Drop taxonomy label 11; renumber 'None of the above' from 12 to 11

api.py

@@ -2,13 +2,11 @@ import json
 import os
 import threading
 import time
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, Optional
 
 import pandas as pd
 from fastapi import BackgroundTasks, FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.middleware.gzip import GZipMiddleware
-from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
 
@@ -30,10 +28,6 @@ progress_lock = threading.Lock()
 # Initialize FastAPI app
 app = FastAPI(title="Wiki Revision Classifier API")
 
-# Compress JSON responses larger than 1 KB. Big win for /demo and /results
-# (multi-MB JSON payloads shrink ~70% on the wire).
-app.add_middleware(GZipMiddleware, minimum_size=1024)
-
 # Enable CORS
 app.add_middleware(
     CORSMiddleware,
@@ -52,13 +46,12 @@ async def add_cache_control_headers(request: Request, call_next):
     response.headers["Expires"] = "0"
     return response
 
-# Prepare backend file directory.
-# Mounted at /files so it doesn't collide with CRA's /static asset paths.
+# Prepare static directory
 os.makedirs("visualizations", exist_ok=True)
 app.mount(
-    "/files",
+    "/static",
     StaticFiles(directory="visualizations"),
-    name="files"
+    name="static"
 )
 
 # Pydantic models
@@ -115,7 +108,7 @@ async def prepare_article(payload: PreparePayload) -> Dict[str, Any]:
 
     return {
         "previewRows": preview,
-        "csvUrl": f"/files/{csv_filename}",
+        "csvUrl": f"/static/{csv_filename}",
         "diffs": diffs,
         "revisionCount": revision_count,
     }
@@ -126,7 +119,7 @@ async def classify_article(payload: ClassifyPayload, background_tasks: Backgroun
     Start classification in the background and return immediately.
     Uses the flexible-linker pipeline + OpenAI Batch API (single batch).
     """
-    api_key = payload.api_key
+    os.environ["OPENAI_API_KEY"] = payload.api_key
     model_name = payload.model or "gpt-5-mini"
 
     df, title = fetch_revision_history(payload.article)
@@ -165,7 +158,6 @@ async def classify_article(payload: ClassifyPayload, background_tasks: Backgroun
     background_tasks.add_task(
         process_classification,
         model_name=model_name,
-        api_key=api_key,
         prompts=prompts,
         df=prompted_df,
         title=title,
@@ -224,7 +216,7 @@ def compute_grouped_idx(results_df: pd.DataFrame) -> pd.Series:
     return pd.Series(out, index=results_df.index, dtype=int)
 
 
-def process_classification(model_name, api_key, prompts, df, title, taxonomy):
+def process_classification(model_name, prompts, df, title, taxonomy):
     """Run the OpenAI batch classification in the background."""
     safe_title = title.replace(" ", "_")
     total = len(prompts)
@@ -247,7 +239,7 @@ def process_classification(model_name, api_key, prompts, df, title, taxonomy):
         with progress_lock:
             progress_map.setdefault(safe_title, {})["stage"] = "submitting"
 
-        model = GPT(model_name=model_name, api_key=api_key)
+        model = GPT(model_name=model_name)
         raw_responses = model.predict(prompts, status_callback=_update_batch_status)
 
         with progress_lock:
@@ -283,29 +275,8 @@ def process_classification(model_name, api_key, prompts, df, title, taxonomy):
         result_path = os.path.join("visualizations", result_filename)
         results_df.to_csv(result_path, index=False)
 
