
+
+
+def get_ocr_agent() -> OCRAgent:
+    """Same OCR instance as the solve pipeline (no duplicate model load)."""
+    return get_orchestrator().ocr_agent
+
+
+supabase_client = get_supabase()
+
+
+@app.get("/")
+def read_root():
+    return {"message": "Visual Math Solver API v5.1 is running", "version": "5.1"}
+
+
+@app.post("/api/v1/ocr")
+async def upload_ocr(
+    file: UploadFile = File(...),
+    _user_id=Depends(get_current_user_id),
+):
+    """OCR upload: requires authenticated user."""
+    temp_path = f"temp_{uuid.uuid4()}.png"
+    with open(temp_path, "wb") as buffer:
+        buffer.write(await file.read())
+
+    try:
+        text = await ocr_from_local_image_path(temp_path, file.filename, get_ocr_agent())
+        return {"text": text}
+    finally:
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+
+
+@app.get("/api/v1/solve/{job_id}")
+async def get_job_status(
+    job_id: str,
+    user_id=Depends(get_current_user_id),
+):
+    """Retrieve job status (can be used for polling if WS fails). Owner-only."""
+    response = supabase_client.table("jobs").select("*").eq("id", job_id).execute()
+    if not response.data:
+        raise HTTPException(status_code=404, detail="Job not found")
+    job = response.data[0]
+    if job.get("user_id") is not None and str(job["user_id"]) != str(user_id):
+        raise HTTPException(status_code=403, detail="Forbidden: You do not own this job.")
+    return job

app/models/schemas.py

@@ -0,0 +1,80 @@
+from pydantic import BaseModel, EmailStr, field_validator
+from typing import Optional, List, Any, Dict
+from datetime import datetime
+import uuid
+
+from app.url_utils import sanitize_url
+
+# --- Auth Schemas ---
+class UserProfile(BaseModel):
+    id: uuid.UUID
+    display_name: Optional[str] = None
+    avatar_url: Optional[str] = None
+    created_at: datetime
+
+class User(BaseModel):
+    id: uuid.UUID
+    email: EmailStr
+
+# --- Session Schemas ---
+class SessionBase(BaseModel):
+    title: str = "Bài toán mới"
+
+class SessionCreate(SessionBase):
+    pass
+
+class Session(SessionBase):
+    id: uuid.UUID
+    user_id: uuid.UUID
+    created_at: datetime
+    updated_at: datetime
+
+    class Config:
+        from_attributes = True
+
+# --- Message Schemas ---
+class MessageBase(BaseModel):
+    role: str
+    type: str = "text"
+    content: str
+    metadata: Dict[str, Any] = {}
+
+class MessageCreate(MessageBase):
+    session_id: uuid.UUID
+
+class Message(MessageBase):
+    id: uuid.UUID
+    session_id: uuid.UUID
+    created_at: datetime
+
+    class Config:
+        from_attributes = True
+
+# --- Solve Job Schemas ---
+class SolveRequest(BaseModel):
+    text: str
+    image_url: Optional[str] = None
+
+    @field_validator("image_url", mode="before")
+    @classmethod
+    def _clean_image_url(cls, v):
+        return sanitize_url(v) if v is not None else None
+
+class SolveResponse(BaseModel):
+    job_id: str
+    status: str
+
+class RenderVideoRequest(BaseModel):
+    job_id: Optional[str] = None
+
+class RenderVideoResponse(BaseModel):
+    job_id: str
+    status: str
+
+
+class OcrPreviewResponse(BaseModel):
+    """Stateless OCR preview before POST .../solve (no DB writes, no job)."""
+
+    ocr_text: str
+    user_message: str = ""
+    combined_draft: str

app/ocr_celery.py

@@ -0,0 +1,54 @@
+"""Run OCR on a remote worker via Celery (queue `ocr`) when OCR_USE_CELERY is enabled."""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import TYPE_CHECKING
+
+import anyio
+
+if TYPE_CHECKING:
+    from agents.ocr_agent import OCRAgent
+
+logger = logging.getLogger(__name__)
+
+
+def ocr_celery_enabled() -> bool:
+    return os.getenv("OCR_USE_CELERY", "").strip().lower() in ("1", "true", "yes", "on")
+
+
+def _ocr_timeout_sec() -> float:
+    raw = os.getenv("OCR_CELERY_TIMEOUT_SEC", "180")
+    try:
+        return max(30.0, float(raw))
+    except ValueError:
+        return 180.0
+
+
+def _run_ocr_celery_sync(image_url: str) -> str:
+    from worker.ocr_tasks import run_ocr_from_url
+
+    async_result = run_ocr_from_url.apply_async(args=[image_url])
+    return async_result.get(timeout=_ocr_timeout_sec())
+
+
+def _is_ocr_error_response(text: str) -> bool:
+    s = (text or "").lstrip()
+    return s.startswith("Error:")
+
+
+async def ocr_from_image_url(image_url: str, fallback_agent: "OCRAgent") -> str:
+    """
+    If OCR_USE_CELERY: delegate to Celery task `run_ocr_from_url` (worker queue `ocr`, raw OCR only),
+    then run ``refine_with_llm`` on the API process.
+    Else: use fallback_agent.process_url (in-process full pipeline).
+    """
+    if not ocr_celery_enabled():
+        return await fallback_agent.process_url(image_url)
+    logger.info("OCR_USE_CELERY: delegating OCR to Celery queue=ocr (LLM refine on API)")
+    raw = await anyio.to_thread.run_sync(_run_ocr_celery_sync, image_url)
+    raw = raw if raw is not None else ""
+    if not raw.strip() or _is_ocr_error_response(raw):
+        return raw
+    return await fallback_agent.refine_with_llm(raw)

app/ocr_local_file.py

@@ -0,0 +1,43 @@
+"""OCR from a local file path, optionally via Celery worker (upload temp blob first)."""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import TYPE_CHECKING
+
+from app.chat_image_upload import (
+    delete_storage_object,
+    upload_ephemeral_ocr_blob,
+    validate_chat_image_bytes,
+)
+from app.ocr_celery import ocr_celery_enabled, ocr_from_image_url
+
+if TYPE_CHECKING:
+    from agents.ocr_agent import OCRAgent
+
+logger = logging.getLogger(__name__)
+
+
+async def ocr_from_local_image_path(
+    local_path: str,
+    original_filename: str | None,
+    fallback_agent: "OCRAgent",
+) -> str:
+    """
+    Run OCR on a file on local disk. If OCR_USE_Celery, upload to ephemeral storage URL
+    then delegate to worker; otherwise process_image in-process.
+    """
+    if not ocr_celery_enabled():
+        return await fallback_agent.process_image(local_path)
+
+    with open(local_path, "rb") as f:
+        body = f.read()
+    ext = os.path.splitext(original_filename or local_path)[1].lower() or ".png"
+    _, content_type = validate_chat_image_bytes(original_filename or local_path, body, None)
+    bucket = os.getenv("SUPABASE_IMAGE_BUCKET", "image")
+    path, url = upload_ephemeral_ocr_blob(body, ext, content_type)
+    try:
+        return await ocr_from_image_url(url, fallback_agent)
+    finally:
+        delete_storage_object(bucket, path)

app/ocr_text_merge.py

@@ -0,0 +1,14 @@
+"""Helpers for OCR preview combined draft (no Pydantic email deps)."""
+
+from __future__ import annotations
+
+from typing import Optional
+
+
+def build_combined_ocr_preview_draft(user_message: Optional[str], ocr_text: str) -> str:
+    """Merge user caption and OCR text for confirm step (user message first, then OCR)."""
+    u = (user_message or "").strip()
+    o = (ocr_text or "").strip()
+    if u and o:
+        return f"{u}\n\n{o}"
+    return u or o

app/routers/__init__.py

@@ -0,0 +1 @@
+from . import auth, sessions, solve

app/routers/auth.py

@@ -0,0 +1,23 @@
+from fastapi import APIRouter, Depends, HTTPException
+from app.dependencies import get_current_user_id
+from app.supabase_client import get_supabase
+from app.models.schemas import UserProfile
+import uuid
+
+router = APIRouter(prefix="/api/v1/auth", tags=["Auth"])
+
+@router.get("/me")
+async def get_me(user_id=Depends(get_current_user_id)):
+    """获取当前登录用户的信息 (Retrieve current user profile)"""
+    supabase = get_supabase()
+    res = supabase.table("profiles").select("*").eq("id", user_id).execute()
+    if not res.data:
+        raise HTTPException(status_code=404, detail="Profile not found.")
+    return res.data[0]
+
+@router.patch("/me")
+async def update_me(data: dict, user_id=Depends(get_current_user_id)):
+    """Cập nhật profile hiện tại (Update current profile)"""
+    supabase = get_supabase()
+    res = supabase.table("profiles").update(data).eq("id", user_id).execute()
+    return res.data[0]

