Update app/app.py

app/app.py

@@ -1,321 +1,316 @@
-# app/app.py
-# Windows click-to-run local RAG app that queries Claude via Anthropic API.
-import os, sys, time, csv, re
-from pathlib import Path
-import yaml
-import pandas as pd
-import numpy as np
-# claude_test.py (or app.py)
-from dotenv import load_dotenv
-
-load_dotenv()
-
-ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
-print("ANTHROPIC in env:", bool(os.getenv("ANTHROPIC_API_KEY")), flush=True)
-
-if not ANTHROPIC_API_KEY:
-    raise SystemExit("Missing ANTHROPIC_API_KEY in environment or .env file.")
-
-# --- Privacy: keep HF offline (embeddings can come from your local cache)
-# os.environ.setdefault("HF_HUB_DISABLE_TELEMETRY", "1")
-# os.environ.setdefault("TRANSFORMERS_OFFLINE", "1")
-
-# --- Resolve paths (works both as .py and PyInstaller .exe)
-APP_DIR = Path(__file__).resolve().parent          # .../app
-if hasattr(sys, "_MEIPASS"):                       # PyInstaller bundle
-    ROOT = Path(sys._MEIPASS)                      # bundle root at runtime
-else:
-    ROOT = APP_DIR.parent                          # project root (…/RAG_APP)
-
-CFG_PATH = ROOT / "app" / "config" / "app.yaml"
-MODELS_DIR = ROOT / "models"
-INDEX_DIR = ROOT / "outputs" / "index"
-LOGS_DIR = ROOT / "local_logs"
-DOCS_DIR = ROOT / "docs"
-
-LOGS_DIR.mkdir(parents=True, exist_ok=True)
-
-# --- Read config
-DEFAULT_CFG = {
-    "retrieval": {"top_k": 12, "evidence_shown": 3, "answerability_threshold": 0.2},
-    "generator": {
-        "enabled_default": False,
-        "use_top_evidence": 5,
-        "temperature": 0.1,
-        "max_answer_sentences": 20,
-        "n_ctx": 4096,
-        "threads": max(2, (os.cpu_count() or 4) - 1)
-    },
-    "models": {
-        "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
-        "embedding_local_dir": None,  # set a folder if you want to ship the embed model locally
-        # Anthropic model alias or versioned name:
-        # good choices: "claude-3-5-sonnet-latest", "claude-3-5-haiku-latest", "claude-3-opus-20240229"
-        "anthropic_model": "claude-3-5-sonnet-latest"
-    },
-    "ui": {"show_online_badge": True, "performance_mode": "standard"}  # quick|standard
-}
-cfg = DEFAULT_CFG
-if CFG_PATH.exists():
-    # shallow merge is fine here; extend if you need nested merges
-    cfg = {**cfg, **yaml.safe_load(CFG_PATH.read_text(encoding="utf-8"))}
-
-# --- Load FAISS index & embeddings
-import faiss
-from sentence_transformers import SentenceTransformer
-
-META_PATH = INDEX_DIR / "meta.parquet"
-FAISS_PATH = INDEX_DIR / "chunks.faiss"
-
-if not META_PATH.exists() or not FAISS_PATH.exists():
-    raise SystemExit("Index not found. Ensure outputs/index/meta.parquet and chunks.faiss exist.")
