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README.md

@@ -1,13 +1,67 @@
 ---
-title: Chatnew
-emoji: 🌍
-colorFrom: pink
-colorTo: yellow
+title: Maria Learning Service
+emoji: 📚
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 6.11.0
+sdk_version: 5.9.1
 app_file: app.py
 pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Maria Learning Service
+
+A FastAPI-based AI tutoring service powered by Qwen3-0.6B (float32, CPU-preloaded).
+
+## Endpoints
+
+| Endpoint   | Method | Description                                              |
+|------------|--------|----------------------------------------------------------|
+| `/dataset` | POST   | Pre-load FAISS index + metadata for a board/class/subject |
+| `/chat`    | POST   | Main tutoring endpoint (requires `/dataset` called first) |
+| `/health`  | GET    | Health check                                             |
+
+## Workflow
+
+**Always call `/dataset` before `/chat`.**  
+`/dataset` loads and caches the knowledge base for the requested board/class/subject.  
+`/chat` performs RAG against the cached dataset and runs inference.
+
+## Authentication
+
+Pass **one** of these headers per request:
+
+| Header | Description |
+|--------|-------------|
+| `auth_code` | Raw value whose SHA-256 must match `HASH_VALUE` secret |
+| `cf-turnstile-token` | Cloudflare Turnstile token verified against `CF_SECRET_KEY` secret |
+
+## Secrets Required
+
+Set these in your Space → Settings → Secrets:
+
+- `HASH_VALUE` — SHA-256 hex digest of your auth code
+- `CF_SECRET_KEY` — Cloudflare Turnstile secret key
+
+## `/dataset` Reference
+
+**Request**
+```json
+{
+  "board": "NCERT",
+  "class": "Class 1",
+  "subject": "English"
+}
+```
+
+**Response**
+```json
+{
+  "status": "ready",
+  "message": "Dataset Loaded"
+}
+```
+
+Repeated calls with the same `board/class/subject` are no-ops (served from cache).  
+Returns `412 Precondition Failed` from `/chat` if `/dataset` has not been called first.

app.py

@@ -0,0 +1,609 @@
+# ─────────────────────────────────────────────────────────────────────────────
+#  Maria Learning Service  |  app.py
+#  FastAPI + CPU (Qwen3-0.6B, int4 via bitsandbytes) + FAISS RAG + gTTS
+# ─────────────────────────────────────────────────────────────────────────────
+
+import asyncio
+import os
+import gc
+import json
+import base64
+import hashlib
+import logging
+import copy
+from io import BytesIO
+from typing import List, Any, Optional
+
+import httpx
+import numpy as np
+import pandas as pd
+import faiss
+import gradio as gr
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+from huggingface_hub import hf_hub_download
+from gtts import gTTS
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s  %(levelname)-8s  %(message)s",
+)
+log = logging.getLogger(__name__)
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  Config / Secrets
+# ─────────────────────────────────────────────────────────────────────────────
+HASH_VALUE    = os.environ.get("HASH_VALUE", "")
+CF_SECRET_KEY = os.environ.get("CF_SECRET_KEY", "")
+HF_REPO_ID    = "digifreely/Maria"
+LLM_MODEL_ID  = "Qwen/Qwen3-0.6B"                           # Qwen3 0.6B — CPU int4
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  Preload: model + tokenizer loaded once into CPU RAM at container start.
+#
+#  bitsandbytes 4-bit is CUDA-only; for CPU we load the 0.6B model in float32
+#  which is lightweight enough to reside in memory for the container lifetime.
+#  No per-request load/unload cycle — the model object is reused directly.
+# ─────────────────────────────────────────────────────────────────────────────
+_llm_tok   = None   # tokenizer
+_llm_model = None   # model — lives in CPU RAM for the container lifetime
+
+def _preload_model():
+    """Load Qwen3-0.6B tokenizer + model into CPU RAM at container start."""