app/routers/sessions.py

@@ -0,0 +1,165 @@
+from __future__ import annotations
+
+from typing import List
+
+from fastapi import APIRouter, Depends, HTTPException
+
+from app.dependencies import get_current_user_id
+from app.logutil import log_step
+from app.session_cache import (
+    get_sessions_list_cached,
+    invalidate_for_user,
+    invalidate_session_owner,
+    session_owned_by_user,
+)
+from app.supabase_client import get_supabase
+
+router = APIRouter(prefix="/api/v1/sessions", tags=["Sessions"])
+
+
+@router.get("", response_model=List[dict])
+async def list_sessions(user_id=Depends(get_current_user_id)):
+    """Danh sách các phiên chat của người dùng (List user's chat sessions)"""
+    supabase = get_supabase()
+
+    def fetch() -> list:
+        res = (
+            supabase.table("sessions")
+            .select("*")
+            .eq("user_id", user_id)
+            .order("updated_at", desc=True)
+            .execute()
+        )
+        log_step("db_select", table="sessions", op="list", user_id=str(user_id))
+        return res.data
+
+    return get_sessions_list_cached(str(user_id), fetch)
+
+
+@router.post("", response_model=dict)
+async def create_session(user_id=Depends(get_current_user_id)):
+    """Tạo một phiên chat mới (Create a new chat session)"""
+    supabase = get_supabase()
+    res = supabase.table("sessions").insert(
+        {"user_id": user_id, "title": "Bài toán mới"}
+    ).execute()
+    log_step("db_insert", table="sessions", op="create")
+    invalidate_for_user(str(user_id))
+    return res.data[0]
+
+
+@router.get("/{session_id}/messages", response_model=List[dict])
+async def get_session_messages(session_id: str, user_id=Depends(get_current_user_id)):
+    """Lấy toàn bộ lịch sử tin nhắn của một phiên (Get chat history for a session)"""
+    supabase = get_supabase()
+
+    def owns() -> bool:
+        res = (
+            supabase.table("sessions")
+            .select("id")
+            .eq("id", session_id)
+            .eq("user_id", user_id)
+            .execute()
+        )
+        log_step("db_select", table="sessions", op="owner_check", session_id=session_id)
+        return bool(res.data)
+
+    if not session_owned_by_user(session_id, str(user_id), owns):
+        raise HTTPException(
+            status_code=403, detail="Forbidden: You do not own this session."
+        )
+
+    res = (
+        supabase.table("messages")
+        .select("*")
+        .eq("session_id", session_id)
+        .order("created_at", desc=False)
+        .execute()
+    )
+    log_step("db_select", table="messages", op="list", session_id=session_id)
+    return res.data
+
+
+@router.delete("/{session_id}")
+async def delete_session(session_id: str, user_id=Depends(get_current_user_id)):
+    """Xóa một phiên chat (Delete a chat session)"""
+    supabase = get_supabase()
+
+    def owns() -> bool:
+        res = (
+            supabase.table("sessions")
+            .select("id")
+            .eq("id", session_id)
+            .eq("user_id", user_id)
+            .execute()
+        )
+        return bool(res.data)
+
+    if not session_owned_by_user(session_id, str(user_id), owns):
+        raise HTTPException(
+            status_code=403, detail="Forbidden: You do not own this session."
+        )
+
+    # jobs.session_id FK must be cleared before sessions row
+    supabase.table("jobs").delete().eq("session_id", session_id).eq("user_id", user_id).execute()
+    log_step("db_delete", table="jobs", op="by_session", session_id=session_id)
+    supabase.table("messages").delete().eq("session_id", session_id).execute()
+    log_step("db_delete", table="messages", op="by_session", session_id=session_id)
+    res = (
+        supabase.table("sessions")
+        .delete()
+        .eq("id", session_id)
+        .eq("user_id", user_id)
+        .execute()
+    )
+    log_step("db_delete", table="sessions", session_id=session_id)
+    invalidate_for_user(str(user_id))
+    invalidate_session_owner(session_id, str(user_id))
+    return {"status": "ok", "deleted_id": session_id}
+
+
+@router.patch("/{session_id}/title")
+async def update_session_title(title: str, session_id: str, user_id=Depends(get_current_user_id)):
+    """Cập nhật tiêu đề phiên chat (Rename a chat session)"""
+    supabase = get_supabase()
+    res = (
+        supabase.table("sessions")
+        .update({"title": title})
+        .eq("id", session_id)
+        .eq("user_id", user_id)
+        .execute()
+    )
+    log_step("db_update", table="sessions", op="title", session_id=session_id)
+    invalidate_for_user(str(user_id))
+    return res.data[0]
+
+
+@router.get("/{session_id}/assets", response_model=List[dict])
+async def get_session_assets(session_id: str, user_id=Depends(get_current_user_id)):
+    """Lấy danh sách video đã render trong session (Get versioned assets for a session)"""
+    supabase = get_supabase()
+
+    def owns() -> bool:
+        res = (
+            supabase.table("sessions")
+            .select("id")
+            .eq("id", session_id)
+            .eq("user_id", user_id)
+            .execute()
+        )
+        return bool(res.data)
+
+    if not session_owned_by_user(session_id, str(user_id), owns):
+        raise HTTPException(
+            status_code=403, detail="Forbidden: You do not own this session."
+        )
+
+    res = (
+        supabase.table("session_assets")
+        .select("*")
+        .eq("session_id", session_id)
+        .order("version", desc=True)
+        .execute()
+    )
+    log_step("db_select", table="session_assets", op="list", session_id=session_id)
+    return res.data