-
-df = pd.read_parquet(META_PATH)
-df["text"] = df["text"].fillna("").astype(str)
-
-# Load SentenceTransformer from local dir if provided (for offline use)
-emb_dir = cfg["models"].get("embedding_local_dir")
-if emb_dir:
-    EMBED_MODEL_PATH = Path(emb_dir)
-    if not EMBED_MODEL_PATH.exists():
-        raise SystemExit(f"Embedding model folder not found: {EMBED_MODEL_PATH}")
-    embed_model = SentenceTransformer(str(EMBED_MODEL_PATH), trust_remote_code=False)
-else:
-    # Will use local cache; ensure you’ve pre-fetched this once on a connected machine
-    embed_model = SentenceTransformer(cfg["models"]["embedding_model"], trust_remote_code=False)
-
-index = faiss.read_index(str(FAISS_PATH))
-
-def _format_citation(row):
-    p = int(row["page"]) if pd.notna(row.get("page")) else None
-    return f"{row['title']} (p.{p})" if p else f"{row['title']}"
-
-def retrieve(query, top_k=6):
-    qv = embed_model.encode([query], convert_to_numpy=True, normalize_embeddings=True).astype("float32")
-    scores, idxs = index.search(qv, top_k)
-    out = []
-    for s, ix in zip(scores[0], idxs[0]):
-        r = df.iloc[int(ix)]
-        out.append({
-            "score": float(s),
-            "citation": _format_citation(r),
-            "doc_id": r.get("doc_id", ""),
-            "page": None if pd.isna(r.get("page")) else int(r["page"]),
-            "chunk_id": int(r["chunk_id"]),
-            "text": r["text"]
-        })
-    return out
-
-# --- Anthropic (Claude) generator
-from anthropic import Anthropic, APIError
-
-ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY")
-if not ANTHROPIC_API_KEY:
-    raise SystemExit("Missing ANTHROPIC_API_KEY environment variable.")
-
-anthropic_client = Anthropic(api_key=ANTHROPIC_API_KEY)
-CLAUDE_MODEL = cfg["models"].get("anthropic_model", "claude-3-5-sonnet-latest")
-
-SYSTEM_PROMPT = (
-    "You are a careful assistant for clinicians. "
-    "Use ONLY the provided context to answer. "
-    "Be concise. Add inline citations like [1], [2] matching the numbered context. "
-    "If the context does not fully answer, provide the best supported guidance you can, and point to the closest relevant passages with citations"
-)
-
-def _citations_valid(text, k):
-    nums = set(int(n) for n in re.findall(r"\[(\d+)\]", text))
-    return bool(nums) and all(1 <= n <= k for n in nums)
-
-def _join_cites(nums):
-    nums = [f"[{n}]" for n in nums]
-    if not nums:
-        return ""
-    if len(nums) == 1:
-        return nums[0]
-    return ", ".join(nums[:-1]) + " and " + nums[-1]
-
-def _make_context_block(ctx, use_n):
-    blocks = []
-    for i, c in enumerate(ctx[:use_n], 1):
-        blocks.append(f"[{i}] {c['citation']}\n{c['text']}\n")
-    return "\n".join(blocks)
-
-def generate_answer(question, ctx, use_n, temp=0.1, max_sentences=6):
-    context_text = _make_context_block(ctx, use_n)
-    user_prompt = (
-        f"Context:\n\n{context_text}\n\n"
-        f"Question: {question}\n"
-        "Answer using ONLY the context above and cite with [1], [2], etc."
-    )
-    try:
-        resp = anthropic_client.messages.create(
-            model=CLAUDE_MODEL,
-            system=SYSTEM_PROMPT,
-            max_tokens=600,
-            temperature=float(temp),
-            messages=[{"role": "user", "content": [{"type": "text", "text": user_prompt}]}],
-        )
-    except APIError as e:
-        return f"_API error from Anthropic: {e}_"
-
-    # Claude returns a list of content blocks; we are expectin text blocks
-    parts = []
-    for blk in resp.content:
-        if blk.type == "text":
-            parts.append(blk.text)
-    full = ("\n".join(parts)).strip()
-
-    # # Log raw model output to console for debugging
-    # print("\n===== RAW MODEL OUTPUT =====\n", full, "\n============================\n", flush=True)
-
-    # Trim to ~N sentences to keep it short for testers
-    sents = re.split(r'(?<=[.!?])\s+', full)
-    short = " ".join(sents[:max_sentences]).strip()
-    return short
-
-# (Gradio on localhost)
-import gradio as gr
-ONLINE_BADGE = "Standards of Practice & Code of Ethics" if cfg["ui"].get("show_online_badge", True) else ""
-def _top_sentences(text, n=3):
-    sents = re.split(r'(?<=[.!?])\s+', text.strip())
-    return [s for s in sents if s][:n]
-
-def answer_extractive(query, k=6, per_chunk_sents=2):
-    ctx = retrieve(query, top_k=k)
-    bullets, refs = [], []
-    for i, c in enumerate(ctx, 1):
-        for s in _top_sentences(c["text"], per_chunk_sents):
-            bullets.append(f"- {s} [{i}]")
-        refs.append(f"[{i}] {c['citation']}")
-    if not bullets:
-        return "I couldn’t find relevant text in the corpus.", refs
-    return "\n".join(bullets) + "\n\nSources:\n" + "\n".join(refs), refs
-
-def app_infer(question, do_generate, mode):
-    start = time.time()
-    if not question or not question.strip():
-        return "", "", "", f"{ONLINE_BADGE}  Ready."