+    global _llm_tok, _llm_model
+    import torch
+    from transformers import AutoTokenizer, AutoModelForCausalLM
+
+    log.info("Loading %s to CPU RAM…", LLM_MODEL_ID)
+    _llm_tok = AutoTokenizer.from_pretrained(LLM_MODEL_ID, trust_remote_code=True)
+    _llm_model = AutoModelForCausalLM.from_pretrained(
+        LLM_MODEL_ID,
+        torch_dtype=torch.float32,   # float32 for CPU compatibility
+        device_map="cpu",
+        trust_remote_code=True,
+    )
+    _llm_model.eval()
+    log.info("Model loaded on CPU — ready for inference.")
+
+# Trigger preload immediately when the module is imported
+_preload_model()
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  Embedding model  (CPU, loaded once per container lifetime)
+# ─────────────────────────────────────────────────────────────────────────────
+_emb_model = None
+
+def _get_emb_model(name: str = "sentence-transformers/all-MiniLM-L6-v2"):
+    global _emb_model
+    if _emb_model is None:
+        from sentence_transformers import SentenceTransformer
+        log.info("Loading embedding model: %s", name)
+        _emb_model = SentenceTransformer(name)
+    return _emb_model
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  Security helpers
+# ─────────────────────────────────────────────────────────────────────────────
+def _check_auth_code(code: str) -> bool:
+    if not HASH_VALUE:
+        return False
+    return hashlib.sha256(code.encode()).hexdigest() == HASH_VALUE
+
+
+async def _check_turnstile(token: str) -> bool:
+    if not CF_SECRET_KEY:
+        return False
+    try:
+        async with httpx.AsyncClient(timeout=8.0) as client:
+            resp = await client.post(
+                "https://challenges.cloudflare.com/turnstile/v0/siteverify",
+                data={"secret": CF_SECRET_KEY, "response": token},
+            )
+        return resp.json().get("success", False)
+    except Exception as exc:
+        log.error("Turnstile verification error: %s", exc)
+        return False
+
+
+async def _authenticate(request: Request) -> bool:
+    auth_code = request.headers.get("auth_code")
+    cf_token  = request.headers.get("cf-turnstile-token")
+    if auth_code:
+        return _check_auth_code(auth_code)
+    if cf_token:
+        return await _check_turnstile(cf_token)
+    return False
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  Change 3: Dataset cache — populated by /dataset, consumed by /chat
+# ─────────────────────────────────────────────────────────────────────────────
+# Key: (board, cls, subject)  →  (config, faiss_index, metadata)
+_dataset_cache: dict = {}
+
+
+def _dataset_key(board: str, cls: str, subject: str) -> tuple:
+    return (board.strip(), cls.strip(), subject.strip())
+
+
+def _load_dataset(board: str, cls: str, subject: str):
+    """Download config / FAISS index / metadata from HF Hub and return them."""
+    prefix = f"knowledgebase/{board}/{cls}/{subject}"
+    log.info("Fetching dataset: %s", prefix)
+
+    config_path = hf_hub_download(
+        repo_id=HF_REPO_ID,
+        filename=f"{prefix}/config.json",
+        repo_type="dataset",
+    )
+    faiss_path = hf_hub_download(
+        repo_id=HF_REPO_ID,
+        filename=f"{prefix}/faiss_index.bin",
+        repo_type="dataset",
+    )
+    meta_path = hf_hub_download(
+        repo_id=HF_REPO_ID,
+        filename=f"{prefix}/metadata.parquet",
+        repo_type="dataset",
+    )
+
+    with open(config_path) as fh:
+        config = json.load(fh)
+
+    index    = faiss.read_index(faiss_path)
+    metadata = pd.read_parquet(meta_path)
+    return config, index, metadata
+
+
+def _rag_search(
+    query: str,
+    config: dict,
+    index,
+    metadata: pd.DataFrame,
+    k: int = 3,
+) -> List[str]:
+    """Embed query, search FAISS, return top-k text chunks."""