-        # Stash the full DataFrame so /summarize can look up Explanations
-        # for any (label, section, period) cell the user clicks.
-        _store_dataset(title, results_df)
-
-        # Generate the page-evolution summary now while we still have the
-        # user's API key in scope. Persist alongside the results CSV.
-        evolution_filename = f"{safe_title}_evolution.txt"
-        evolution_path = os.path.join("visualizations", evolution_filename)
-        try:
-            with progress_lock:
-                progress_map.setdefault(safe_title, {})["stage"] = "summarizing"
-            evolution_text = generate_evolution_summary(
-                results_df, api_key=api_key, model=model_name
-            )
-            if evolution_text:
-                with open(evolution_path, "w", encoding="utf-8") as f:
-                    f.write(evolution_text)
-        except Exception as e:
-            # Don't fail the whole classification if the summary errors.
-            print(f"[warn] evolution summary failed: {e}")
-
         print("Creating visualizations...")
-        taxonomy[12] = {
+        taxonomy[11] = {
             "category": "None of the above",
             "description": "No meaningful scientific changes were detected between the revisions; applies to non-scientific, formatting, or trivial edits."
         }
@@ -359,24 +330,12 @@ async def get_results(article: str) -> Dict[str, Any]:
 
     # Load results
     results_df = pd.read_csv(result_path)
-    _store_dataset(article, results_df)
     preview_rows = results_df.head(5).to_dict(orient="records")
 
     # URL paths for frontend
-    csv_url = f"/files/{result_filename}"
-    distribution_url = f"/files/{dist_filename}"
-    cooccurrence_url = f"/files/{cooc_filename}"
-
-    # Optional: page-evolution summary (label 1 + 6 across years).
-    evolution_filename = f"{safe_title}_evolution.txt"
-    evolution_path = os.path.join("visualizations", evolution_filename)
-    evolution_text: Optional[str] = None
-    if os.path.exists(evolution_path):
-        try:
-            with open(evolution_path, "r", encoding="utf-8") as f:
-                evolution_text = f.read().strip() or None
-        except Exception:
-            evolution_text = None
+    csv_url = f"/static/{result_filename}"
+    distribution_url = f"/static/{dist_filename}"
+    cooccurrence_url = f"/static/{cooc_filename}"
 
     return {
         "status": "complete",
@@ -385,484 +344,8 @@ async def get_results(article: str) -> Dict[str, Any]:
         "results": results_df.to_dict(orient="records"),
         "distributionUrl": distribution_url,
         "cooccurrenceUrl": cooccurrence_url,
-        "evolutionSummary": evolution_text,
     }
 