-
-    # Retrieval
-    top_k = int(cfg["retrieval"]["top_k"])
-    shown = int(cfg["retrieval"]["evidence_shown"])
-    use_n = int(cfg["generator"]["use_top_evidence"])
-    if mode == "quick":
-        shown = min(3, shown)
-        use_n = min(3, use_n)
-
-    ctx = retrieve(question, top_k=top_k)
-
-    # Prepare evidence panel (hidden when shown == 0)
-    if shown > 0:
-        ev_md_lines = []
-        for i, c in enumerate(ctx[:shown], 1):
-            title = c["citation"]
-            pg = f" (p.{c['page']})" if c["page"] else ""
-            body = c["text"].strip()
-            body_short = body if len(body) <= 1200 else body[:1200] + "..."
-            ev_md_lines.append(f"**[{i}] {title}**\n\n{body_short}\n")
-        evidence_md = "\n---\n".join(ev_md_lines)
-    else:
-        evidence_md = ""  
-    # Decide if we should generate
-    answer = ""
-    sources_md = ""
-    conf = float(ctx[0]["score"]) if ctx else 0.0
-    threshold = float(cfg["retrieval"].get("answerability_threshold", 0.01))
-
-    if not ctx:
-        status = f"{ONLINE_BADGE}  No evidence found."
-        return evidence_md, answer, sources_md, status
-
-    if do_generate and conf >= threshold:
-        draft = generate_answer(
-            question=question,
-            ctx=ctx,
-            use_n=use_n,
-            temp=float(cfg["generator"]["temperature"]),
-            max_sentences=int(cfg["generator"]["max_answer_sentences"])
-        )
-        # Validate citations
-        # if not _citations_valid(draft, min(use_n, len(ctx))):
-        #     answer = "_Not enough evidence to generate a reliable summary. See Evidence below._"
-        # else:
-        #     answer = draft
-        
-        if not _citations_valid(draft, min(use_n, len(ctx))):
-            extractive, _ = answer_extractive(question, k=use_n, per_chunk_sents=2)
-            answer = extractive
-        else:
-            answer = draft
-
-
-
-    elif do_generate and conf < threshold:
-        answer = "_Not enough evidence—see Evidence below._"
-
-    # Sources list
-    src_lines = [f"[{i}] {c['citation']}" for i, c in enumerate(ctx[:use_n], 1)]
-    sources_md = "Sources:\n" + "\n".join(src_lines)
-    if answer:
-        a = answer.strip()
-        if not a.lower().startswith("answer:"):
-            answer = f"Answer: {a}"
-
-    dur = time.time() - start
-    status = f"{ONLINE_BADGE}  Done in {dur:.1f}s (conf={conf:.2f})."
-    return evidence_md, answer, sources_md, status
-
-def save_feedback(question, rating, note, answer_shown):
-    fpath = LOGS_DIR / "feedback.csv"
-    new = not fpath.exists()
-    with fpath.open("a", newline="", encoding="utf-8") as f:
-        w = csv.writer(f)
-        if new:
-            w.writerow(["timestamp","question","rating","note","answer_shown"])
-        w.writerow([time.strftime("%Y-%m-%d %H:%M:%S"), question, rating, note, "yes" if answer_shown else "no"])
-    return "Feedback saved. Thank you!"