+    emb_model_name = config.get(
+        "embedding_model", "sentence-transformers/all-MiniLM-L6-v2"
+    )
+    emb  = _get_emb_model(emb_model_name)
+    vec  = emb.encode([query], normalize_embeddings=True).astype(np.float32)
+    _, idxs = index.search(vec, k)
+
+    text_cols = ["text", "content", "chunk", "passage", "answer", "description"]
+    chunks: List[str] = []
+    for i in idxs[0]:
+        if 0 <= i < len(metadata):
+            row = metadata.iloc[i]
+            for col in text_cols:
+                if col in metadata.columns and pd.notna(row[col]):
+                    chunks.append(str(row[col])[:600])
+                    break
+    return chunks
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  LLM inference — uses the preloaded CPU model; no per-call load/unload.
+# ─────────────────────────────────────────────────────────────────────────────
+def _model_generate(system_prompt: str, user_prompt: str) -> str:
+    import torch
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user",   "content": user_prompt},
+    ]
+
+    text = _llm_tok.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+    )
+    inputs = _llm_tok([text], return_tensors="pt").to(_llm_model.device)
+
+    with torch.no_grad():
+        out_ids = _llm_model.generate(
+            **inputs,
+            max_new_tokens=150,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True,
+            repetition_penalty=1.1,
+            pad_token_id=_llm_tok.eos_token_id,
+        )
+
+    new_tokens = out_ids[0][inputs.input_ids.shape[1]:]
+    result     = _llm_tok.decode(new_tokens, skip_special_tokens=True).strip()
+
+    log.info("Inference complete. Output length: %d chars", len(result))
+    return result
+
+
+# CPU inference — direct reference (no ZeroGPU decorator)
+run_inference = _model_generate
+
+
+# ──────────────────────────────────────────────────��──────────────────────────
+#  Text-to-Speech
+# ─────────────────────────────────────────────────────────────────────────────
+def _tts_to_b64(text: str) -> str:
+    try:
+        tts = gTTS(text=text[:3000], lang="en", tld="co.uk", slow=False)
+        buf = BytesIO()
+        tts.write_to_fp(buf)
+        buf.seek(0)
+        return base64.b64encode(buf.read()).decode("utf-8")
+    except Exception as exc:
+        log.error("TTS error: %s", exc)
+        return ""
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  Prompt builder — trimmed for 150-token output budget (Qwen3-0.6B, CPU)
+# ─────────────────────────────────────────────────────────────────────────────
+def _build_system_prompt(lp: dict, rag_chunks: List[str]) -> str:
+    persona          = lp.get("teacher_persona", "A friendly and patient teacher")
+    student          = lp.get("student_name", "Student")
+    chat_history     = lp.get("chat_history", [])[-2:]      # last 2 turns only
+    scratchpad       = lp.get("scratchpad", [])[-1:]         # last 1 entry only
+    current_learning = lp.get("assessment_stages", {}).get("current_learning", [])
+
+    history_block = "\n".join(
+        f'S: {h.get("user_input","")}\nT: {h.get("system_output","")}'
+        for h in chat_history
+    ) or "None."
+
+    scratch_block = "\n".join(
+        f'[{s.get("chat_id","")}] {s.get("thought","")} | {s.get("action","")}'
+        for s in scratchpad
+    ) or "Empty."
+
+    rag_block = "\n---\n".join(rag_chunks) if rag_chunks else "No relevant content found."
+    cl_block  = json.dumps(current_learning, indent=2) if current_learning else "[]"
+
+    return f"""You are {persona} teaching {student}, aged 6–12. Use simple English. Be warm and brief.
+
+STUDENT: {student}
+LEARNING OBJECTIVES:
+{cl_block}
+
+KNOWLEDGE BASE:
+{rag_block}
+
+RECENT CHAT:
+{history_block}
+
+NOTES:
+{scratch_block}
+
+TASK: Classify intent, respond to the student, return ONLY valid JSON. Keep "response" under 60 words.
+
+INTENT RULES:
+"block" — rude/inappropriate message. First time: redirect kindly. Repeat: end gently.
+"questions" — off-topic question. Answer briefly from KB if found, then redirect.
+"curriculum" — on-topic. Follow stages in order: teach → re_teach → show_and_tell → assess.
+  teach: explain using KB. re_teach: ask one check question; re-explain if wrong.