-DEMOS = {
-    "CRISPR": {
-        "csv": "demo/crispr_results.csv",
-        "title": "CRISPR",
-        "evolution": "demo/crispr_evolution.txt",
-    },
-}
-
-# Full per-article DataFrames retained in memory so the summarize endpoint
-# can look up Explanations for any (label, section, period) cell. Distinct
-# from `_demo_cache`, which holds the slim JSON payload sent to the browser.
-_dataset_cache: Dict[str, "pd.DataFrame"] = {}
-_dataset_cache_lock = threading.Lock()
-
-
-def _store_dataset(article: str, df: "pd.DataFrame") -> None:
-    with _dataset_cache_lock:
-        _dataset_cache[article] = df
-
-
-def _get_dataset(article: str) -> Optional["pd.DataFrame"]:
-    with _dataset_cache_lock:
-        return _dataset_cache.get(article)
-
-# Columns the frontend's interactive charts actually consume. We strip the
-# large free-text columns (Prompt/Explanation) from the bulk `results` array
-# to keep the JSON payload small; the first few preview rows keep them.
-CHART_COLUMNS = [
-    "Section",
-    "Timestamp",
-    "Labels",
-    "grouped_idx",
-    "Level",
-    "Flexible_Previous_Section",
-]
-
-# In-memory cache for prepared demo payloads. Keyed by article name; cleared
-# on container restart. Reading + serializing the CSV is the slow part on
-# repeat clicks, so caching the final dict is the biggest win.
-_demo_cache: Dict[str, Dict[str, Any]] = {}
-_demo_cache_lock = threading.Lock()
-
-
-def _build_demo_payload(demo: Dict[str, str]) -> Dict[str, Any]:
-    results_df = pd.read_csv(demo["csv"])
-    # The bundled demo CSVs may lack grouped_idx (e.g. older offline pipelines);
-    # compute it if absent so the frontend charts work uniformly.
-    if "grouped_idx" not in results_df.columns:
-        results_df["grouped_idx"] = compute_grouped_idx(results_df)
-
-    # Stash the full DataFrame so /summarize can look up Explanations.
-    _store_dataset(demo["title"], results_df)
-
-    preview_rows = results_df.head(5).to_dict(orient="records")
-
-    # Slim the bulk array: keep only columns the charts actually read.
-    chart_cols = [c for c in CHART_COLUMNS if c in results_df.columns]
-    slim_df = results_df[chart_cols]
-
-    # Static, pre-generated evolution summary if the demo entry provides one.
-    evolution_text: Optional[str] = None
-    evo_path = demo.get("evolution")
-    if evo_path and os.path.exists(evo_path):
-        try:
-            with open(evo_path, "r", encoding="utf-8") as f:
-                evolution_text = f.read().strip() or None
-        except Exception:
-            evolution_text = None
-
-    return {
-        "status": "complete",
-        "title": demo["title"],
-        "previewRows": preview_rows,
-        "results": slim_df.to_dict(orient="records"),
-        "evolutionSummary": evolution_text,
-        "csvUrl": f"/demo/download?article={demo['title']}",
-    }
-
-
-@app.get("/demo")
-async def get_demo(article: str) -> Dict[str, Any]:
-    """
-    Return a pre-classified results dataset packaged with the Space.
-    Lets users see the interactive visualizations without running their own
-    classification.
-    """
-    demo = DEMOS.get(article)
-    if demo is None:
-        raise HTTPException(status_code=404, detail=f"No demo available for '{article}'")
-    if not os.path.exists(demo["csv"]):
-        raise HTTPException(status_code=500, detail=f"Demo file missing: {demo['csv']}")
-
-    with _demo_cache_lock:
-        cached = _demo_cache.get(article)
-    if cached is not None:
-        return cached
-
-    payload = _build_demo_payload(demo)
-    with _demo_cache_lock:
-        _demo_cache[article] = payload
-    return payload
-
-
-@app.get("/demo/download")
-async def download_demo_csv(article: str) -> FileResponse:
-    """Serve the bundled demo CSV so users can save and re-load it later."""
-    demo = DEMOS.get(article)
-    if demo is None:
-        raise HTTPException(status_code=404, detail=f"No demo available for '{article}'")
-    if not os.path.exists(demo["csv"]):
-        raise HTTPException(status_code=500, detail=f"Demo file missing: {demo['csv']}")
-    return FileResponse(
-        demo["csv"],
-        media_type="text/csv",
-        filename=f"{demo['title']}_classified_revisions.csv",
-    )
-
-
-# ---------------------------------------------------------------------------
-# Summarize endpoint: small OpenAI call over the Explanation column for the
-# rows behind a clicked chart cell or line-chart point.
-# ---------------------------------------------------------------------------
-
-class SummarizePayload(BaseModel):
-    article: str
-    label: int
-    period: str  # one of "Y" | "2Q" | "Q" — matches the frontend Period type
-    period_key: str  # e.g. "2018", "2018Q3", "2018H1"
-    section_ids: Optional[List[int]] = None  # grouped_idx values; None = all sections
-    api_key: str
-    model: Optional[str] = "gpt-4o-mini"
-    # When the dataset lives only in the user's browser (CSV they uploaded),