app/routers/solve.py

@@ -0,0 +1,410 @@
+from __future__ import annotations
+
+import logging
+import os
+import uuid
+
+from fastapi import APIRouter, BackgroundTasks, Depends, File, Form, HTTPException, UploadFile
+
+from agents.orchestrator import Orchestrator
+from app.chat_image_upload import upload_session_chat_image, validate_chat_image_bytes
+from app.ocr_celery import ocr_celery_enabled
+from app.ocr_local_file import ocr_from_local_image_path
+from app.dependencies import get_current_user_id
+from app.errors import format_error_for_user
+from app.logutil import log_pipeline_failure, log_pipeline_success, log_step
+from app.models.schemas import (
+    OcrPreviewResponse,
+    RenderVideoRequest,
+    RenderVideoResponse,
+    SolveRequest,
+    SolveResponse,
+)
+from app.ocr_text_merge import build_combined_ocr_preview_draft
+from app.session_cache import invalidate_for_user, session_owned_by_user
+from app.supabase_client import get_supabase
+
+logger = logging.getLogger(__name__)
+router = APIRouter(prefix="/api/v1/sessions", tags=["Solve"])
+
+# Eager init: all agents and models load at import time (also run in Docker build via scripts/prewarm_models.py).
+ORCHESTRATOR = Orchestrator()
+
+
+def get_orchestrator() -> Orchestrator:
+    return ORCHESTRATOR
+
+
+_OCR_PREVIEW_MAX_BYTES = 10 * 1024 * 1024
+
+
+def _assert_session_owner(supabase, session_id: str, user_id, uid: str, op: str) -> None:
+    def owns() -> bool:
+        res = (
+            supabase.table("sessions")
+            .select("id")
+            .eq("id", session_id)
+            .eq("user_id", user_id)
+            .execute()
+        )
+        log_step("db_select", table="sessions", op=op, session_id=session_id)
+        return bool(res.data)
+
+    if not session_owned_by_user(session_id, uid, owns):
+        log_pipeline_failure("solve_request", error="forbidden", session_id=session_id)
+        raise HTTPException(
+            status_code=403, detail="Forbidden: You do not own this session."
+        )
+
+
+def _enqueue_solve_common(
+    supabase,
+    background_tasks: BackgroundTasks,
+    session_id: str,
+    user_id,
+    uid: str,
+    request: SolveRequest,
+    message_metadata: dict,
+    job_id: str,
+) -> SolveResponse:
+    """Insert user message, job row, enqueue pipeline; update title when first message."""
+    supabase.table("messages").insert(
+        {
+            "session_id": session_id,
+            "role": "user",
+            "type": "text",
+            "content": request.text,
+            "metadata": message_metadata,
+        }
+    ).execute()
+    log_step("db_insert", table="messages", op="user_message", session_id=session_id)
+
+    supabase.table("jobs").insert(
+        {
+            "id": job_id,
+            "user_id": user_id,
+            "session_id": session_id,
+            "status": "processing",
+            "input_text": request.text,
+        }
+    ).execute()
+    log_step("db_insert", table="jobs", job_id=job_id)
+
+    background_tasks.add_task(process_session_job, job_id, session_id, request, str(user_id))
+
+    title_check = supabase.table("sessions").select("title").eq("id", session_id).execute()
+    if title_check.data and title_check.data[0]["title"] == "Bài toán mới":
+        new_title = request.text[:50] + ("..." if len(request.text) > 50 else "")
+        supabase.table("sessions").update({"title": new_title}).eq("id", session_id).execute()
+        log_step("db_update", table="sessions", op="title_from_first_message")
+        invalidate_for_user(uid)
+
+    log_pipeline_success("solve_accepted", job_id=job_id, session_id=session_id)
+    return SolveResponse(job_id=job_id, status="processing")
+
+
+@router.post("/{session_id}/ocr_preview", response_model=OcrPreviewResponse)
+async def ocr_preview(
+    session_id: str,
+    user_id=Depends(get_current_user_id),
+    file: UploadFile = File(...),
+    user_message: str | None = Form(None),
+):
+    """
+    Run OCR on an uploaded image and merge with optional user_message into combined_draft.
+    Does not insert messages or start a solve job. After user confirms, call POST .../solve
+    with text=combined_draft (edited) and omit image_url to avoid double OCR.
+    """
+    supabase = get_supabase()
+    uid = str(user_id)
+    _assert_session_owner(supabase, session_id, user_id, uid, "owner_check_ocr_preview")
+
+    body = await file.read()
+    if len(body) > _OCR_PREVIEW_MAX_BYTES:
+        raise HTTPException(
+            status_code=413,
+            detail=f"Image too large (max {_OCR_PREVIEW_MAX_BYTES // (1024 * 1024)} MB).",
+        )
+    if not body:
+        raise HTTPException(status_code=400, detail="Empty file.")
+
+    if ocr_celery_enabled():
+        validate_chat_image_bytes(file.filename, body, file.content_type)
+
+    suffix = os.path.splitext(file.filename or "")[1].lower()
+    if suffix not in (".png", ".jpg", ".jpeg", ".webp", ".gif", ".bmp", ""):
+        suffix = ".png"
+    temp_path = f"temp_ocr_preview_{uuid.uuid4()}{suffix or '.png'}"
+    try:
+        with open(temp_path, "wb") as f:
+            f.write(body)
+        ocr_text = await ocr_from_local_image_path(
+            temp_path, file.filename, get_orchestrator().ocr_agent
+        )
+        if ocr_text is None:
+            ocr_text = ""
+    finally:
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+
+    um = (user_message or "").strip()
+    combined = build_combined_ocr_preview_draft(user_message, ocr_text)
+    log_step("ocr_preview_done", session_id=session_id, ocr_len=len(ocr_text), user_len=len(um))
+    return OcrPreviewResponse(
+        ocr_text=ocr_text,
+        user_message=um,
+        combined_draft=combined,
+    )
+
+
+@router.post("/{session_id}/solve", response_model=SolveResponse)
+async def solve_problem(
+    session_id: str,
+    request: SolveRequest,
+    background_tasks: BackgroundTasks,
+    user_id=Depends(get_current_user_id),
+):
+    """
+    Gửi câu hỏi giải toán trong một session (Submit geometry problem in a session).
+    Lưu câu hỏi vào history và bắt đầu tiến trình giải (chỉ giải toán và tạo hình tĩnh).
+    """
+    supabase = get_supabase()
+    uid = str(user_id)
+    _assert_session_owner(supabase, session_id, user_id, uid, "owner_check")
+
+    message_metadata = {"image_url": request.image_url} if request.image_url else {}
+    job_id = str(uuid.uuid4())
+    return _enqueue_solve_common(
+        supabase,
+        background_tasks,
+        session_id,
+        user_id,
+        uid,
+        request,
+        message_metadata,
+        job_id,
+    )
+
+
+@router.post("/{session_id}/solve_multipart", response_model=SolveResponse)
+async def solve_multipart(
+    session_id: str,
+    background_tasks: BackgroundTasks,
+    user_id=Depends(get_current_user_id),
+    text: str = Form(...),
+    file: UploadFile = File(...),
+):
+    """
+    Gửi text + file ảnh trong một request multipart: validate, upload bucket `image`,
+    ghi session_assets, lưu message kèm metadata (URL, size, type), rồi enqueue solve
+    (image_url trỏ public URL để orchestrator OCR).
+    """
+    supabase = get_supabase()
+    uid = str(user_id)
+    _assert_session_owner(supabase, session_id, user_id, uid, "owner_check_solve_multipart")
+
+    t = (text or "").strip()
+    if not t:
+        raise HTTPException(status_code=400, detail="text must not be empty.")
+
+    body = await file.read()
+    ext, content_type = validate_chat_image_bytes(file.filename, body, file.content_type)
+
+    job_id = str(uuid.uuid4())
+    up = upload_session_chat_image(session_id, job_id, body, ext, content_type)
+    public_url = up["public_url"]
+
+    message_metadata = {
+        "image_url": public_url,
+        "attachment": {
+            "public_url": public_url,
+            "storage_path": up["storage_path"],
+            "size_bytes": len(body),
+            "content_type": content_type,
+            "original_filename": file.filename or "",
+            "session_asset_id": up.get("session_asset_id"),
+        },
+    }
+    request = SolveRequest(text=t, image_url=public_url)
+    return _enqueue_solve_common(
+        supabase,
+        background_tasks,
+        session_id,
+        user_id,
+        uid,
+        request,
+        message_metadata,
+        job_id,
+    )
+
+
+@router.post("/{session_id}/render_video", response_model=RenderVideoResponse)
+async def render_video(
+    session_id: str,
+    request: RenderVideoRequest,
+    background_tasks: BackgroundTasks,
+    user_id=Depends(get_current_user_id),
+):
+    """
+    Yêu cầu tạo video Manim từ trạng thái hình ảnh mới nhất của session.
+    """
+    supabase = get_supabase()
+    
+    # 1. Kiểm tra quyền sở hữu
+    res = supabase.table("sessions").select("id").eq("id", session_id).eq("user_id", user_id).execute()
+    if not res.data:
+        raise HTTPException(status_code=403, detail="Forbidden: You do not own this session.")
+
+    # 2. Tìm tin nhắn assistant có metadata hình học (cụ thể job_id hoặc mới nhất trong 10 tin nhắn gần nhất)
+    msg_res = (
+        supabase.table("messages")
+        .select("metadata")
+        .eq("session_id", session_id)
+        .eq("role", "assistant")
+        .order("created_at", desc=True)
+        .limit(10)
+        .execute()
+    )
+    
+    latest_geometry = None
+    if msg_res.data:
+        for msg in msg_res.data:
+            meta = msg.get("metadata", {})
+            # Nếu có yêu cầu job_id cụ thể, phải khớp job_id
+            if request.job_id and meta.get("job_id") != request.job_id:
+                continue
+            
+            # Phải có dữ liệu hình học
+            if meta.get("geometry_dsl") and meta.get("coordinates"):
+                latest_geometry = meta
+                break
+    
+    if not latest_geometry:
+        raise HTTPException(status_code=404, detail="Không tìm thấy dữ liệu hình học để render video.")
+
+    # 3. Tạo Job rendering
+    job_id = str(uuid.uuid4())
+    supabase.table("jobs").insert({
+        "id": job_id,
+        "user_id": user_id,
+        "session_id": session_id,
+        "status": "rendering_queued",
+        "input_text": f"Render video requested at {job_id}",
+    }).execute()
+
+    # 4. Dispatch background task
+    background_tasks.add_task(process_render_job, job_id, session_id, latest_geometry)
+    
+    return RenderVideoResponse(job_id=job_id, status="rendering_queued")
+
+
+async def process_session_job(
+    job_id: str, session_id: str, request: SolveRequest, user_id: str
+):
+    """Tiến trình giải toán ngầm, tạo hình ảnh tĩnh."""
+    from app.websocket_manager import notify_status
+
+    async def status_update(status: str):
+        await notify_status(job_id, {"status": status})
+
+    supabase = get_supabase()
+    try:
+        history_res = (
+            supabase.table("messages")
+            .select("*")
+            .eq("session_id", session_id)
+            .order("created_at", desc=False)
+            .execute()
+        )
+        history = history_res.data if history_res.data else []
+
+        result = await get_orchestrator().run(
+            request.text,
+            request.image_url,
+            job_id=job_id,
+            session_id=session_id,
+            status_callback=status_update,
+            history=history,
+        )
+
+        status = result.get("status", "error") if "error" not in result else "error"
+
+        supabase.table("jobs").update({"status": status, "result": result}).eq(
+            "id", job_id
+        ).execute()
+
+        supabase.table("messages").insert(
+            {
+                "session_id": session_id,
+                "role": "assistant",
+                "type": "analysis" if "error" not in result else "error",
+                "content": (
+                    result.get("semantic_analysis", "Đã có lỗi xảy ra.")
+                    if "error" not in result
+                    else result["error"]
+                ),
+                "metadata": {
+                    "job_id": job_id,
+                    "coordinates": result.get("coordinates"),
+                    "geometry_dsl": result.get("geometry_dsl"),
+                    "polygon_order": result.get("polygon_order", []),
+                    "drawing_phases": result.get("drawing_phases", []),
+                    "circles": result.get("circles", []),
+                    "lines": result.get("lines", []),
+                    "rays": result.get("rays", []),
+                    "solution": result.get("solution"),
+                    "is_3d": result.get("is_3d", False),
+                },
+            }
+        ).execute()
+
+        await notify_status(job_id, {"status": status, "result": result})
+
+    except Exception as e:
+        logger.exception("Error processing session job %s", job_id)
+        error_msg = format_error_for_user(e)
+        supabase = get_supabase()
+        supabase.table("jobs").update(
+            {"status": "error", "result": {"error": str(e)}}
+        ).eq("id", job_id).execute()
+        supabase.table("messages").insert(
+            {
+                "session_id": session_id,
+                "role": "assistant",
+                "type": "error",
+                "content": error_msg,
+                "metadata": {"job_id": job_id},
+            }
+        ).execute()
+        await notify_status(job_id, {"status": "error", "error": error_msg})
+
+async def process_render_job(job_id: str, session_id: str, geometry_data: dict):
+    """Tiến trình render video từ metadata có sẵn."""
+    from app.websocket_manager import notify_status
+    from worker.tasks import render_geometry_video
+    
+    await notify_status(job_id, {"status": "rendering_queued"})
+    
+    # Prepare payload for Celery (similar to what orchestrator used to do)
+    result_payload = {
+        "geometry_dsl": geometry_data.get("geometry_dsl"),
+        "coordinates": geometry_data.get("coordinates"),
+        "polygon_order": geometry_data.get("polygon_order", []),
+        "drawing_phases": geometry_data.get("drawing_phases", []),
+        "circles": geometry_data.get("circles", []),
+        "lines": geometry_data.get("lines", []),
+        "rays": geometry_data.get("rays", []),
+        "semantic": geometry_data.get("semantic", {}),
+        "semantic_analysis": geometry_data.get("semantic_analysis", "🎬 Video minh họa dựng từ trạng thái gần nhất."),
+        "session_id": session_id,
+    }
+    
+    try:
+        logger.info(f"[RenderJob] Attempting to dispatch Celery task for job {job_id}...")
+        render_geometry_video.delay(job_id, result_payload)
+        logger.info(f"[RenderJob] SUCCESS: Dispatched Celery task for job {job_id}")
+    except Exception as e:
+        logger.exception(f"[RenderJob] FAILED to dispatch Celery task: {e}")
+        supabase = get_supabase()
+        supabase.table("jobs").update({"status": "error", "result": {"error": f"Task dispatch failed: {str(e)}"}}).eq("id", job_id).execute()
+        await notify_status(job_id, {"status": "error", "error": str(e)})

app/runtime_env.py

@@ -0,0 +1,12 @@
+"""Default process env vars (Paddle/OpenMP). Call as early as possible after load_dotenv."""
+
+from __future__ import annotations
+
+import os
+
+
+def apply_runtime_env_defaults() -> None:
+    # Paddle respects OMP_NUM_THREADS at import; setdefault loses if platform already set 2+
+    os.environ["OMP_NUM_THREADS"] = "1"
+    os.environ["MKL_NUM_THREADS"] = "1"
+    os.environ["OPENBLAS_NUM_THREADS"] = "1"

app/session_cache.py

@@ -0,0 +1,48 @@
+"""TTL in-memory cache để giảm truy vấn Supabase lặp lại (list session, quyền sở hữu session)."""
+
+from __future__ import annotations
+
+from typing import Any, Callable
+
+from cachetools import TTLCache
+
+from app.logutil import log_step
+
+_session_list: TTLCache[str, list[Any]] = TTLCache(maxsize=512, ttl=45)
+_session_owner: TTLCache[tuple[str, str], bool] = TTLCache(maxsize=4096, ttl=45)
+
+
+def invalidate_for_user(user_id: str) -> None:
+    """Xoá cache list session của user (sau create / delete / rename / solve đổi title)."""
+    _session_list.pop(user_id, None)
+    log_step("cache_invalidate", target="session_list", user_id=user_id)
+
+
+def invalidate_session_owner(session_id: str, user_id: str) -> None:
+    _session_owner.pop((session_id, user_id), None)
+    log_step("cache_invalidate", target="session_owner", session_id=session_id, user_id=user_id)
+
+
+def get_sessions_list_cached(user_id: str, fetch: Callable[[], list[Any]]) -> list[Any]:
+    if user_id in _session_list:
+        log_step("cache_hit", kind="session_list", user_id=user_id)
+        return _session_list[user_id]
+    log_step("cache_miss", kind="session_list", user_id=user_id)
+    data = fetch()
+    _session_list[user_id] = data
+    return data
+
+
+def session_owned_by_user(
+    session_id: str,
+    user_id: str,
+    fetch: Callable[[], bool],
+) -> bool:
+    key = (session_id, user_id)
+    if key in _session_owner:
+        log_step("cache_hit", kind="session_owner", session_id=session_id)
+        return _session_owner[key]
+    log_step("cache_miss", kind="session_owner", session_id=session_id)
+    ok = fetch()
+    _session_owner[key] = ok
+    return ok