+  show_and_tell: ask a similar question. assess: pass=complete, fail=Not_Complete (retry).
+"chitchat" — casual talk. Respond warmly, then bring up learning topic.
+
+OUTPUT — return ONLY this JSON:
+{{
+  "intent": "<block|questions|curriculum|chitchat>",
+  "response": "<reply to student, max 60 words>",
+  "stage_updates": [{{"topic":"<topic>","goal":"<goal>","teach":"<complete|Not_Complete>","re_teach":"<complete|Not_Complete>","show_and_tell":"<complete|Not_Complete>","assess":"<complete|Not_Complete>"}}],
+  "thought": "<brief internal reasoning>",
+  "action": "<teach|re_teach|show_and_tell|assess|answer|redirect|discourage|end|chitchat>",
+  "observation": "<brief student observation>"
+}}\
+"""
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  JSON parser  (robust — handles markdown fences, partial JSON, etc.)
+# ─────────────────────────────────────────────────────────────────────────────
+def _parse_llm_output(raw: str) -> dict:
+    text = raw.strip()
+
+    if "```" in text:
+        for part in text.split("```"):
+            part = part.strip()
+            if part.startswith("json"):
+                part = part[4:].strip()
+            try:
+                return json.loads(part)
+            except json.JSONDecodeError:
+                continue
+
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        pass
+
+    start = text.find("{")
+    end   = text.rfind("}") + 1
+    if start != -1 and end > start:
+        try:
+            return json.loads(text[start:end])
+        except json.JSONDecodeError:
+            pass
+
+    log.warning("Could not parse JSON from model output. Using raw text as response.")
+    return {
+        "intent":        "questions",
+        "response":      raw,
+        "stage_updates": [],
+        "thought":       "",
+        "action":        "answer",
+        "observation":   "json_parse_failed",
+    }
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  State updater
+# ─────────────────────────────────────────────────────────────────────────────
+def _apply_state_updates(
+    lp: dict,
+    parsed: dict,
+    user_msg: str,
+    ai_msg: str,
+) -> dict:
+    lp = copy.deepcopy(lp)
+
+    history = lp.setdefault("chat_history", [])
+    new_id  = (history[-1]["chat_id"] + 1) if history else 1
+    history.append({
+        "chat_id":       new_id,
+        "user_input":    user_msg,
+        "system_output": ai_msg,
+    })
+
+    scratch = lp.setdefault("scratchpad", [])
+    scratch.append({
+        "chat_id":      new_id,
+        "thought":      parsed.get("thought", ""),
+        "action":       parsed.get("action", ""),
+        "action_input": user_msg,
+        "observation":  parsed.get("observation", ""),
+    })
+
+    current_learning = lp.get("assessment_stages", {}).get("current_learning", [])
+    valid_statuses   = {"complete", "Not_Complete"}
+
+    for upd in parsed.get("stage_updates", []):
+        for item in current_learning:
+            if item.get("topic") == upd.get("topic"):
+                for obj in item.get("learning_objectives", []):
+                    if obj.get("goal") == upd.get("goal"):
+                        for stage in ("teach", "re_teach", "show_and_tell", "assess"):
+                            val = upd.get(stage)
+                            if val in valid_statuses:
+                                obj[stage] = val
+
+    lp.setdefault("assessment_stages", {})["current_learning"] = current_learning
+    return lp
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  FastAPI application
+# ─────────────────────────────────────────────────────────────────────────────
+_fastapi = FastAPI(
+    title="Maria Learning Service",
+    description="AI tutoring API powered by Qwen3-0.6B on CPU.",
+    version="1.1.0",
+    docs_url="/docs",
+    redoc_url="/redoc",
+)
+
+
+class ChatRequest(BaseModel):
+    learning_path: dict[str, Any]
+    query: dict[str, Any]
+
+
+class DatasetRequest(BaseModel):
+    board:   str
+    subject: str
+    # Pydantic alias so "class" (reserved word) maps to cls_name internally
+    class_name: str = ""
+
+    class Config:
+        # Allow the JSON field "class" to populate class_name via alias
+        populate_by_name = True
+
+    @classmethod
+    def model_validate_with_class(cls, data: dict):
+        data = dict(data)
+        if "class" in data:
+            data["class_name"] = data.pop("class")
+        return cls(**data)
+
+
+@_fastapi.get("/health", tags=["Utility"])
+async def health():
+    return {"status": "ok", "model": LLM_MODEL_ID}
+
+@_fastapi.get("/ping", tags=["Utility"])
+async def ping(request: Request):
+    """Health-check endpoint – wakes the Space if sleeping."""