-
-APP_CSS = """
-:root{
-  --app-font: system-ui, -apple-system, "Segoe UI", Roboto, Helvetica, Arial,
-              "Apple Color Emoji","Segoe UI Emoji";
-}
-body, .gradio-container { font-family: var(--app-font) !important; }
-/* make reading nicer */
-.gr-markdown { font-size: 16px; line-height: 1.6; }
-.gr-markdown h2 { font-size: 18px; margin-top: 0.6rem; }
-.gr-textbox textarea { font-size: 16px; }
-"""
-
-with gr.Blocks(title="Clinician Q&A", theme="soft", css=APP_CSS) as demo:
-    gr.Markdown(f"## Clinician Q&A   {'&nbsp;&nbsp;'+ONLINE_BADGE if ONLINE_BADGE else ''}")
-    with gr.Row():
-        with gr.Column(scale=1):
-            q = gr.Textbox(label="Ask a question", placeholder="e.g., When can confidentiality be broken?")
-            do_gen = gr.Checkbox(value=cfg["generator"]["enabled_default"], label="Use LLM")
-            mode = gr.Radio(choices=["standard","quick"], value=cfg["ui"].get("performance_mode","standard"), label="Performance mode")
-            run = gr.Button("Answer", variant="primary")
-            rating = gr.Radio(choices=["Helpful","Not sure","Incorrect"], label="Feedback", value=None)
-            note = gr.Textbox(label="Add a note (optional)")
-            submit = gr.Button("Submit feedback")
-            status = gr.Markdown("Ready.")
-        with gr.Column(scale=1):
-            ans = gr.Markdown(label="Answer")
-            ev = gr.Markdown(label="Evidence")
-            src = gr.Markdown(label="Sources")
-
-    run.click(app_infer, inputs=[q, do_gen, mode], outputs=[ ans,ev,src, status])
-    submit.click(lambda question, r, n, a: save_feedback(question, r, n, bool(a and a.strip())),
-                 inputs=[q, rating, note, ans], outputs=[status])
-
-# if __name__ == "__main__":
-#     # Bind to localhost only; opens a browser tab automatically.
-#     demo.launch(server_name="127.0.0.1", server_port=7860, inbrowser=True, show_error=True)
-
-if __name__ == "__main__":
-    # In cloud (HF Spaces), bind to 0.0.0.0 and respect PORT if provided.
-    port = int(os.getenv("PORT", "7860"))
-    host = "0.0.0.0"
-    demo.queue(max_size=32).launch(server_name=host, server_port=port, show_error=True)
-
+# app/app.py
+import os, sys, time, csv, re
+from pathlib import Path
+import yaml
+import pandas as pd
+import numpy as np
+from dotenv import load_dotenv
+
+load_dotenv()
+
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
+print("ANTHROPIC in env:", bool(os.getenv("ANTHROPIC_API_KEY")), flush=True)
+
+if not ANTHROPIC_API_KEY:
+    raise SystemExit("Missing ANTHROPIC_API_KEY in environment or .env file.")
+
+
+# os.environ.setdefault("HF_HUB_DISABLE_TELEMETRY", "1")
+# os.environ.setdefault("TRANSFORMERS_OFFLINE", "1")
+
+# --- Resolve paths (works both as .py and PyInstaller .exe)
+APP_DIR = Path(__file__).resolve().parent          # .../app
+if hasattr(sys, "_MEIPASS"):                       
+    ROOT = Path(sys._MEIPASS)                      
+else:
+    ROOT = APP_DIR.parent                          
+
+CFG_PATH = ROOT / "app" / "config" / "app.yaml"
+MODELS_DIR = ROOT / "models"
+INDEX_DIR = ROOT / "outputs" / "index"
+LOGS_DIR = ROOT / "local_logs"
+DOCS_DIR = ROOT / "docs"
+
+LOGS_DIR.mkdir(parents=True, exist_ok=True)
+
+# --- Read config
+DEFAULT_CFG = {
+    "retrieval": {"top_k": 12, "evidence_shown": 3, "answerability_threshold": 0.2},
+    "generator": {
+        "enabled_default": False,
+        "use_top_evidence": 5,
+        "temperature": 0.1,
+        "max_answer_sentences": 20,
+        "n_ctx": 4096,
+        "threads": max(2, (os.cpu_count() or 4) - 1)
+    },
+    "models": {
+        "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
+        "embedding_local_dir": None,  
+        "anthropic_model": "claude-3-5-sonnet-latest"
+    },
+    "ui": {"show_online_badge": True, "performance_mode": "standard"}  # quick|standard
+}
+cfg = DEFAULT_CFG
+if CFG_PATH.exists():
+    cfg = {**cfg, **yaml.safe_load(CFG_PATH.read_text(encoding="utf-8"))}
+
+# --- Load FAISS index & embeddings
+import faiss
+from sentence_transformers import SentenceTransformer
+
+META_PATH = INDEX_DIR / "meta.parquet"
+FAISS_PATH = INDEX_DIR / "chunks.faiss"
+
+if not META_PATH.exists() or not FAISS_PATH.exists():
+    raise SystemExit("Index not found. Ensure outputs/index/meta.parquet and chunks.faiss exist.")