app/supabase_client.py

@@ -0,0 +1,37 @@
+import os
+from supabase import Client, ClientOptions, create_client
+from supabase_auth import SyncMemoryStorage
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from app.url_utils import sanitize_env
+
+
+def get_supabase() -> Client:
+    """Service-role client for server-side operations (bypasses RLS when policies expect service role)."""
+    url = sanitize_env(os.getenv("SUPABASE_URL"))
+    key = sanitize_env(os.getenv("SUPABASE_SERVICE_ROLE_KEY") or os.getenv("SUPABASE_KEY"))
+    if not url or not key:
+        raise RuntimeError(
+            "SUPABASE_URL and SUPABASE_SERVICE_ROLE_KEY (or SUPABASE_KEY) must be set"
+        )
+    return create_client(url, key)
+
+
+def get_supabase_for_user_jwt(access_token: str) -> Client:
+    """
+    Client scoped to the logged-in user: PostgREST sends the user's JWT so RLS applies.
+    Use SUPABASE_ANON_KEY (publishable), not the service role key.
+    """
+    url = sanitize_env(os.getenv("SUPABASE_URL"))
+    anon = sanitize_env(os.getenv("SUPABASE_ANON_KEY") or os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY"))
+    if not url or not anon:
+        raise RuntimeError(
+            "SUPABASE_URL and SUPABASE_ANON_KEY (or NEXT_PUBLIC_SUPABASE_ANON_KEY) must be set "
+            "for user-scoped Supabase access"
+        )
+    base_opts = ClientOptions(storage=SyncMemoryStorage())
+    merged_headers = {**dict(base_opts.headers), "Authorization": f"Bearer {access_token}"}
+    opts = ClientOptions(storage=SyncMemoryStorage(), headers=merged_headers)
+    return create_client(url, anon, opts)

app/url_utils.py

@@ -0,0 +1,23 @@
+"""Normalize URLs / env strings (HF secrets and copy-paste often include trailing newlines)."""
+
+
+def sanitize_url(value: str | None) -> str | None:
+    if value is None:
+        return None
+    s = value.strip().replace("\r", "").replace("\n", "").replace("\t", "")
+    return s or None
+
+
+def sanitize_env(value: str | None) -> str | None:
+    """Strip whitespace and line breaks from environment-backed strings."""
+    return sanitize_url(value)
+
+
+# OpenAI SDK (>=1.x) requires a non-empty api_key at client construction (Docker build / prewarm has no secrets).
+_OPENAI_API_KEY_BUILD_PLACEHOLDER = "build-placeholder-openrouter-not-for-production"
+
+
+def openai_compatible_api_key(raw: str | None) -> str:
+    """Return sanitized API key, or a placeholder so AsyncOpenAI() can be constructed without env at build time."""
+    k = sanitize_env(raw)
+    return k if k else _OPENAI_API_KEY_BUILD_PLACEHOLDER

app/websocket_manager.py

@@ -0,0 +1,40 @@
+"""WebSocket connection registry and job status notifications (avoid circular imports with main)."""
+
+from __future__ import annotations
+
+import logging
+from typing import Dict, List
+
+from fastapi import WebSocket, WebSocketDisconnect
+
+logger = logging.getLogger(__name__)
+
+active_connections: Dict[str, List[WebSocket]] = {}
+
+
+async def notify_status(job_id: str, data: dict) -> None:
+    if job_id not in active_connections:
+        return
+    for connection in list(active_connections[job_id]):
+        try:
+            await connection.send_json(data)
+        except Exception as e:
+            logger.error("WS error sending to %s: %s", job_id, e)
+
+
+def register_websocket_routes(app) -> None:
+    """Attach websocket endpoint to the FastAPI app."""
+
+    @app.websocket("/ws/{job_id}")
+    async def websocket_endpoint(websocket: WebSocket, job_id: str) -> None:
+        await websocket.accept()
+        if job_id not in active_connections:
+            active_connections[job_id] = []
+        active_connections[job_id].append(websocket)
+        try:
+            while True:
+                await websocket.receive_text()
+        except WebSocketDisconnect:
+            active_connections[job_id].remove(websocket)
+            if not active_connections[job_id]:
+                del active_connections[job_id]

clean_ports.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+# Script to kill all project-related processes for a clean restart
+
+echo "🧹 Cleaning up project processes..."
+
+# Kill things on ports 8000 (Backend) and 3000 (Frontend)
+PORTS="8000 3000 11020"
+for PORT in $PORTS; do
+    PIDS=$(lsof -ti :$PORT)
+    if [ ! -z "$PIDS" ]; then
+        echo "Killing processes on port $PORT: $PIDS"
+        kill -9 $PIDS 2>/dev/null
+    fi
+done
+
+# Kill by process name
+echo "Killing any remaining Celery, Uvicorn, or Manim processes..."
+pkill -9 -f "celery" 2>/dev/null
+pkill -9 -f "uvicorn" 2>/dev/null
+pkill -9 -f "manim" 2>/dev/null
+
+echo "✅ Done. You can now restart your Backend, Worker, and Frontend."

migrations/add_image_bucket_storage.sql

@@ -0,0 +1,35 @@
+-- ============================================================
+-- MathSolver: Supabase Storage bucket `image` (chat / OCR attachments)
+-- Run after session_assets and storage.video policies exist.
+-- ============================================================
+
+INSERT INTO storage.buckets (id, name, public)
+VALUES ('image', 'image', true)
+ON CONFLICT (id) DO UPDATE SET public = true;
+
+-- Service role: upload/delete/list for API + workers
+DROP POLICY IF EXISTS "Service Role manage images" ON storage.objects;
+CREATE POLICY "Service Role manage images" ON storage.objects
+    FOR ALL
+    TO service_role
+    USING (bucket_id = 'image')
+    WITH CHECK (bucket_id = 'image');
+
+-- Authenticated: read only objects under sessions they own (path sessions/{session_id}/...)
+DROP POLICY IF EXISTS "Users view session images" ON storage.objects;
+CREATE POLICY "Users view session images" ON storage.objects
+    FOR SELECT
+    TO authenticated
+    USING (
+        bucket_id = 'image'
+        AND (storage.foldername(name))[2] IN (
+            SELECT id::text FROM public.sessions WHERE user_id = auth.uid()
+        )
+    );
+
+-- Public read for get_public_url / FE img tags (same model as video bucket)
+DROP POLICY IF EXISTS "Public read images" ON storage.objects;
+CREATE POLICY "Public read images" ON storage.objects
+    FOR SELECT
+    TO public
+    USING (bucket_id = 'image');

migrations/fix_rls_assets.sql

@@ -0,0 +1,96 @@
+-- ============================================================
+-- FIX RLS & SESSION ASSETS (MathSolver v5.1 Worker Fix)
+-- ============================================================
+
+-- 1. Ensure session_assets table exists
+CREATE TABLE IF NOT EXISTS public.session_assets (
+    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    session_id UUID NOT NULL REFERENCES public.sessions(id) ON DELETE CASCADE,
+    job_id UUID NOT NULL,
+    asset_type TEXT NOT NULL CHECK (asset_type IN ('video', 'image')),
+    storage_path TEXT NOT NULL,
+    public_url TEXT NOT NULL,
+    version INTEGER NOT NULL DEFAULT 1,
+    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
+);
+
+-- Index for session_assets
+CREATE INDEX IF NOT EXISTS idx_session_assets_session_id ON public.session_assets(session_id);
+CREATE INDEX IF NOT EXISTS idx_session_assets_type ON public.session_assets(session_id, asset_type);
+
+-- 2. Enable RLS for all tables
+ALTER TABLE public.session_assets ENABLE ROW LEVEL SECURITY;
+ALTER TABLE public.profiles ENABLE ROW LEVEL SECURITY;
+ALTER TABLE public.sessions ENABLE ROW LEVEL SECURITY;
+ALTER TABLE public.messages ENABLE ROW LEVEL SECURITY;
+ALTER TABLE public.jobs ENABLE ROW LEVEL SECURITY;
+
+
+-- 3. Fix Table Policies to allow SERVICE ROLE
+-- In Supabase, service_role usually bypasses RLS, but we add explicit policies for safety
+-- especially for path-based checks or when SECURITY DEFINER functions are used.
+
+-- [Session Assets]
+DROP POLICY IF EXISTS "Users view own assets" ON public.session_assets;
+CREATE POLICY "Users view own assets" ON public.session_assets 
+    FOR SELECT USING (
+        session_id IN (SELECT id FROM public.sessions WHERE user_id = auth.uid())
+    );
+
+DROP POLICY IF EXISTS "Service role manages assets" ON public.session_assets;
+CREATE POLICY "Service role manages assets" ON public.session_assets 
+    FOR ALL USING (true) 
+    WITH CHECK (true);
+
+
+-- [Messages] - Allow Worker to insert assistant messages
+DROP POLICY IF EXISTS "Users manage own messages" ON public.messages;
+CREATE POLICY "Users manage own messages" ON public.messages 
+    FOR ALL USING (
+        session_id IN (SELECT id FROM public.sessions WHERE user_id = auth.uid())
+        OR 
+        (auth.jwt() ->> 'role' = 'service_role')
+    );
+
+
+-- [Jobs] - Allow Worker to update job status
+DROP POLICY IF EXISTS "Users manage own jobs" ON public.jobs;
+CREATE POLICY "Users manage own jobs" ON public.jobs 
+    FOR ALL USING (
+        auth.uid() = user_id 
+        OR user_id IS NULL
+        OR (auth.jwt() ->> 'role' = 'service_role')
+    );
+
+
+-- 4. Storage Policies (Bucket: video)
+-- Ensure 'video' bucket exists
+INSERT INTO storage.buckets (id, name, public)
+VALUES ('video', 'video', true)
+ON CONFLICT (id) DO UPDATE SET public = true;
+
+-- [Storage: Worker / Service Role] - Allow all in video bucket
+DROP POLICY IF EXISTS "Service Role manage videos" ON storage.objects;
+CREATE POLICY "Service Role manage videos" ON storage.objects
+    FOR ALL 
+    TO service_role
+    USING (bucket_id = 'video');
+
+-- [Storage: Users] - Allow users to view their session videos
+DROP POLICY IF EXISTS "Users view session videos" ON storage.objects;
+CREATE POLICY "Users view session videos" ON storage.objects
+    FOR SELECT 
+    TO authenticated
+    USING (
+        bucket_id = 'video' 
+        AND (storage.foldername(name))[2] IN (
+            SELECT id::text FROM public.sessions WHERE user_id = auth.uid()
+        )
+    );
+
+-- [Storage: Public] - Allow public read access to videos
+DROP POLICY IF EXISTS "Public read videos" ON storage.objects;
+CREATE POLICY "Public read videos" ON storage.objects
+    FOR SELECT 
+    TO public
+    USING (bucket_id = 'video');