-    # the frontend filters rows itself and sends just the matching explanation
-    # snippets here. If provided, the server skips its own dataset lookup.
-    explanations: Optional[List[str]] = None
-
-
-def _period_key(timestamp: pd.Timestamp, period: str) -> str:
-    """Mirror of the frontend's periodKey() in taxonomy.ts."""
-    if pd.isna(timestamp):
-        return ""
-    if not isinstance(timestamp, pd.Timestamp):
-        timestamp = pd.Timestamp(timestamp)
-    if timestamp.tzinfo is None:
-        timestamp = timestamp.tz_localize("UTC")
-    else:
-        timestamp = timestamp.tz_convert("UTC")
-    y = timestamp.year
-    m = timestamp.month - 1
-    if period == "Y":
-        return str(y)
-    q = m // 3
-    if period == "Q":
-        return f"{y}Q{q + 1}"
-    # half-year ("2Q")
-    return f"{y}H1" if q < 2 else f"{y}H2"
-
-
-def _parse_labels_field(raw: Any) -> List[int]:
-    if raw is None or (isinstance(raw, float) and pd.isna(raw)) or raw == "":
-        return []
-    if isinstance(raw, list):
-        return [int(x) for x in raw if str(x).strip().lstrip("-").isdigit()]
-    s = str(raw).strip()
-    if not s:
-        return []
-    try:
-        import ast as _ast
-        parsed = _ast.literal_eval(s.replace("'", '"'))
-        if isinstance(parsed, list):
-            return [int(x) for x in parsed if str(x).strip().lstrip("-").isdigit()]
-    except Exception:
-        pass
-    inner = s.strip("[]")
-    out: List[int] = []
-    for tok in inner.split(","):
-        tok = tok.strip()
-        if tok and tok.lstrip("-").isdigit():
-            out.append(int(tok))
-    return out
-
-
-def _explanation_for_label(raw: Any, label: int) -> Optional[str]:
-    """Explanation column stores a dict-like JSON string keyed by label."""
-    if raw is None or (isinstance(raw, float) and pd.isna(raw)) or raw == "":
-        return None
-    s = str(raw).strip()
-    if not s:
-        return None
-    try:
-        import ast as _ast
-        parsed = _ast.literal_eval(s)
-        if isinstance(parsed, dict):
-            # Keys may be int or str depending on how it was serialized.
-            for k in (label, str(label)):
-                if k in parsed:
-                    return str(parsed[k])
-    except Exception:
-        pass
-    return None
-
-
-# Labels considered "scientifically significant" for the evolution summary.
-EVOLUTION_LABELS = {1, 6}
-# Years with fewer than this many matching edits are dropped from the prompt.
-EVOLUTION_MIN_PER_YEAR = 5
-# Cap explanations sent per year to keep the prompt bounded on huge corpora.
-EVOLUTION_MAX_PER_YEAR = 60
-
-
-def generate_evolution_summary(
-    df: "pd.DataFrame", api_key: str, model: str = "gpt-4o-mini"
-) -> Optional[str]:
-    """
-    Build a prose paragraph describing scientifically significant edits across
-    years, grouped by year. Uses rows whose Labels include 1 (New Scientific
-    Information) or 6 (Change in Scientific Narrative). Drops sparse years
-    (< EVOLUTION_MIN_PER_YEAR matches) and caps very dense years.
-
-    Returns None if there's nothing to summarize, or raises on OpenAI errors.
-    """
-    if "Labels" not in df.columns or "Timestamp" not in df.columns:
-        return None
-    if "Explanation" not in df.columns:
-        return None
-
-    # Pre-parse once.
-    timestamps = pd.to_datetime(df["Timestamp"], utc=True, errors="coerce")
-    parsed_labels = df["Labels"].apply(_parse_labels_field)
-
-    # Group explanations by year, only for rows touching label 1 or 6.
-    by_year: Dict[int, List[str]] = {}
-    for idx, ts in timestamps.items():
-        if pd.isna(ts):
-            continue
-        labels = parsed_labels.iloc[idx] if hasattr(parsed_labels, "iloc") else parsed_labels[idx]
-        relevant = EVOLUTION_LABELS.intersection(labels)
-        if not relevant:
-            continue
-        raw = df.at[idx, "Explanation"]
-        snippets: List[str] = []
-        for label in relevant:
-            snippet = _explanation_for_label(raw, label)
-            if snippet:
-                snippets.append(f"(label {label}) {snippet}")
-        if not snippets:
-            continue
-        year = int(ts.year)
-        by_year.setdefault(year, []).extend(snippets)
-
-    if not by_year:
-        return None
-
-    # Drop sparse years and cap dense ones.
-    kept_years = sorted(y for y, items in by_year.items() if len(items) >= EVOLUTION_MIN_PER_YEAR)
-    if not kept_years:
-        return None
-
-    # Build a year-headered prompt.
-    lines: List[str] = []
-    total_used = 0
-    for year in kept_years:
-        items = by_year[year][:EVOLUTION_MAX_PER_YEAR]
-        total_used += len(items)
-        lines.append(f"\n## {year} ({len(by_year[year])} matching edits)")
-        for item in items:
-            lines.append(f"- {item}")
-    grouped_text = "\n".join(lines)
-
-    user_prompt = (
-        "You are summarizing the scientific evolution of a Wikipedia article "
