Delete app.py

app.py

@@ -1,543 +0,0 @@
-"""
-groundlens — Geometric LLM Hallucination Detection Demo
-
-Plain-language interface: paste a question and the AI's answer,
-optionally upload context (PDF, Excel, or plain text).
-Compares groundlens (embedding geometry) vs Vectara HHEM-2.1-Open.
-
-Models load once at module level to avoid cold-start on Space wake.
-"""
-
-import logging
-import time
-import tempfile
-import os
-
-import gradio as gr
-from groundlens import compute_sgi, compute_dgi
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# FILE EXTRACTION — PDF and Excel support
-# ─────────────────────────────────────────────────────────────────────────────
-
-def extract_pdf_text(file_path: str, max_chars: int = 8000) -> str:
-    """Extract text from a PDF file."""
-    try:
-        import pdfplumber
-        text_parts = []
-        with pdfplumber.open(file_path) as pdf:
-            for page in pdf.pages[:20]:  # limit to 20 pages
-                page_text = page.extract_text()
-                if page_text:
-                    text_parts.append(page_text)
-        full_text = "\n\n".join(text_parts)
-        return full_text[:max_chars] if len(full_text) > max_chars else full_text
-    except Exception as e:
-        return f"[Could not read PDF: {e}]"
-
-
-def extract_excel_text(file_path: str, max_chars: int = 8000) -> str:
-    """Extract text from an Excel file."""
-    try:
-        import openpyxl
-        wb = openpyxl.load_workbook(file_path, data_only=True)
-        text_parts = []
-        for sheet_name in wb.sheetnames[:5]:  # limit to 5 sheets
-            ws = wb[sheet_name]
-            text_parts.append(f"--- {sheet_name} ---")
-            for row in ws.iter_rows(max_row=200, values_only=True):
-                cells = [str(c) if c is not None else "" for c in row]
-                line = " | ".join(cells).strip()
-                if line and line != " | ".join([""] * len(cells)):
-                    text_parts.append(line)
-        full_text = "\n".join(text_parts)
-        return full_text[:max_chars] if len(full_text) > max_chars else full_text
-    except Exception as e:
-        return f"[Could not read Excel file: {e}]"
-
-
-def process_uploaded_file(file) -> str:
-    """Extract text from an uploaded file (PDF or Excel)."""
-    if file is None:
-        return ""
-
-    file_path = file.name if hasattr(file, 'name') else str(file)
-    ext = os.path.splitext(file_path)[1].lower()
-
-    if ext == ".pdf":
-        return extract_pdf_text(file_path)
-    elif ext in (".xlsx", ".xls"):
-        return extract_excel_text(file_path)
-    elif ext in (".txt", ".md", ".csv"):
-        try:
-            with open(file_path, "r", encoding="utf-8", errors="replace") as f:
-                text = f.read(8000)
-            return text
-        except Exception as e:
-            return f"[Could not read file: {e}]"
-    else:
-        return f"[Unsupported file type: {ext}. Use PDF, Excel, TXT, or CSV.]"
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# HHEM-2.1-Open — baseline comparison
-# ─────────────────────────────────────────────────────────────────────────────
-
-logger.info("Loading HHEM-2.1-Open...")
-from transformers import AutoModelForSequenceClassification
-
-_hhem = AutoModelForSequenceClassification.from_pretrained(
-    "vectara/hallucination_evaluation_model",
-    trust_remote_code=True,
-)
-logger.info("HHEM loaded.")
-
-# Warm up groundlens embedding model
-logger.info("Warming up groundlens...")
-compute_dgi(question="warmup", response="warmup")
-logger.info("groundlens ready.")
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# SCORING
-# ─────────────────────────────────────────────────────────────────────────────
-
-def score_groundlens(question: str, response: str, context: str) -> dict:
-    start = time.perf_counter()
-    has_context = bool(context.strip())
-
-    if has_context:
-        result = compute_sgi(
-            question=question,
-            context=context,
-            response=response,
-        )
-        method = "SGI (with context)"
-        raw_score = result.value
-        grounded = not result.flagged
-        threshold = 0.95
-        mode_note = (
-            "Measured how much the AI's answer used your source document "
-            "vs. just rephrasing the question."
-        )
-    else:
-        result = compute_dgi(
-            question=question,
-            response=response,
-        )
-        method = "DGI (without context)"
-        raw_score = result.value
-        grounded = not result.flagged
-        threshold = 0.30
-        mode_note = (
-            "Measured whether the AI's answer follows patterns typical "
-            "of grounded, factual responses."