+    if not await _authenticate(request):
+        raise HTTPException(status_code=403, detail="Forbidden")
+    return JSONResponse(content={"status": "alive"})
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  Change 3: /dataset endpoint
+# ─────────────────────────────────────────────────────────────────────────────
+@_fastapi.post("/dataset", tags=["Dataset"])
+async def dataset(request: Request):
+    """
+    Pre-load the FAISS index, config, and metadata for a given board/class/subject.
+    Must be called before /chat. Subsequent calls with the same key are no-ops (cached).
+
+    Request body:
+      { "board": "NCERT", "class": "Class 1", "subject": "English" }
+
+    Response:
+      { "status": "ready", "message": "Dataset Loaded" }
+    """
+    # ── Authentication ──────────────────────────────────────────────────────
+    if not await _authenticate(request):
+        raise HTTPException(status_code=403, detail="Forbidden")
+
+    # ── Parse body manually to handle "class" reserved keyword ─────────────
+    try:
+        body = await request.json()
+    except Exception:
+        raise HTTPException(status_code=422, detail="Invalid JSON body")
+
+    board   = str(body.get("board", "")).strip()
+    cls     = str(body.get("class", "")).strip()
+    subject = str(body.get("subject", "")).strip()
+
+    if not all([board, cls, subject]):
+        raise HTTPException(
+            status_code=422,
+            detail="Request body must contain board, class, and subject",
+        )
+
+    key = _dataset_key(board, cls, subject)
+
+    # ── Return immediately if already cached ────────────────────────────────
+    if key in _dataset_cache:
+        log.info("Dataset cache hit: %s", key)
+        return JSONResponse({"status": "ready", "message": "Dataset Loaded"})
+
+    # ── Load and cache — run blocking HF I/O in a thread pool so the event
+    #    loop is not frozen, but we still await completion before responding. ──
+    try:
+        config, faiss_index, metadata = await asyncio.to_thread(
+            _load_dataset, board, cls, subject
+        )
+        _dataset_cache[key] = (config, faiss_index, metadata)
+        log.info("Dataset cached for key: %s", key)
+    except Exception as exc:
+        log.error("Dataset load error: %s", exc)
+        raise HTTPException(
+            status_code=500,
+            detail=f"Could not load dataset for {board}/{cls}/{subject}: {exc}",
+        )
+
+    return JSONResponse({"status": "ready", "message": "Dataset Loaded"})
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  /chat endpoint — Change 4: uses dataset preloaded via /dataset
+# ─────────────────────────────────────────────────────────────────────────────
+@_fastapi.post("/chat", tags=["Tutor"])
+async def chat(request: Request, body: ChatRequest):
+    # ── 1. Authentication ───────────────────────────────────────────────────
+    if not await _authenticate(request):
+        raise HTTPException(status_code=403, detail="Forbidden")
+
+    # ── 2. Validate request body ────────────────────────────────────────────
+    lp  = body.learning_path
+    msg = body.query.get("request_message", "").strip()
+    if not msg:
+        raise HTTPException(status_code=422, detail="request_message must not be empty")
+
+    board   = lp.get("board", "").strip()
+    cls     = lp.get("class", "").strip()
+    subject = lp.get("subject", "").strip()
+
+    if not all([board, cls, subject]):
+        raise HTTPException(
+            status_code=422,
+            detail="learning_path must contain board, class, and subject",
+        )
+
+    # ── 3. Change 4: Retrieve dataset from cache (must call /dataset first) ─
+    key = _dataset_key(board, cls, subject)
+    if key not in _dataset_cache:
+        raise HTTPException(
+            status_code=412,
+            detail=(
+                f"Dataset for {board}/{cls}/{subject} is not loaded. "
+                "Please call POST /dataset first."