migrations/v4_migration.sql

@@ -0,0 +1,131 @@
+-- ============================================================
+-- MATHSOLVER v4.0 - Migration Script (Multi-Session & History)
+-- ============================================================
+
+-- 1. Profiles Table (Extends Supabase Auth)
+CREATE TABLE IF NOT EXISTS public.profiles (
+    id UUID PRIMARY KEY REFERENCES auth.users(id) ON DELETE CASCADE,
+    display_name TEXT,
+    avatar_url TEXT,
+    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
+    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
+);
+
+-- Function to handle new user signup and auto-create profile
+CREATE OR REPLACE FUNCTION public.handle_new_user()
+RETURNS TRIGGER AS $$
+BEGIN
+    INSERT INTO public.profiles (id, display_name, avatar_url)
+    VALUES (
+        NEW.id, 
+        COALESCE(NEW.raw_user_meta_data->>'full_name', NEW.email),
+        NEW.raw_user_meta_data->>'avatar_url'
+    );
+    RETURN NEW;
+END;
+$$ LANGUAGE plpgsql SECURITY DEFINER;
+
+-- Trigger for profile creation
+DROP TRIGGER IF EXISTS on_auth_user_created ON auth.users;
+CREATE TRIGGER on_auth_user_created
+    AFTER INSERT ON auth.users
+    FOR EACH ROW EXECUTE FUNCTION public.handle_new_user();
+
+-- 2. Sessions Table
+CREATE TABLE IF NOT EXISTS public.sessions (
+    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    user_id UUID NOT NULL REFERENCES auth.users(id) ON DELETE CASCADE,
+    title TEXT DEFAULT 'Bài toán mới',
+    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
+    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
+);
+
+-- Index for sessions
+CREATE INDEX IF NOT EXISTS idx_sessions_user_id ON public.sessions(user_id);
+CREATE INDEX IF NOT EXISTS idx_sessions_updated_at ON public.sessions(updated_at DESC);
+
+-- 3. Messages Table
+CREATE TABLE IF NOT EXISTS public.messages (
+    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    session_id UUID NOT NULL REFERENCES public.sessions(id) ON DELETE CASCADE,
+    role TEXT NOT NULL CHECK (role IN ('user', 'assistant', 'system')),
+    type TEXT NOT NULL DEFAULT 'text',
+    content TEXT NOT NULL,
+    metadata JSONB DEFAULT '{}'::jsonb,
+    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
+);
+
+-- Index for messages
+CREATE INDEX IF NOT EXISTS idx_messages_session_id ON public.messages(session_id);
+CREATE INDEX IF NOT EXISTS idx_messages_created_at ON public.messages(session_id, created_at);
+
+-- 4. Session Assets Table (v5.1 Versioning)
+CREATE TABLE IF NOT EXISTS public.session_assets (
+    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    session_id UUID NOT NULL REFERENCES public.sessions(id) ON DELETE CASCADE,
+    job_id UUID NOT NULL,
+    asset_type TEXT NOT NULL CHECK (asset_type IN ('video', 'image')),
+    storage_path TEXT NOT NULL,
+    public_url TEXT NOT NULL,
+    version INTEGER NOT NULL DEFAULT 1,
+    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
+);
+
+-- Index for session_assets
+CREATE INDEX IF NOT EXISTS idx_session_assets_session_id ON public.session_assets(session_id);
+
+-- 5. Update Jobs Table
+ALTER TABLE public.jobs ADD COLUMN IF NOT EXISTS user_id UUID REFERENCES auth.users(id);
+ALTER TABLE public.jobs ADD COLUMN IF NOT EXISTS session_id UUID REFERENCES public.sessions(id);
+
+-- 6. Row Level Security (RLS)
+ALTER TABLE public.profiles ENABLE ROW LEVEL SECURITY;
+ALTER TABLE public.sessions ENABLE ROW LEVEL SECURITY;
+ALTER TABLE public.messages ENABLE ROW LEVEL SECURITY;
+ALTER TABLE public.jobs ENABLE ROW LEVEL SECURITY;
+ALTER TABLE public.session_assets ENABLE ROW LEVEL SECURITY;
+
+-- Polices for public.profiles
+DROP POLICY IF EXISTS "Users view own profile" ON public.profiles;
+CREATE POLICY "Users view own profile" ON public.profiles FOR SELECT USING (auth.uid() = id);
+DROP POLICY IF EXISTS "Users update own profile" ON public.profiles;
+CREATE POLICY "Users update own profile" ON public.profiles FOR UPDATE USING (auth.uid() = id);
+
+-- Policies for public.sessions
+DROP POLICY IF EXISTS "Users manage own sessions" ON public.sessions;
+CREATE POLICY "Users manage own sessions" ON public.sessions FOR ALL USING (auth.uid() = user_id);
+
+-- Policies for public.messages
+DROP POLICY IF EXISTS "Users manage own messages" ON public.messages;
+CREATE POLICY "Users manage own messages" ON public.messages FOR ALL USING (
+    session_id IN (SELECT id FROM public.sessions WHERE user_id = auth.uid())
+    OR (auth.jwt() ->> 'role' = 'service_role')
+);
+
+-- Policies for public.session_assets
+DROP POLICY IF EXISTS "Users view own assets" ON public.session_assets;
+CREATE POLICY "Users view own assets" ON public.session_assets FOR SELECT USING (
+    session_id IN (SELECT id FROM public.sessions WHERE user_id = auth.uid())
+);
+DROP POLICY IF EXISTS "Service role manages assets" ON public.session_assets;
+CREATE POLICY "Service role manages assets" ON public.session_assets FOR ALL USING (true);
+
+-- Policies for public.jobs
+DROP POLICY IF EXISTS "Users manage own jobs" ON public.jobs;
+CREATE POLICY "Users manage own jobs" ON public.jobs FOR ALL USING (
+    auth.uid() = user_id OR user_id IS NULL OR (auth.jwt() ->> 'role' = 'service_role')
+);
+
+-- 7. Storage Policies (Bucket: video)
+-- (Run this in Supabase Dashboard if not allowed in migration)
+-- INSERT INTO storage.buckets (id, name, public) VALUES ('video', 'video', true) ON CONFLICT (id) DO NOTHING;
+-- CREATE POLICY "Service Role manage videos" ON storage.objects FOR ALL TO service_role USING (bucket_id = 'video');
+-- CREATE POLICY "Public read videos" ON storage.objects FOR SELECT TO public USING (bucket_id = 'video');
+
+-- Grant permissions to public/authenticated
+GRANT ALL ON public.profiles TO authenticated;
+GRANT ALL ON public.sessions TO authenticated;
+GRANT ALL ON public.messages TO authenticated;
+GRANT ALL ON public.jobs TO authenticated;
+GRANT ALL ON public.session_assets TO authenticated;
+GRANT ALL ON public.session_assets TO service_role;

pytest.ini

@@ -0,0 +1,18 @@
+[pytest]
+asyncio_mode = auto
+testpaths = tests
+pythonpath = .
+filterwarnings =
+    ignore::DeprecationWarning
+
+markers =
+    real_api: HTTP tests need running backend and TEST_USER_ID / TEST_SESSION_ID.
+    real_worker_ocr: OCR Celery task or full OCR stack (heavy).
+    real_worker_manim: Real Manim render and Supabase video upload.
+    real_agents: Live LLM / orchestrator agent calls.
+    slow: Large suite or long polling timeouts.
+    smoke: Fast API health + one solve job.
+    orchestrator_local: In-process Orchestrator without HTTP server.
+
+# Default: skip integration tests that need services, keys, or long runs.
+addopts = -m "not real_api and not real_worker_ocr and not real_worker_manim and not real_agents and not slow and not orchestrator_local"

requirements.txt

@@ -0,0 +1,38 @@
+# Target: Python 3.11 (see Dockerfile). Used by: FastAPI API, Celery worker, Manim render, OCR/vision stack.
+# Install: pip install -r requirements.txt
+
+# --- Dev / test ---
+pytest>=8.0
+pytest-asyncio>=0.24
+
+# --- HTTP API ---
+cachetools>=5.3
+fastapi>=0.115,<1
+uvicorn[standard]>=0.30
+python-multipart>=0.0.9
+python-dotenv>=1.0
+pydantic[email]>=2.4
+email-validator>=2
+
+# --- Auth / data / queue ---
+openai>=1.40
+supabase>=2.0
+celery>=5.3
+redis>=5
+httpx>=0.27
+websockets>=12
+
+# --- Math & symbolic solver ---
+sympy>=1.12
+numpy>=1.26,<2
+scipy>=1.11
+opencv-python-headless>=4.8,<4.10
+
+# --- Video (GeometryScene via CLI) ---
+manim>=0.18,<0.20
+
+# --- OCR & vision (orchestrator / legacy /ocr) ---
+pix2tex>=0.1.4
+paddleocr==2.7.3
+paddlepaddle==2.6.2
+ultralytics==8.2.2

requirements.worker-ocr.txt

@@ -0,0 +1,23 @@
+# OCR-only Celery worker: YOLO + PaddleOCR + Pix2Tex (no OpenRouter / no Manim).
+# Install: pip install -r requirements.worker-ocr.txt
+
+cachetools>=5.3
+fastapi>=0.115,<1
+uvicorn[standard]>=0.30
+python-multipart>=0.0.9
+python-dotenv>=1.0
+pydantic[email]>=2.4
+email-validator>=2
+
+celery>=5.3
+redis>=5
+httpx>=0.27
+websockets>=12
+
+numpy>=1.26,<2
+opencv-python-headless>=4.8,<4.10
+
+pix2tex>=0.1.4
+paddleocr==2.7.3
+paddlepaddle==2.6.2
+ultralytics==8.2.2