-        )
-
-    elapsed_ms = (time.perf_counter() - start) * 1000
-
-    return {
-        "method": method,
-        "raw_score": round(raw_score, 4),
-        "grounded": grounded,
-        "threshold": threshold,
-        "elapsed_ms": round(elapsed_ms, 1),
-        "mode_note": mode_note,
-    }
-
-
-def score_hhem(question: str, response: str, context: str) -> dict:
-    has_context = bool(context.strip())
-    premise = (
-        f"{context.strip()}\n\n{question}".strip()
-        if has_context
-        else question
-    )
-    if len(premise) > 1800:
-        premise = premise[:1800]
-
-    start = time.perf_counter()
-    scores = _hhem.predict([(premise, response)])
-    raw_score = float(scores[0])
-    elapsed_ms = (time.perf_counter() - start) * 1000
-
-    return {
-        "method": "HHEM-2.1-Open",
-        "raw_score": round(raw_score, 4),
-        "grounded": raw_score >= 0.5,
-        "elapsed_ms": round(elapsed_ms, 1),
-        "label": "consistent" if raw_score >= 0.5 else "hallucinated",
-    }
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# MAIN COMPARISON
-# ─────────────────────────────────────────────────────────────────────────────
-
-def run_comparison(
-    question: str, context_text: str, file_upload, response: str
-) -> tuple[str, str, str]:
-
-    if not question.strip():
-        return "⚠️ Enter the question you asked the AI.", "", ""
-    if not response.strip():
-        return "⚠️ Enter the AI's response.", "", ""
-
-    # Merge context: typed text + uploaded file
-    context_parts = []
-    if context_text and context_text.strip():
-        context_parts.append(context_text.strip())
-    if file_upload is not None:
-        extracted = process_uploaded_file(file_upload)
-        if extracted and not extracted.startswith("["):
-            context_parts.append(extracted)
-        elif extracted.startswith("["):
-            return f"⚠️ {extracted}", "", ""
-    context = "\n\n".join(context_parts)
-
-    gl = score_groundlens(question, response, context)
-    hhem = score_hhem(question, response, context)
-
-    # groundlens result
-    if gl["grounded"]:
-        gl_verdict = "🟢 Looks grounded"
-        gl_explain = "The AI's answer appears to be based on real information."
-    else:
-        gl_verdict = "🔴 Possible hallucination"
-        gl_explain = "The AI's answer shows signs of being fabricated or not grounded in the source."
-
-    gl_md = f"""### groundlens
-
-**{gl_verdict}**
-
-{gl_explain}
-
-| | |
-|---|---|
-| **Method** | {gl["method"]} |
-| **Score** | {gl["raw_score"]} (threshold: {gl["threshold"]}) |
-| **Time** | {gl["elapsed_ms"]} ms |
-
-*{gl["mode_note"]}*"""
-
-    # HHEM result
-    if hhem["grounded"]:
-        hhem_verdict = "🟢 Looks consistent"
-        hhem_explain = "The classifier considers this answer consistent with the input."
-    else:
-        hhem_verdict = "🔴 Possible hallucination"
-        hhem_explain = "The classifier flagged this answer as potentially hallucinated."
-
-    hhem_md = f"""### Vectara HHEM-2.1-Open
-
-**{hhem_verdict}**
-
-{hhem_explain}
-
-| | |
-|---|---|
-| **Method** | {hhem["method"]} |
-| **Score** | {hhem["raw_score"]} ({hhem["label"]}) |
-| **Time** | {hhem["elapsed_ms"]} ms |
-
-*Fine-tuned flan-T5 classifier.*"""
-
-    # Agreement
-    agree = gl["grounded"] == hhem["grounded"]
-    if agree and gl["grounded"]:
-        agreement_md = "### 🔵 Both methods agree: the answer looks reliable."
-    elif agree and not gl["grounded"]:
-        agreement_md = "### 🔴 Both methods agree: this answer is likely hallucinated."
-    else:
-        agreement_md = """### 🟠 The two methods disagree.
-
-This often happens with **subtle factual errors** — the answer sounds right and
-uses the correct vocabulary, but gets specific facts wrong. Embedding geometry
-(groundlens) measures the shape of the answer; the classifier (HHEM) evaluates
-its content differently. When they disagree, it's worth checking the facts manually.