-        "based on per-revision edit explanations. The explanations below are "
-        "grouped by year, drawn only from edits classified as introducing "
-        "new scientific information (label 1) or changing the scientific "
-        "narrative (label 6).\n\n"
-        "Write ONE flowing paragraph (about 8–12 sentences) that traces "
-        "how the article's scientific content evolved across years. "
-        "Highlight the most dominant scientific changes per year, weaving in "
-        "concrete topics (e.g., specific discoveries, mechanisms, applications, "
-        "or controversies) by year (e.g., \"In 2017, findings about X were added\"). "
-        "Mention years explicitly. Do not list every edit — synthesize the "
-        "patterns. Do not invent details that aren't in the explanations. Do "
-        "not use bullet points or section headers; output plain prose only.\n\n"
-        f"Explanations grouped by year ({total_used} total snippets):\n"
-        f"{grouped_text}"
-    )
-
-    import openai
-    client = openai.OpenAI(api_key=api_key)
-    completion = client.chat.completions.create(
-        model=model or "gpt-4o-mini",
-        messages=[
-            {
-                "role": "system",
-                "content": (
-                    "You are a careful research assistant that writes compact, "
-                    "evidence-grounded prose summaries of how Wikipedia articles "
-                    "evolved scientifically over time."
-                ),
-            },
-            {"role": "user", "content": user_prompt},
-        ],
-    )
-    text = completion.choices[0].message.content or ""
-    return text.strip() or None
-
-
-@app.post("/summarize")
-async def summarize_edits(payload: SummarizePayload) -> Dict[str, Any]:
-    """
-    Summarize the Explanation text for all rows that share a (label, period,
-    section-group) cell using a fast OpenAI model. Used by interactive chart
-    drill-downs.
-    """
-    # Client-supplied path: the dataset is in the user's browser, not cached
-    # on the server. The frontend has already filtered rows by (label, period,
-    # sections) and sent the relevant explanation snippets. Skip the server
-    # dataset lookup entirely.
-    client_snippets: Optional[List[str]] = None
-    matched_count: Optional[int] = None
-    if payload.explanations is not None:
-        client_snippets = [s for s in payload.explanations if s and s.strip()]
-        matched_count = len(payload.explanations)
-        if not client_snippets:
-            return {
-                "summary": None,
-                "edit_count": matched_count,
-                "explanation_count": 0,
-                "message": "Matched edits had no explanation text to summarize.",
-            }
-
-    df = None
-    if client_snippets is None:
-        df = _get_dataset(payload.article)
-        if df is None:
-            # Lazy-load the demo dataset if its cache got cleared (e.g. container restart).
-            demo = DEMOS.get(payload.article)
-            if demo is not None and os.path.exists(demo["csv"]):
-                _build_demo_payload(demo)
-                df = _get_dataset(payload.article)
-        if df is None:
-            raise HTTPException(
-                status_code=404,
-                detail=f"No dataset loaded for '{payload.article}'.",
-            )
-        if "Explanation" not in df.columns:
-            raise HTTPException(
-                status_code=400,
-                detail="This dataset has no Explanation column to summarize.",
-            )
-
-    if client_snippets is not None:
-        snippets = client_snippets
-        # We don't know the unmatched count on the client path; use the count
-        # the client reported.
-        match_count_for_response = matched_count or len(snippets)
-    else:
-        # Filter to rows that contain the requested label.
-        mask = df["Labels"].apply(lambda v: payload.label in _parse_labels_field(v))
-
-        # Filter by period bucket.
-        timestamps = pd.to_datetime(df["Timestamp"], utc=True, errors="coerce")
-        period_mask = timestamps.apply(lambda t: _period_key(t, payload.period) == payload.period_key)
-        mask = mask & period_mask
-
-        # Filter by section group(s) if provided.
-        if payload.section_ids is not None and "grouped_idx" in df.columns:
-            section_set = set(int(s) for s in payload.section_ids)
-            mask = mask & df["grouped_idx"].apply(
-                lambda v: int(v) in section_set if pd.notna(v) else False
-            )
-
-        matched = df.loc[mask]
-        if matched.empty:
-            return {
-                "summary": None,
-                "edit_count": 0,
-                "message": "No edits found for this cell.",
-            }
-
-        # Pull the relevant Explanation snippet for each matched row.
-        snippets = []
-        for _, row in matched.iterrows():