+
+df = pd.read_parquet(META_PATH)
+df["text"] = df["text"].fillna("").astype(str)
+
+#loading sentence transformer
+emb_dir = cfg["models"].get("embedding_local_dir")
+if emb_dir:
+    EMBED_MODEL_PATH = Path(emb_dir)
+    if not EMBED_MODEL_PATH.exists():
+        raise SystemExit(f"Embedding model folder not found: {EMBED_MODEL_PATH}")
+    embed_model = SentenceTransformer(str(EMBED_MODEL_PATH), trust_remote_code=False)
+else:
+    
+    embed_model = SentenceTransformer(cfg["models"]["embedding_model"], trust_remote_code=False)
+
+index = faiss.read_index(str(FAISS_PATH))
+
+def _format_citation(row):
+    p = int(row["page"]) if pd.notna(row.get("page")) else None
+    return f"{row['title']} (p.{p})" if p else f"{row['title']}"
+
+def retrieve(query, top_k=6):
+    qv = embed_model.encode([query], convert_to_numpy=True, normalize_embeddings=True).astype("float32")
+    scores, idxs = index.search(qv, top_k)
+    out = []
+    for s, ix in zip(scores[0], idxs[0]):
+        r = df.iloc[int(ix)]
+        out.append({
+            "score": float(s),
+            "citation": _format_citation(r),
+            "doc_id": r.get("doc_id", ""),
+            "page": None if pd.isna(r.get("page")) else int(r["page"]),
+            "chunk_id": int(r["chunk_id"]),
+            "text": r["text"]
+        })
+    return out
+
+# Anthropic (Claude) LLM
+from anthropic import Anthropic, APIError
+
+ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY")
+if not ANTHROPIC_API_KEY:
+    raise SystemExit("Missing ANTHROPIC_API_KEY environment variable.")
+
+anthropic_client = Anthropic(api_key=ANTHROPIC_API_KEY)
+CLAUDE_MODEL = cfg["models"].get("anthropic_model", "claude-3-5-sonnet-latest")
+
+SYSTEM_PROMPT = (
+    "You are a careful assistant for clinicians. "
+    "Use ONLY the provided context to answer. "
+    "Be concise. Add inline citations like [1], [2] matching the numbered context. "
+    "If the context does not fully answer, provide the best supported guidance you can, and point to the closest relevant passages with citations"
+)
+
+def _citations_valid(text, k):
+    nums = set(int(n) for n in re.findall(r"\[(\d+)\]", text))
+    return bool(nums) and all(1 <= n <= k for n in nums)
+
+def _join_cites(nums):
+    nums = [f"[{n}]" for n in nums]
+    if not nums:
+        return ""
+    if len(nums) == 1:
+        return nums[0]
+    return ", ".join(nums[:-1]) + " and " + nums[-1]
+
+def _make_context_block(ctx, use_n):
+    blocks = []
+    for i, c in enumerate(ctx[:use_n], 1):
+        blocks.append(f"[{i}] {c['citation']}\n{c['text']}\n")
+    return "\n".join(blocks)
+
+def generate_answer(question, ctx, use_n, temp=0.1, max_sentences=6):
+    context_text = _make_context_block(ctx, use_n)
+    user_prompt = (
+        f"Context:\n\n{context_text}\n\n"
+        f"Question: {question}\n"
+        "Answer using ONLY the context above and cite with [1], [2], etc."