-
-[Learn more about hallucination types →](https://docs.groundlens.dev/theory/hallucination-taxonomy/)"""
-
-    return gl_md, hhem_md, agreement_md
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# EXAMPLES
-# ─────────────────────────────────────────────────────────────────────────────
-
-EXAMPLES = [
-    [
-        "What does the water damage policy cover?",
-        "Coverage includes burst pipes and sudden appliance failure up to "
-        "$50,000. Flood damage requires a separate NFIP policy. "
-        "Deductible is $1,500 per occurrence.",
-        "The policy covers burst pipes and sudden appliance failure up to "
-        "$50,000 per occurrence, with a $1,500 deductible.",
-    ],
-    [
-        "What does the water damage policy cover?",
-        "Coverage includes burst pipes and sudden appliance failure up to "
-        "$50,000. Flood damage requires a separate NFIP policy. "
-        "Deductible is $1,500 per occurrence.",
-        "The policy covers all water damage including floods "
-        "with no deductible required.",
-    ],
-    [
-        "What causes seasons on Earth?",
-        "",
-        "Seasons are caused by Earth's 23.5-degree axial tilt, which "
-        "changes how directly sunlight hits each hemisphere.",
-    ],
-    [
-        "What causes seasons on Earth?",
-        "",
-        "Seasons are regulated by the Atmospheric Regulation Committee, "
-        "a UN body established in 1952 that adjusts global temperature "
-        "through orbital satellites.",
-    ],
-]
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# THEME — dark, matching groundlens.dev
-# ─────────────────────────────────────────────────────────────────────────────
-
-theme = gr.themes.Base(
-    primary_hue=gr.themes.Color(
-        c50="#fff7ed",
-        c100="#ffedd5",
-        c200="#fed7aa",
-        c300="#fdba74",
-        c400="#fb923c",
-        c500="#fc7604",
-        c600="#ea580c",
-        c700="#c2410c",
-        c800="#9a3412",
-        c900="#7c2d12",
-        c950="#431407",
-    ),
-    secondary_hue="slate",
-    neutral_hue="slate",
-    font=gr.themes.GoogleFont("Inter"),
-    font_mono=gr.themes.GoogleFont("JetBrains Mono"),
-    text_size=gr.themes.sizes.text_lg,
-    radius_size=gr.themes.sizes.radius_md,
-).set(
-    body_background_fill="#0a0a0a",
-    body_background_fill_dark="#0a0a0a",
-    body_text_color="#e2e8f0",
-    body_text_color_dark="#e2e8f0",
-    body_text_size="1rem",
-    block_background_fill="#141414",
-    block_background_fill_dark="#141414",
-    block_border_color="#1e293b",
-    block_border_color_dark="#1e293b",
-    block_label_text_color="#94a3b8",
-    block_label_text_color_dark="#94a3b8",
-    block_label_text_size="0.95rem",
-    block_title_text_color="#e2e8f0",
-    block_title_text_color_dark="#e2e8f0",
-    input_background_fill="#1e1e1e",
-    input_background_fill_dark="#1e1e1e",
-    input_border_color="#334155",
-    input_border_color_dark="#334155",
-    input_text_size="1rem",
-    input_placeholder_color="#64748b",
-    input_placeholder_color_dark="#64748b",
-    button_primary_background_fill="#fc7604",
-    button_primary_background_fill_dark="#fc7604",
-    button_primary_background_fill_hover="#fb923c",
-    button_primary_background_fill_hover_dark="#fb923c",
-    button_primary_text_color="#0a0a0a",
-    button_primary_text_color_dark="#0a0a0a",
-    button_large_text_size="1.1rem",
-    border_color_primary="#fc7604",
-    border_color_primary_dark="#fc7604",
-)
-
-
-# ─────────────────────────────────────────────────────────────────────────────
-# INTERFACE
-# ─────────────────────────────────────────────────────────────────────────────
-
-css = """
-.gradio-container {
-    max-width: 1200px !important;
-    margin: 0 auto !important;
-    padding: 1.5rem !important;
-}
-h1 { color: #fc7604 !important; font-size: 2.2rem !important; font-weight: 700 !important; margin-bottom: 0.2rem !important; }
-h3 { font-size: 1.15rem !important; }
-.subtitle { color: #94a3b8 !important; font-size: 1.1rem !important; margin-top: 0 !important; }
-a { color: #fd9a42 !important; }
-a:hover { color: #fec08a !important; }
-.step-label { color: #fc7604; font-weight: 600; font-size: 1.05rem; }
-.links-bar { font-size: 0.9rem; color: #64748b; margin-top: 0.5rem; }
-.links-bar a { color: #64748b !important; }
-.links-bar a:hover { color: #fd9a42 !important; }
-footer { display: none !important; }
-
-/* Unified context box */
-.context-box {
-    border: 1px solid #334155 !important;
-    border-radius: 8px !important;
-    padding: 1rem !important;
-    background: #141414 !important;
-}
-.context-box .block {
-    border: none !important;
-    background: transparent !important;
-    padding: 0 !important;
-    box-shadow: none !important;
-}
-.context-box .wrap {
-    gap: 0.75rem !important;
-}
-.context-box textarea {
-    background: #1e1e1e !important;
-    border: 1px solid #334155 !important;
-    border-radius: 6px !important;
-}
-.context-divider {
-    text-align: center;
-    color: #64748b !important;
-    font-size: 0.85rem !important;
-    margin: 0.25rem 0 !important;
-    padding: 0 !important;
-}
-.context-divider p { margin: 0 !important; }
-.context-box .file-upload,
-.context-box .upload-button {
-    border: 1px dashed #475569 !important;
-    border-radius: 6px !important;
-    background: #1a1a1a !important;
-}
-.context-box .file-preview {
-    border: none !important;
-}
-
-@media (max-width: 768px) {
-    .gradio-container { padding: 0.75rem !important; }
-    h1 { font-size: 1.6rem !important; }
-}
-"""
-
-with gr.Blocks(
-    title="groundlens — Check if your AI is hallucinating",
-    theme=theme,
-    css=css,
-) as demo:
-
-    gr.Markdown("""
-# groundlens
-
-<p class="subtitle">Check if an AI gave you a real answer or made something up.</p>
-""")
-
-    gr.Markdown("""
-You asked an AI a question and got an answer. Was it real or hallucinated?