run_api_test.sh

@@ -0,0 +1,69 @@
+#!/bin/bash
+
+LOG_FILE="api_test_results.log"
+echo "=== Starting API E2E Test Suite ($(date)) ===" > $LOG_FILE
+
+# 1. Start BE Server in background
+echo "[INFO] Starting Backend Server..." | tee -a $LOG_FILE
+export ALLOW_TEST_BYPASS=true
+export LOG_LEVEL=info
+export CELERY_TASK_ALWAYS_EAGER=true
+export CELERY_RESULT_BACKEND=rpc://
+export MOCK_VIDEO=true
+PYTHONPATH=. venv/bin/python -m uvicorn app.main:app --port 8000 > server_debug.log 2>&1 &
+SERVER_PID=$!
+
+# 2. Wait for server to be ready
+echo "[INFO] Waiting for server (PID: $SERVER_PID) on port 8000..." | tee -a $LOG_FILE
+MAX_RETRIES=15
+READY=0
+for i in $(seq 1 $MAX_RETRIES); do
+    if curl -s http://localhost:8000/ > /dev/null; then
+        READY=1
+        break
+    fi
+    sleep 2
+done
+
+if [ $READY -eq 0 ]; then
+    echo "[ERROR] Server failed to start in time. Check server_debug.log" | tee -a $LOG_FILE
+    kill $SERVER_PID
+    exit 1
+fi
+echo "[INFO] Server is READY." | tee -a $LOG_FILE
+
+# 3. Prepare Test Data
+echo "[INFO] Preparing fresh test data..." | tee -a $LOG_FILE
+PREP_OUTPUT=$(PYTHONPATH=. venv/bin/python scripts/prepare_api_test.py)
+echo "$PREP_OUTPUT" >> $LOG_FILE
+
+export TEST_USER_ID=$(echo "$PREP_OUTPUT" | grep "RESULT:USER_ID=" | cut -d'=' -f2)
+export TEST_SESSION_ID=$(echo "$PREP_OUTPUT" | grep "RESULT:SESSION_ID=" | cut -d'=' -f2)
+
+if [ -z "$TEST_USER_ID" ] || [ -z "$TEST_SESSION_ID" ]; then
+    echo "[ERROR] Failed to prepare test data." | tee -a $LOG_FILE
+    kill $SERVER_PID
+    exit 1
+fi
+
+echo "[INFO] Test Data: User=$TEST_USER_ID, Session=$TEST_SESSION_ID" | tee -a $LOG_FILE
+
+# 4. Run Pytest
+echo "[INFO] Running API E2E Tests..." | tee -a $LOG_FILE
+PYTHONPATH=. venv/bin/python -m pytest tests/test_api_real_e2e.py -m "smoke and real_api" -s \
+  --junitxml=pytest_smoke.xml >> $LOG_FILE 2>&1
+TEST_EXIT_CODE=$?
+
+# 5. Cleanup
+echo "[INFO] Shutting down Server..." | tee -a $LOG_FILE
+kill $SERVER_PID
+
+echo "==========================================" | tee -a $LOG_FILE
+if [ $TEST_EXIT_CODE -eq 0 ]; then
+    echo "FINAL RESULT: ✅ ALL API TESTS PASSED" | tee -a $LOG_FILE
+else
+    echo "FINAL RESULT: ❌ SOME API TESTS FAILED (Code: $TEST_EXIT_CODE)" | tee -a $LOG_FILE
+fi
+echo "==========================================" | tee -a $LOG_FILE
+
+exit $TEST_EXIT_CODE

run_full_api_test.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+# Full API integration (CI-style): eager Celery + mock video + full HTTP suite.
+LOG_FILE="full_api_suite.log"
+REPORT_FILE="full_api_test_report.md"
+JSON_RESULTS="temp_suite_results.json"
+JUNIT="pytest_api_suite.xml"
+
+echo "=== Starting Full API Suite Test ($(date)) ===" >"$LOG_FILE"
+
+trap 'echo "[INFO] Cleaning up processes..."; kill $SERVER_PID 2>/dev/null; sleep 1' EXIT
+
+echo "[INFO] Starting Backend Server (EAGER + MOCK_VIDEO)..." | tee -a "$LOG_FILE"
+export ALLOW_TEST_BYPASS=true
+export LOG_LEVEL=info
+export CELERY_TASK_ALWAYS_EAGER=true
+export CELERY_RESULT_BACKEND=rpc://
+export MOCK_VIDEO=true
+PYTHONPATH=. venv/bin/python -m uvicorn app.main:app --port 8000 >server_debug.log 2>&1 &
+SERVER_PID=$!
+
+echo "[INFO] Waiting for server (PID: $SERVER_PID)..." | tee -a "$LOG_FILE"
+for i in {1..20}; do
+  if curl -s http://localhost:8000/ >/dev/null; then
+    echo "[INFO] Server is READY." | tee -a "$LOG_FILE"
+    break
+  fi
+  sleep 2
+done
+
+echo "[INFO] Preparing fresh test data..." | tee -a "$LOG_FILE"
+PREP_OUTPUT=$(PYTHONPATH=. venv/bin/python scripts/prepare_api_test.py)
+export TEST_USER_ID=$(echo "$PREP_OUTPUT" | grep "RESULT:USER_ID=" | cut -d'=' -f2)
+export TEST_SESSION_ID=$(echo "$PREP_OUTPUT" | grep "RESULT:SESSION_ID=" | cut -d'=' -f2)
+
+if [ -z "$TEST_USER_ID" ]; then
+  echo "[ERROR] Failed to prepare test data." | tee -a "$LOG_FILE"
+  exit 1
+fi
+
+echo "[INFO] Executing API tests (smoke + full suite)..." | tee -a "$LOG_FILE"
+PYTHONPATH=. venv/bin/python -m pytest tests/test_api_real_e2e.py tests/test_api_full_suite.py \
+  -m "real_api" -s --tb=short --junitxml="$JUNIT" >>"$LOG_FILE" 2>&1
+TEST_EXIT_CODE=$?
+
+echo "[INFO] Generating Markdown Report..." | tee -a "$LOG_FILE"
+if [ -f "$JSON_RESULTS" ]; then
+  PYTHONPATH=. venv/bin/python scripts/generate_report.py "$JSON_RESULTS" "$REPORT_FILE" "$JUNIT"
+else
+  echo "[WARN] $JSON_RESULTS not found" | tee -a "$LOG_FILE"
+fi
+
+echo "==========================================" | tee -a "$LOG_FILE"
+echo "DONE. Check $REPORT_FILE for results." | tee -a "$LOG_FILE"
+echo "==========================================" | tee -a "$LOG_FILE"
+
+exit $TEST_EXIT_CODE

scripts/benchmark_openrouter.py

@@ -0,0 +1,77 @@
+"""Benchmark several OpenRouter models (manual tool; not part of pytest)."""
+
+from __future__ import annotations
+
+import json
+import os
+import time
+
+import httpx
+from dotenv import load_dotenv
+
+_BACKEND_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+load_dotenv(os.path.join(_BACKEND_ROOT, ".env"))
+
+MODELS = [
+    "nvidia/nemotron-3-super-120b-a12b:free",
+    "meta-llama/llama-3.3-70b-instruct:free",
+    "openai/gpt-oss-120b:free",
+    "z-ai/glm-4.5-air:free",
+    "minimax/minimax-m2.5:free",
+    "google/gemma-4-26b-a4b-it:free",
+    "google/gemma-4-31b-it:free",
+]
+
+PROMPT = (
+    "Cho hình chữ nhật ABCD có AB bằng 5 và AD bằng 10. Gọi E là điểm nằm trong đoạn CD sao cho CE = 2ED. "
+    "Vẽ đoạn thẳng AE. Vẽ thêm P là điểm nằm trên đường thẳng BC sao cho BP = 2PC, tính chu vi tam giác PEA"
+)
+
+
+def main() -> None:
+    api_key = os.getenv("OPENROUTER_API_KEY_1") or os.getenv("OPENROUTER_API_KEY")
+    base_url = "https://openrouter.ai/api/v1/chat/completions"
+
+    if not api_key:
+        print("Missing OPENROUTER_API_KEY_1 or OPENROUTER_API_KEY in .env")
+        return
+
+    print("Benchmark OpenRouter models\nPrompt:", PROMPT, "\n")
+    results = []
+
+    for model in MODELS:
+        print(f"Calling {model}...", end="", flush=True)
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+            "HTTP-Referer": "https://mathsolver.io",
+            "X-Title": "MathSolver Benchmark Tool",
+        }
+        payload = {"model": model, "messages": [{"role": "user", "content": PROMPT}]}
+        start = time.time()
+        try:
+            with httpx.Client(timeout=120.0) as client:
+                r = client.post(base_url, headers=headers, json=payload)
+                r.raise_for_status()
+                data = r.json()
+                answer = data["choices"][0]["message"]["content"]
+                duration = time.time() - start
+                results.append(
+                    {"model": model, "duration": duration, "answer": answer, "status": "success"}
+                )
+                print(f" OK ({duration:.2f}s)")
+        except Exception as e:
+            duration = time.time() - start
+            results.append(
+                {"model": model, "duration": duration, "error": str(e), "status": "error"}
+            )
+            print(f" FAIL ({duration:.2f}s) {e}")
+
+    print("\n" + "=" * 80)
+    for res in results:
+        print(json.dumps(res, ensure_ascii=False, indent=2)[:2000])
+        print("-" * 40)
+
+
+if __name__ == "__main__":
+    main()