-            text = _explanation_for_label(row.get("Explanation"), payload.label)
-            if text:
-                snippets.append(text)
-
-        if not snippets:
-            return {
-                "summary": None,
-                "edit_count": int(len(matched)),
-                "message": "Matched edits had no explanation text to summarize.",
-            }
-        match_count_for_response = int(len(matched))
-
-    # Cap to keep the prompt comfortably within the model's context.
-    MAX_SNIPPETS = 100
-    truncated = len(snippets) > MAX_SNIPPETS
-    snippets_for_prompt = snippets[:MAX_SNIPPETS]
-
-    # Compose the prompt.
-    label_name_map = {
-        1: "New Scientific Information",
-        2: "Scientific Information Removed",
-        3: "Scientific Clarification Added",
-        4: "Scientific Technical Terms Added",
-        5: "Researcher Names Added",
-        6: "Change in Scientific Narrative",
-        7: "Academic References Added",
-        8: "Academic References Removed",
-        9: "Wikilink Added",
-        10: "Addition or Modification of Quantitative Information",
-        11: "Other Scientifically Relevant Change",
-        12: "None of the above",
-    }
-    label_name = label_name_map.get(payload.label, f"Label {payload.label}")
-
-    section_note = (
-        "across all sections"
-        if not payload.section_ids
-        else f"across {len(payload.section_ids)} section group(s)"
-    )
-
-    user_prompt = (
-        f"You are summarizing Wikipedia revision edits for the article "
-        f"'{payload.article}'.\n\n"
-        f"Below are explanations from {len(snippets_for_prompt)} edits "
-        f"classified as '{label_name}' (label {payload.label}) during "
-        f"period {payload.period_key}, {section_note}.\n\n"
-        f"Write a concise (4–6 sentences) summary describing what kinds of "
-        f"changes happened, common themes, and notable additions or removals. "
-        f"Be specific and cite concrete examples where possible. Do not "
-        f"enumerate every edit; synthesize patterns.\n\n"
-        f"Explanations:\n"
-        + "\n\n".join(f"- {s}" for s in snippets_for_prompt)
-    )
-
-    if truncated:
-        user_prompt += (
-            f"\n\n(Note: {len(snippets) - MAX_SNIPPETS} additional explanations "
-            f"were omitted to fit context.)"
-        )
-
-    try:
-        import openai
-        client = openai.OpenAI(api_key=payload.api_key)
-        completion = client.chat.completions.create(
-            model=payload.model or "gpt-4o-mini",
-            messages=[
-                {
-                    "role": "system",
-                    "content": (
-                        "You are a careful research assistant that summarizes "
-                        "lists of Wikipedia edit explanations into compact, "
-                        "high-signal prose."
-                    ),
-                },
-                {"role": "user", "content": user_prompt},
-            ],
-        )
-        summary_text = completion.choices[0].message.content or ""
-    except Exception as e:
-        raise HTTPException(status_code=502, detail=f"OpenAI request failed: {e}")
-
-    return {
-        "summary": summary_text.strip(),
-        "edit_count": match_count_for_response,
-        "explanation_count": len(snippets),
-        "truncated": truncated,
-        "label": payload.label,
-        "label_name": label_name,
-        "period_key": payload.period_key,
-    }
-
-
 @app.get("/progress")
 async def get_progress(article: str) -> Dict[str, Any]:
     """
@@ -882,31 +365,3 @@ async def get_progress(article: str) -> Dict[str, Any]:
         "batch": article_progress.get("batch"),
         "error": article_progress.get("error"),
     }
-
-# Serve the built React app at "/".
-# This must be registered AFTER all API routes so /prepare, /classify, /progress, /results,
-# /files still take precedence.
-from fastapi.responses import FileResponse  # noqa: E402
-
-FRONTEND_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "frontend_build")
-
-if os.path.isdir(FRONTEND_DIR):
-    # CRA's compiled JS/CSS lives under build/static/...
-    app.mount(
-        "/static",
-        StaticFiles(directory=os.path.join(FRONTEND_DIR, "static")),
-        name="frontend-assets",
-    )
-
-    @app.get("/")
-    async def serve_index() -> FileResponse:
-        return FileResponse(os.path.join(FRONTEND_DIR, "index.html"))
-
-    @app.get("/{full_path:path}")
-    async def spa_fallback(full_path: str) -> FileResponse:
-        # Try a real file under frontend_build first (favicon.ico, manifest.json, etc.).
-        candidate = os.path.join(FRONTEND_DIR, full_path)
-        if os.path.isfile(candidate):
-            return FileResponse(candidate)
-        # Otherwise fall back to index.html (no client-side router today, but harmless).
-        return FileResponse(os.path.join(FRONTEND_DIR, "index.html"))