+            ),
+        )
+    config, faiss_index, metadata = _dataset_cache[key]
+
+    # ── 4. RAG retrieval ────────────────────────────────────────────────────
+    try:
+        rag_chunks = _rag_search(msg, config, faiss_index, metadata)
+    except Exception as exc:
+        log.warning("RAG search failed (%s) — continuing without context", exc)
+        rag_chunks = []
+
+    # ── 5. Build prompt and run LLM (Change 2: only CPU→GPU move happens here)
+    system_prompt = _build_system_prompt(lp, rag_chunks)
+    user_prompt   = f"Student: {msg}"
+
+    try:
+        raw_output = run_inference(system_prompt, user_prompt)
+    except Exception as exc:
+        log.error("Inference error: %s", exc)
+        raise HTTPException(status_code=500, detail=f"Inference failed: {exc}")
+
+    # ── 6. Parse structured output ──────────────────────────────────────────
+    parsed  = _parse_llm_output(raw_output)
+    ai_text = parsed.get("response", raw_output).strip()
+
+    # ── 7. Text-to-speech ───────────────────────────────────────────────────
+    audio_b64 = _tts_to_b64(ai_text)
+
+    # ── 8. Update learning path state ───────────────────────────────────��───
+    updated_lp = _apply_state_updates(lp, parsed, msg, ai_text)
+
+    # ── 9. Return response ──────────────────────────────────────────────────
+    return JSONResponse({
+        "learning_path": updated_lp,
+        "query": {
+            "response_message": {
+                "text":           ai_text,
+                "visual":         "No",
+                "visual_content": "",
+                "audio_output":   audio_b64,
+            }
+        },
+    })
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  Gradio shim
+# ─────────────────────────────────────────────────────────────────────────────
+with gr.Blocks(title="Maria Learning Service") as _gradio_ui:
+    gr.Markdown(
+        """
+        ## Maria Learning Service
+        This Space exposes a **REST API** — it is not a chat UI.
+
+        | Endpoint  | Method | Description                        |
+        |-----------|--------|------------------------------------|
+        | `/dataset`| POST   | Pre-load dataset (call before chat)|
+        | `/chat`   | POST   | Main tutoring endpoint             |
+        | `/health` | GET    | Health check                       |
+        | `/docs`   | GET    | Swagger UI                         |
+
+        Authenticate via `auth_code` header or `cf-turnstile-token` header.
+        """
+    )
+
+# Mount Gradio UI at /ui — keeps FastAPI routes at root level
+app = gr.mount_gradio_app(_fastapi, _gradio_ui, path="/ui")
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+#  Entry point
+# ─────────────────────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(
+        "app:app",
+        host="0.0.0.0",
+        port=7860,
+        log_level="info",
+        workers=1,          # Single worker — shared in-memory model object
+    )

requirements.txt

@@ -0,0 +1,27 @@
+# ── Web framework ─────────────────────────────────────────────────────────────
+fastapi==0.115.6
+uvicorn[standard]==0.32.1
+pydantic==2.10.3
+python-multipart==0.0.20
+httpx==0.28.1
+
+# ── HuggingFace ecosystem ─────────────────────────────────────────────────────
+huggingface-hub>=0.27.0
+transformers>=4.50.0
+tokenizers>=0.21.0
+safetensors>=0.5.0
+accelerate>=1.3.0
+
+# ── Embeddings ────────────────────────────────────────────────────────────────
+sentence-transformers>=3.3.0
+
+# ── Vector search ─────────────────────────────────────────────────────────────
+faiss-cpu>=1.9.0
+
+# ── Data ──────────────────────────────────────────────────────────────────────
+pandas>=2.2.0
+pyarrow>=14.0.0
+numpy>=1.26.0,<2.0.0
+
+# ── Audio ─────────────────────────────────────────────────────────────────────
+gTTS>=2.5.0