scripts/generate_report.py

@@ -0,0 +1,115 @@
+import json
+import os
+import sys
+import xml.etree.ElementTree as ET
+from datetime import datetime
+
+
+def _parse_junit_xml(path: str) -> dict:
+    """Summarize pytest junitxml (JUnit) file."""
+    out = {"tests": 0, "failures": 0, "errors": 0, "skipped": 0, "time": 0.0}
+    try:
+        tree = ET.parse(path)
+        root = tree.getroot()
+        nodes = [root] if root.tag == "testsuite" else list(root.iter("testsuite"))
+        for ts in nodes:
+            if ts.tag != "testsuite":
+                continue
+            out["tests"] += int(ts.attrib.get("tests", 0) or 0)
+            out["failures"] += int(ts.attrib.get("failures", 0) or 0)
+            out["errors"] += int(ts.attrib.get("errors", 0) or 0)
+            out["skipped"] += int(ts.attrib.get("skipped", 0) or 0)
+            out["time"] += float(ts.attrib.get("time", 0) or 0)
+    except Exception as e:
+        out["parse_error"] = str(e)
+    return out
+
+
+def generate_report(json_path: str, report_path: str, junit_path: str | None = None) -> None:
+    try:
+        with open(json_path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+
+        junit_summary = None
+        if junit_path and os.path.isfile(junit_path):
+            junit_summary = _parse_junit_xml(junit_path)
+
+        with open(report_path, "w", encoding="utf-8") as f:
+            f.write("# Báo cáo Kiểm thử tích hợp Backend (Integration Report)\n\n")
+            f.write(f"**Thời gian chạy:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
+            suite_ok = all(r.get("success", False) for r in data) if isinstance(data, list) else False
+            f.write(f"**API suite (JSON):** {'PASS' if suite_ok else 'FAIL'}\n")
+
+            if junit_summary and "parse_error" not in junit_summary:
+                j_ok = junit_summary["failures"] == 0 and junit_summary["errors"] == 0
+                f.write(
+                    f"**Pytest (JUnit):** {'PASS' if j_ok else 'FAIL'} — "
+                    f"tests={junit_summary['tests']}, failures={junit_summary['failures']}, "
+                    f"errors={junit_summary['errors']}, skipped={junit_summary['skipped']}, "
+                    f"time_s={junit_summary['time']:.2f}\n"
+                )
+            elif junit_summary and "parse_error" in junit_summary:
+                f.write(f"**Pytest (JUnit):** (could not parse: {junit_summary['parse_error']})\n")
+
+            f.write("\n")
+
+            f.write("| ID | Câu hỏi (Query) | Trạng thái | Thời gian (s) | Kết quả / Lỗi |\n")
+            f.write("| :--- | :--- | :--- | :--- | :--- |\n")
+            for r in data:
+                status = "PASS" if r.get("success") else "FAIL"
+                elapsed = f"{float(r.get('elapsed', 0) or 0):.2f}"
+                query = r.get("query", "-")
+
+                res = r.get("result", {})
+                if not isinstance(res, dict):
+                    res = {}
+
+                analysis = res.get("semantic_analysis", "-")
+                if not r.get("success"):
+                    analysis = f"**Lỗi:** {r.get('error', '-')}"
+
+                short_analysis = (analysis[:100] + "...") if len(str(analysis)) > 100 else analysis
+
+                f.write(f"| {r['id']} | {query} | {status} | {elapsed} | {short_analysis} |\n")
+
+            f.write("\n---\n**Chi tiết Output (DSL & Analysis):**\n")
+            for r in data:
+                if not r.get("success"):
+                    continue
+                res = r.get("result", {})
+                if not isinstance(res, dict):
+                    continue
+
+                f.write(f"\n### Case {r['id']}: {r.get('query')}\n")
+                f.write(f"**Semantic Analysis:**\n{res.get('semantic_analysis', '-')}\n\n")
+                f.write(f"**Geometry DSL:**\n```\n{res.get('geometry_dsl', '-')}\n```\n")
+
+                sol = res.get("solution")
+                if sol and isinstance(sol, dict):
+                    f.write("**Solution (v5.1):**\n")
+                    f.write(f"- **Answer:** {sol.get('answer', 'N/A')}\n")
+                    f.write("- **Steps:**\n")
+                    steps = sol.get("steps", [])
+                    if steps:
+                        for step in steps:
+                            f.write(f"  - {step}\n")
+                    else:
+                        f.write("  - (Không có bước giải cụ thể)\n")
+
+                    if sol.get("symbolic_expression"):
+                        f.write(f"- **Symbolic:** `{sol.get('symbolic_expression')}`\n")
+                    f.write("\n")
+
+        print(f"Report generated: {report_path}")
+    except Exception as e:
+        print(f"Error generating report: {e}")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        print(
+            "Usage: python generate_report.py <json_results> <report_output> [junit_xml_optional]"
+        )
+        sys.exit(1)
+    junit = sys.argv[3] if len(sys.argv) > 3 else None
+    generate_report(sys.argv[1], sys.argv[2], junit)

scripts/prepare_api_test.py

@@ -0,0 +1,38 @@
+import os
+import sys
+import uuid
+
+from dotenv import load_dotenv
+
+# Add parent dir to path to import app modules
+_BACKEND_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(_BACKEND_ROOT)
+load_dotenv(os.path.join(_BACKEND_ROOT, ".env"))
+
+from app.supabase_client import get_supabase
+
+# Default UUID matches historical dev DB; override with TEST_SUPABASE_USER_ID in .env
+_DEFAULT_TEST_USER = "8cd3adb0-7964-4575-949c-d0cadcd8b679"
+
+
+def prepare():
+    supabase = get_supabase()
+    user_id = os.environ.get("TEST_SUPABASE_USER_ID", _DEFAULT_TEST_USER).strip()
+    session_id = str(uuid.uuid4())
+    
+    print(f"Using test user (TEST_SUPABASE_USER_ID or default): {user_id}")
+    
+    print(f"Creating fresh test session: {session_id}")
+    # Insert session
+    supabase.table("sessions").insert({
+        "id": session_id,
+        "user_id": user_id,
+        "title": f"Fresh API Test {session_id[:8]}"
+    }).execute()
+    
+    # Return IDs for the test script
+    print(f"RESULT:USER_ID={user_id}")
+    print(f"RESULT:SESSION_ID={session_id}")
+
+if __name__ == "__main__":
+    prepare()

scripts/prewarm_models.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+"""
+Download and load all heavy models during Docker build (YOLO, PaddleOCR, Pix2Tex, agents).
+Fails the image build if initialization fails.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import sys
+
+# Ensure imports work when run as `python scripts/prewarm_models.py` from WORKDIR
+_APP_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if _APP_ROOT not in sys.path:
+    sys.path.insert(0, _APP_ROOT)
+
+os.chdir(_APP_ROOT)
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from app.runtime_env import apply_runtime_env_defaults
+
+apply_runtime_env_defaults()
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s %(name)s | %(message)s")
+
+logger = logging.getLogger("prewarm")
+
+
+def main() -> None:
+    from agents.orchestrator import Orchestrator
+
+    logger.info("Constructing Orchestrator (full agent + model load)...")
+    Orchestrator()
+    logger.info("Prewarm finished successfully.")
+
+
+if __name__ == "__main__":
+    main()

scripts/prewarm_ocr_worker.py

@@ -0,0 +1,37 @@
+#!/usr/bin/env python3
+"""Docker build: load OCR models only (no Orchestrator / no LLM). Used by Dockerfile.worker.ocr."""
+
+from __future__ import annotations
+
+import logging
+import os
+import sys
+
+_APP_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if _APP_ROOT not in sys.path:
+    sys.path.insert(0, _APP_ROOT)
+
+os.chdir(_APP_ROOT)
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from app.runtime_env import apply_runtime_env_defaults
+
+apply_runtime_env_defaults()
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s %(name)s | %(message)s")
+logger = logging.getLogger("prewarm_ocr_worker")
+
+
+def main() -> None:
+    from agents.ocr_agent import OCRAgent
+
+    logger.info("Loading OCRAgent(skip_llm_refinement=True)...")
+    OCRAgent(skip_llm_refinement=True)
+    logger.info("OCR worker prewarm finished successfully.")
+
+
+if __name__ == "__main__":
+    main()