frontend_build/asset-manifest.json

@@ -1,12 +1,12 @@
 {
   "files": {
-    "main.css": "/static/css/main.78d0a2b0.css",
-    "main.js": "/static/js/main.ec549950.js",
+    "main.css": "/static/css/main.fb799031.css",
+    "main.js": "/static/js/main.0f49f162.js",
     "static/js/206.b8423ec3.chunk.js": "/static/js/206.b8423ec3.chunk.js",
     "index.html": "/index.html"
   },
   "entrypoints": [
-    "static/css/main.78d0a2b0.css",
-    "static/js/main.ec549950.js"
+    "static/css/main.fb799031.css",
+    "static/js/main.0f49f162.js"
   ]
 }

frontend_build/index.html

@@ -1 +1 @@
-<!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Web site created using create-react-app"/><link rel="apple-touch-icon" href="/logo192.png"/><link rel="manifest" href="/manifest.json"/><title>React App</title><script defer="defer" src="/static/js/main.ec549950.js"></script><link href="/static/css/main.78d0a2b0.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div></body></html>
+<!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Web site created using create-react-app"/><link rel="apple-touch-icon" href="/logo192.png"/><link rel="manifest" href="/manifest.json"/><title>React App</title><script defer="defer" src="/static/js/main.0f49f162.js"></script><link href="/static/css/main.fb799031.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div></body></html>

frontend_build/static/css/{main.78d0a2b0.css → main.fb799031.css}

@@ -1,3 +1,3 @@
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