+    )
+    try:
+        resp = anthropic_client.messages.create(
+            model=CLAUDE_MODEL,
+            system=SYSTEM_PROMPT,
+            max_tokens=600,
+            temperature=float(temp),
+            messages=[{"role": "user", "content": [{"type": "text", "text": user_prompt}]}],
+        )
+    except APIError as e:
+        return f"_API error from Anthropic: {e}_"
+
+    # Claude returns a list of content blocks
+    parts = []
+    for blk in resp.content:
+        if blk.type == "text":
+            parts.append(blk.text)
+    full = ("\n".join(parts)).strip()
+
+    # # Log raw model output to console for debugging
+    # print("\n===== RAW MODEL OUTPUT =====\n", full, "\n============================\n", flush=True)
+
+    # Trim to ~N sentences to keep it short for testers
+    sents = re.split(r'(?<=[.!?])\s+', full)
+    short = " ".join(sents[:max_sentences]).strip()
+    return short
+
+# gradio
+import gradio as gr
+ONLINE_BADGE = "Standards of Practice & Code of Ethics" if cfg["ui"].get("show_online_badge", True) else ""
+def _top_sentences(text, n=3):
+    sents = re.split(r'(?<=[.!?])\s+', text.strip())
+    return [s for s in sents if s][:n]
+
+def answer_extractive(query, k=6, per_chunk_sents=2):
+    ctx = retrieve(query, top_k=k)
+    bullets, refs = [], []
+    for i, c in enumerate(ctx, 1):
+        for s in _top_sentences(c["text"], per_chunk_sents):
+            bullets.append(f"- {s} [{i}]")
+        refs.append(f"[{i}] {c['citation']}")
+    if not bullets:
+        return "I couldn’t find relevant text in the corpus.", refs
+    return "\n".join(bullets) + "\n\nSources:\n" + "\n".join(refs), refs
+
+def app_infer(question, do_generate, mode):
+    start = time.time()
+    if not question or not question.strip():
+        return "", "", "", f"{ONLINE_BADGE}  Ready."
+
+    # Retrieval
+    top_k = int(cfg["retrieval"]["top_k"])
+    shown = int(cfg["retrieval"]["evidence_shown"])
+    use_n = int(cfg["generator"]["use_top_evidence"])
+    if mode == "quick":
+        shown = min(3, shown)
+        use_n = min(3, use_n)
+
+    ctx = retrieve(question, top_k=top_k)
+
+    # Prepare evidence panel (currently hidden as shown == 0)
+    if shown > 0:
+        ev_md_lines = []
+        for i, c in enumerate(ctx[:shown], 1):
+            title = c["citation"]
+            pg = f" (p.{c['page']})" if c["page"] else ""
+            body = c["text"].strip()
+            body_short = body if len(body) <= 1200 else body[:1200] + "..."
+            ev_md_lines.append(f"**[{i}] {title}**\n\n{body_short}\n")
+        evidence_md = "\n---\n".join(ev_md_lines)
+    else:
+        evidence_md = ""  
+    # Decide if we should generate?
+    answer = ""
+    sources_md = ""
+    conf = float(ctx[0]["score"]) if ctx else 0.0
+    threshold = float(cfg["retrieval"].get("answerability_threshold", 0.01))
+
+    if not ctx:
+        status = f"{ONLINE_BADGE}  No evidence found."