scripts/run_real_integration.sh

@@ -0,0 +1,134 @@
+#!/usr/bin/env bash
+# Run backend integration tests. Usage:
+#   ./scripts/run_real_integration.sh           # profile ci (default)
+#   ./scripts/run_real_integration.sh ci
+#   ./scripts/run_real_integration.sh real      # heavy: workers, manim, OCR, full API suite
+set -euo pipefail
+
+PROFILE="${1:-ci}"
+ROOT="$(cd "$(dirname "$0")/.." && pwd)"
+cd "$ROOT"
+PY="${ROOT}/venv/bin/python"
+if [[ ! -x "$PY" ]]; then
+  PY="python3"
+fi
+
+export PYTHONPATH="$ROOT"
+LOG_FILE="${LOG_FILE:-integration_run.log}"
+JUNIT="${JUNIT:-pytest_integration.xml}"
+REPORT_MD="${REPORT_MD:-integration_report.md}"
+JSON_RESULTS="${JSON_RESULTS:-temp_suite_results.json}"
+
+log() { echo "[$(date '+%H:%M:%S')] $*" | tee -a "$LOG_FILE"; }
+
+log "Profile=$PROFILE working_dir=$ROOT"
+
+if [[ "$PROFILE" == "ci" ]]; then
+  export ALLOW_TEST_BYPASS="${ALLOW_TEST_BYPASS:-true}"
+  export LOG_LEVEL="${LOG_LEVEL:-info}"
+  export CELERY_TASK_ALWAYS_EAGER="${CELERY_TASK_ALWAYS_EAGER:-true}"
+  export CELERY_RESULT_BACKEND="${CELERY_RESULT_BACKEND:-rpc://}"
+  export MOCK_VIDEO="${MOCK_VIDEO:-true}"
+
+  set +e
+  log "Phase A: default pytest (unit / mocked; excludes real_* markers per pytest.ini)"
+  "$PY" -m pytest tests/ -q --tb=short -p no:cacheprovider 2>&1 | tee -a "$LOG_FILE"
+  P1=${PIPESTATUS[0]}
+  set -e
+
+  log "Starting uvicorn for API phase..."
+  "$PY" -m uvicorn app.main:app --port 8000 >>uvicorn_integration.log 2>&1 &
+  SERVER_PID=$!
+  trap 'kill "$SERVER_PID" 2>/dev/null || true' EXIT
+
+  for i in $(seq 1 25); do
+    if curl -sf "http://localhost:8000/" >/dev/null; then
+      log "API ready"
+      break
+    fi
+    sleep 2
+    if [[ "$i" -eq 25 ]]; then
+      log "ERROR: API did not start"
+      exit 1
+    fi
+  done
+
+  PREP="$("$PY" scripts/prepare_api_test.py)"
+  echo "$PREP" | tee -a "$LOG_FILE"
+  export TEST_USER_ID="$(echo "$PREP" | grep "RESULT:USER_ID=" | cut -d'=' -f2)"
+  export TEST_SESSION_ID="$(echo "$PREP" | grep "RESULT:SESSION_ID=" | cut -d'=' -f2)"
+  if [[ -z "${TEST_USER_ID:-}" || -z "${TEST_SESSION_ID:-}" ]]; then
+    log "ERROR: prepare_api_test did not emit USER_ID / SESSION_ID"
+    exit 1
+  fi
+
+  set +e
+  log "Phase B: API smoke + full suite (real_api)"
+  "$PY" -m pytest tests/test_api_real_e2e.py tests/test_api_full_suite.py \
+    -m "real_api" -s --tb=short --junitxml="$JUNIT" -p no:cacheprovider 2>&1 | tee -a "$LOG_FILE"
+  P2=${PIPESTATUS[0]}
+  set -e
+
+  if [[ -f "$JSON_RESULTS" ]]; then
+    log "Generating Markdown report"
+    "$PY" scripts/generate_report.py "$JSON_RESULTS" "$REPORT_MD" "$JUNIT"
+  else
+    log "WARN: $JSON_RESULTS missing (suite may have failed before write)"
+  fi
+
+  if [[ "$P1" -ne 0 || "$P2" -ne 0 ]]; then
+    log "FAIL: phase A exit=$P1 phase B exit=$P2"
+    exit 1
+  fi
+
+  log "Done CI profile. See $REPORT_MD and $LOG_FILE"
+  exit 0
+fi
+
+if [[ "$PROFILE" == "real" ]]; then
+  unset CELERY_TASK_ALWAYS_EAGER || true
+  export CELERY_TASK_ALWAYS_EAGER="${CELERY_TASK_ALWAYS_EAGER:-false}"
+  export MOCK_VIDEO="${MOCK_VIDEO:-false}"
+  export RUN_REAL_WORKER_OCR="${RUN_REAL_WORKER_OCR:-0}"
+  export RUN_REAL_WORKER_MANIM="${RUN_REAL_WORKER_MANIM:-0}"
+
+  log "Phase A: default pytest (fast)"
+  "$PY" -m pytest tests/ -q --tb=short -p no:cacheprovider 2>&1 | tee -a "$LOG_FILE"
+
+  log "Phase B: real agents + orchestrator smoke (requires OpenRouter keys)"
+  "$PY" -m pytest tests/integration/test_agents_real.py tests/integration/test_orchestrator_smoke.py \
+    -m "real_agents" -q --tb=short --junitxml="$JUNIT" -p no:cacheprovider 2>&1 | tee -a "$LOG_FILE" || true
+
+  if [[ "${RUN_REAL_WORKER_OCR:-0}" == "1" ]] || [[ "${RUN_REAL_WORKER_OCR:-0}" =~ ^(true|yes)$ ]]; then
+    log "Phase C: OCR worker task (RUN_REAL_WORKER_OCR enabled)"
+    "$PY" -m pytest tests/integration/test_worker_ocr_real.py \
+      -m "real_worker_ocr" -q --tb=short -p no:cacheprovider 2>&1 | tee -a "$LOG_FILE" || true
+  else
+    log "Skipping OCR worker (set RUN_REAL_WORKER_OCR=1 to enable)"
+  fi
+
+  if [[ "${RUN_REAL_WORKER_MANIM:-0}" == "1" ]]; then
+    log "Phase D: Manim + storage (RUN_REAL_WORKER_MANIM=1, MOCK_VIDEO=false)"
+    "$PY" -m pytest tests/integration/test_worker_manim_real.py -m "real_worker_manim" -s --tb=short \
+      -p no:cacheprovider 2>&1 | tee -a "$LOG_FILE" || true
+  else
+    log "Skipping Manim integration (set RUN_REAL_WORKER_MANIM=1 to enable)"
+  fi
+
+  log "Phase E: API real (expects TEST_BASE_URL or localhost:8000 with server already up)"
+  if curl -sf "http://localhost:8000/" >/dev/null 2>&1; then
+    PREP="$("$PY" scripts/prepare_api_test.py)"
+    export TEST_USER_ID="$(echo "$PREP" | grep "RESULT:USER_ID=" | cut -d'=' -f2)"
+    export TEST_SESSION_ID="$(echo "$PREP" | grep "RESULT:SESSION_ID=" | cut -d'=' -f2)"
+    "$PY" -m pytest tests/test_api_real_e2e.py tests/test_api_full_suite.py tests/test_api_metadata_real.py \
+      -m "real_api" -q --tb=short -p no:cacheprovider 2>&1 | tee -a "$LOG_FILE" || true
+  else
+    log "WARN: No server on :8000 — skip API real phase (start backend first)"
+  fi
+
+  log "Done REAL profile. Review $LOG_FILE"
+  exit 0
+fi
+
+echo "Unknown profile: $PROFILE (use ci or real)"
+exit 1

scripts/test_LLM.py

@@ -0,0 +1,142 @@
+import sys
+import os
+import time
+import asyncio
+import logging
+from typing import List, Dict, Any
+from dotenv import load_dotenv
+
+# Add the parent directory to sys.path to allow importing from 'app'
+# This assumes the script is inside 'backend/scripts' and we want to import from 'backend/app'
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from app.url_utils import openai_compatible_api_key
+from openai import AsyncOpenAI
+
+# Set up logger
+logging.basicConfig(level=logging.INFO, format="%(message)s")
+logger = logging.getLogger(__name__)
+
+# List of models to benchmark
+MODELS_TO_TEST = [
+    "nvidia/nemotron-3-super-120b-a12b:free",
+    "meta-llama/llama-3.3-70b-instruct:free",
+    "openai/gpt-oss-120b:free",
+    "z-ai/glm-4.5-air:free",
+    "minimax/minimax-m2.5:free",
+    "google/gemma-4-26b-a4b-it:free",
+    "google/gemma-4-31b-it:free",
+    "arcee-ai/trinity-large-preview:free",
+    "openai/gpt-oss-20b:free",
+    "nvidia/nemotron-3-nano-30b-a3b:free",
+    "nvidia/nemotron-nano-9b-v2:free",
+]
+
+DEFAULT_QUERY = "Giải hệ phương trình sau: x + y = 10, 2x - y = 2. Trả về kết quả cuối cùng x và y."
+
+async def test_model(client: AsyncOpenAI, model: str, query: str) -> Dict[str, Any]:
+    """Test a single model and return performance metrics."""
+    start_time = time.time()
+    result = {
+        "model": model,
+        "status": "success",
+        "duration": 0,
+        "content": "",
+        "error": None
+    }
+    
+    try:
+        response = await client.chat.completions.create(
+            model=model,
+            messages=[{"role": "user", "content": query}],
+            timeout=60.0
+        )
+        result["duration"] = time.time() - start_time
+        result["content"] = response.choices[0].message.content.strip()
+    except Exception as e:
+        result["status"] = "failed"
+        result["duration"] = time.time() - start_time
+        result["error"] = str(e)
+    
+    return result
+
+async def main():
+    # Load configuration from .env file inside backend directory
+    # If starting from root, backend/.env might be needed. If starting from backend/, .env is enough.
+    load_dotenv()
+    
+    # Try multiple common env keys for api key
+    api_key = os.getenv("OPENROUTER_API_KEY_1") or os.getenv("OPENROUTER_API_KEY")
+    
+    if not api_key:
+        logger.error("❌ Error: NO OPENROUTER_API_KEY found in environment variables.")
+        logger.info("Check your .env file in the backend directory.")
+        return
+
+    # Using the project's url_utils to maintain consistency with the main app
+    sanitized_key = openai_compatible_api_key(api_key)
+
+    client = AsyncOpenAI(
+        api_key=sanitized_key,
+        base_url="https://openrouter.ai/api/v1",
+        default_headers={
+            "HTTP-Referer": "https://mathsolver.ai",
+            "X-Title": "MathSolver LLM Benchmarker",
+        }
+    )
+
+    query = DEFAULT_QUERY
+    logger.info("=" * 80)
+    logger.info(f"🚀 LLM PERFORMANCE BENCHMARK")
+    logger.info(f"Query: {query}")
+    logger.info("=" * 80)
+    logger.info(f"Testing {len(MODELS_TO_TEST)} models sequentially with 30s delay...\n")
+
+    results = []
+    for i, model in enumerate(MODELS_TO_TEST):
+        if i > 0:
+            logger.info(f"⏳ Waiting 30s before testing next model...")
+            await asyncio.sleep(30)
+        
+        logger.info(f"[{i+1}/{len(MODELS_TO_TEST)}] Testing: {model}...")
+        res = await test_model(client, model, query)
+        results.append(res)
+        
+        # Immediate feedback
+        status_str = "✅ SUCCESS" if res["status"] == "success" else "❌ FAILED"
+        logger.info(f"   Status: {status_str} | Time: {res['duration']:.2f}s")
+
+    # Report Summary Table
+    logger.info("\n" + "=" * 80)
+    logger.info("📊 FINAL BENCHMARK SUMMARY")
+    logger.info("=" * 80)
+    header = f"{'MODEL':<45} | {'STATUS':<10} | {'TIME (s)':<10}"
+    logger.info(header)
+    logger.info("-" * len(header))
+    
+    for res in results:
+        status_str = "✅ SUCCESS" if res["status"] == "success" else "❌ FAILED"
+        duration_str = f"{res['duration']:.2f}s"
+        logger.info(f"{res['model']:<45} | {status_str:<10} | {duration_str:<10}")
+
+    logger.info("-" * len(header))
+
+    # Detailed report for successful ones
+    logger.info("\n📝 FULL RESPONSES:")
+    for res in results:
+        logger.info(f"\n{'='*20} [{res['model']}] {'='*20}")
+        if res["status"] == "success":
+            logger.info(res["content"])
+        else:
+            logger.info(f"❌ Error: {res['error']}")
+    
+    logger.info("\n" + "=" * 80)
+    logger.info(f"Benchmark finished.")
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(main())
+    except KeyboardInterrupt:
+        logger.info("\nBenchmark cancelled by user.")
+    except Exception as e:
+        logger.error(f"Unexpected error: {e}")

scripts/test_engine_direct.py

@@ -0,0 +1,36 @@
+import asyncio
+import os
+import json
+import logging
+import sys
+
+# Add root directory to path to import app and agents
+sys.path.append("/Volumes/WorkSpace/Project/MathSolver/backend")
+
+# Configure logging to stdout
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+
+from agents.orchestrator import Orchestrator
+
+async def main():
+    orch = Orchestrator()
+    text = "Vẽ tam giác đều cạnh 5."
+    job_id = "test_direct_equilateral"
+    
+    print(f"\n--- Testing Orchestrator Direct: {text} ---")
+    
+    async def status_cb(status):
+        print(f"  [STATUS] {status}")
+        
+    try:
+        result = await orch.run(text, job_id=job_id, status_callback=status_cb, request_video=False)
+        print("\n--- Final Result ---")
+        print(json.dumps(result, indent=2))
+    except Exception as e:
+        print(f"\n--- ERROR ---")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    asyncio.run(main())