-Paste both below and we'll check using two independent methods: **groundlens**
-(geometric analysis) and **Vectara HHEM** (neural classifier).
-""")
-
-    gr.Markdown("""<p class="links-bar">
-<a href="https://github.com/groundlens-dev/groundlens">GitHub</a> ·
-<a href="https://docs.groundlens.dev">Docs</a> ·
-<a href="https://pypi.org/project/groundlens/">PyPI</a> ·
-<a href="https://arxiv.org/abs/2512.13771">SGI paper</a> ·
-<a href="https://arxiv.org/pdf/2602.13224v3">Taxonomy</a> ·
-<a href="https://arxiv.org/abs/2603.13259">Mechanistic paper</a>
-</p>""")
-
-    # ── Step 1: Question ──
-    gr.Markdown('<p class="step-label">1. What did you ask the AI?</p>')
-    q_in = gr.Textbox(
-        show_label=False,
-        placeholder="e.g. What does our insurance policy cover for water damage?",
-        lines=2,
-    )
-
-    # ── Step 2: Context ──
-    gr.Markdown(
-        '<p class="step-label">2. Did you give the AI any source material? (optional)</p>'
-    )
-    gr.Markdown(
-        "If you gave the AI a document, a webpage, an Excel file, or any reference "
-        "material to base its answer on, paste the text here or upload the file. "
-        "If you just asked a question with no source, skip this step.",
-    )
-
-    with gr.Group(elem_classes=["context-box"]):
-        ctx_in = gr.Textbox(
-            show_label=False,
-            placeholder="Paste source text here...",
-            lines=4,
-            container=False,
-        )
-        gr.Markdown("— or —", elem_classes=["context-divider"])
-        file_in = gr.File(
-            label="Upload a file (PDF, Excel, CSV, TXT — max 20 pages / 200 rows)",
-            file_types=[".pdf", ".xlsx", ".xls", ".csv", ".txt"],
-            file_count="single",
-            height=60,
-        )
-
-    # ── Step 3: Response ──
-    gr.Markdown('<p class="step-label">3. What did the AI answer?</p>')
-    r_in = gr.Textbox(
-        show_label=False,
-        placeholder="Paste the AI's response here...",
-        lines=4,
-    )
-
-    # ── Evaluate button ──
-    run_btn = gr.Button(
-        "Check for hallucination",
-        variant="primary",
-        size="lg",
-    )
-
-    # ── Results ──
-    with gr.Row(equal_height=True):
-        gl_out = gr.Markdown()
-        hhem_out = gr.Markdown()
-
-    agreement_out = gr.Markdown()
-
-    # ── Examples ──
-    gr.Markdown("---")
-    gr.Markdown("### Try an example")
-
-    gr.Examples(
-        examples=EXAMPLES,
-        inputs=[q_in, ctx_in, r_in],
-        label="",
-    )
-
-    # ── Footer ──
-    gr.Markdown("""
----
-
-<p style="color:#475569; font-size:0.85rem; text-align:center;">
-<strong>groundlens</strong> is open source (MIT). Built by
-<a href="https://jmarin.info" style="color:#64748b !important;">Javier Marin</a>.
-This demo runs the same library available via <code>pip install groundlens</code>.<br>
-groundlens is verification triage, not a truth oracle. It tells you which answers
-deserve trust and which need a closer look.
-</p>
-""")
-
-    # ── Event binding ──
-    run_btn.click(
-        fn=run_comparison,
-        inputs=[q_in, ctx_in, file_in, r_in],
-        outputs=[gl_out, hhem_out, agreement_out],
-    )
-
-
-if __name__ == "__main__":
-    demo.launch()