+        return evidence_md, answer, sources_md, status
+
+    if do_generate and conf >= threshold:
+        draft = generate_answer(
+            question=question,
+            ctx=ctx,
+            use_n=use_n,
+            temp=float(cfg["generator"]["temperature"]),
+            max_sentences=int(cfg["generator"]["max_answer_sentences"])
+        )
+        # Validate citations
+        # if not _citations_valid(draft, min(use_n, len(ctx))):
+        #     answer = "_Not enough evidence to generate a reliable summary. See Evidence below._"
+        # else:
+        #     answer = draft
+        
+        if not _citations_valid(draft, min(use_n, len(ctx))):
+            extractive, _ = answer_extractive(question, k=use_n, per_chunk_sents=2)
+            answer = extractive
+        else:
+            answer = draft
+
+
+
+    elif do_generate and conf < threshold:
+        answer = "_Not enough evidence—see Evidence below._"
+
+    # Sources list
+    src_lines = [f"[{i}] {c['citation']}" for i, c in enumerate(ctx[:use_n], 1)]
+    sources_md = "Sources:\n" + "\n".join(src_lines)
+    if answer:
+        a = answer.strip()
+        if not a.lower().startswith("answer:"):
+            answer = f"Answer: {a}"
+
+    dur = time.time() - start
+    status = f"{ONLINE_BADGE}  Done in {dur:.1f}s (conf={conf:.2f})."
+    return evidence_md, answer, sources_md, status
+
+def save_feedback(question, rating, note, answer_shown):
+    fpath = LOGS_DIR / "feedback.csv"
+    new = not fpath.exists()
+    with fpath.open("a", newline="", encoding="utf-8") as f:
+        w = csv.writer(f)
+        if new:
+            w.writerow(["timestamp","question","rating","note","answer_shown"])
+        w.writerow([time.strftime("%Y-%m-%d %H:%M:%S"), question, rating, note, "yes" if answer_shown else "no"])
+    return "Feedback saved. Thank you!"
+
+APP_CSS = """
+:root{
+  --app-font: system-ui, -apple-system, "Segoe UI", Roboto, Helvetica, Arial,
+              "Apple Color Emoji","Segoe UI Emoji";
+}
+body, .gradio-container { font-family: var(--app-font) !important; }
+/* make reading nicer */
+.gr-markdown { font-size: 16px; line-height: 1.6; }
+.gr-markdown h2 { font-size: 18px; margin-top: 0.6rem; }
+.gr-textbox textarea { font-size: 16px; }
+"""
+
+with gr.Blocks(title="Clinician Q&A", theme="soft", css=APP_CSS) as demo:
+    gr.Markdown(f"## Clinician Q&A   {'&nbsp;&nbsp;'+ONLINE_BADGE if ONLINE_BADGE else ''}")
+    with gr.Row():
+        with gr.Column(scale=1):
+            q = gr.Textbox(label="Ask a question", placeholder="e.g., When can confidentiality be broken?")
+            do_gen = gr.Checkbox(value=cfg["generator"]["enabled_default"], label="Use LLM")
+            mode = gr.Radio(choices=["standard","quick"], value=cfg["ui"].get("performance_mode","standard"), label="Performance mode")
+            run = gr.Button("Answer", variant="primary")
+            rating = gr.Radio(choices=["Helpful","Not sure","Incorrect"], label="Feedback", value=None)
+            note = gr.Textbox(label="Add a note (optional)")
+            submit = gr.Button("Submit feedback")
+            status = gr.Markdown("Ready.")
+        with gr.Column(scale=1):
+            ans = gr.Markdown(label="Answer")
+            ev = gr.Markdown(label="Evidence")
+            src = gr.Markdown(label="Sources")
+
+    run.click(app_infer, inputs=[q, do_gen, mode], outputs=[ ans,ev,src, status])
+    submit.click(lambda question, r, n, a: save_feedback(question, r, n, bool(a and a.strip())),
+                 inputs=[q, rating, note, ans], outputs=[status])
+
+# if __name__ == "__main__":
+#     # Bind to localhost only; opens a browser tab automatically.
+#     demo.launch(server_name="127.0.0.1", server_port=7860, inbrowser=True, show_error=True)
+
+if __name__ == "__main__":
+    # In cloud (HF Spaces), bind to 0.0.0.0 and respect PORT if provided.
+    port = int(os.getenv("PORT", "7860"))
+    host = "0.0.0.0"
+    demo.queue(max_size=32).launch(server_name=host, server_port=port, show_error=True)
+