Create app.py

app.py

@@ -0,0 +1,831 @@
+# ================================================================
+# Self-Sensing Concrete Assistant — Predictor (XGB) + Hybrid RAG
+# - Uses local 'papers/' folder for literature
+# - Robust MMR sentence selection (no list index errors)
+# - Predictor: safe model caching + safe feature alignment
+# - Stable categoricals ("NA"); no over-strict completeness gate
+# - Fixed [[PAGE=...]] regex
+# ================================================================
+
+# ---------------------- Runtime flags (HF-safe) ----------------------
+import os
+os.environ["TRANSFORMERS_NO_TF"] = "1"
+os.environ["TRANSFORMERS_NO_FLAX"] = "1"
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+# ------------------------------- Imports ------------------------------
+import re, joblib, warnings, json, traceback
+from pathlib import Path
+from typing import List, Dict, Any
+
+import numpy as np
+import pandas as pd
+import gradio as gr
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+# Optional deps (handled gracefully if missing)
+USE_DENSE = True
+try:
+    from sentence_transformers import SentenceTransformer
+except Exception:
+    USE_DENSE = False
+
+try:
+    from rank_bm25 import BM25Okapi
+except Exception:
+    BM25Okapi = None
+    print("rank_bm25 not installed; BM25 disabled (TF-IDF still works).")
+
+# Optional OpenAI (for LLM paraphrase)
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+OPENAI_MODEL   = os.getenv("OPENAI_MODEL", "gpt-5")
+try:
+    from openai import OpenAI
+except Exception:
+    OpenAI = None
+
+# LLM availability flag — used internally; UI remains hidden
+LLM_AVAILABLE = (OPENAI_API_KEY is not None and OPENAI_API_KEY.strip() != "" and OpenAI is not None)
+
+# ========================= Predictor (kept) =========================
+CF_COL     = "Conductive Filler Conc. (wt%)"
+TARGET_COL = "Stress GF (MPa-1)"
+CANON_NA   = "NA"  # canonical placeholder for categoricals
+
+MAIN_VARIABLES = [
+    "Filler 1 Type",
+    "Filler 1 Diameter (µm)",
+    "Filler 1 Length (mm)",
+    CF_COL,
+    "Filler 1 Dimensionality",
+    "Filler 2 Type",
+    "Filler 2 Diameter (µm)",
+    "Filler 2 Length (mm)",
+    "Filler 2 Dimensionality",
+    "Specimen Volume (mm3)",
+    "Probe Count",
+    "Probe Material",
+    "W/B",
+    "S/B",
+    "Gauge Length (mm)",
+    "Curing Condition",
+    "Number of Fillers",
+    "Drying Temperature (°C)",
+    "Drying Duration (hr)",
+    "Loading Rate (MPa/s)",
+    "Modulus of Elasticity (GPa)",
+    "Current Type",
+    "Applied Voltage (V)"
+]
+
+NUMERIC_COLS = {
+    "Filler 1 Diameter (µm)",
+    "Filler 1 Length (mm)",
+    CF_COL,
+    "Filler 2 Diameter (µm)",
+    "Filler 2 Length (mm)",
+    "Specimen Volume (mm3)",
+    "Probe Count",
+    "W/B",
+    "S/B",
+    "Gauge Length (mm)",
+    "Number of Fillers",
+    "Drying Temperature (°C)",
+    "Drying Duration (hr)",
+    "Loading Rate (MPa/s)",
+    "Modulus of Elasticity (GPa)",
+    "Applied Voltage (V)"
+}
+
+CATEGORICAL_COLS = {
+    "Filler 1 Type",
+    "Filler 1 Dimensionality",
+    "Filler 2 Type",
+    "Filler 2 Dimensionality",
+    "Probe Material",
+    "Curing Condition",
+    "Current Type"
+}
+
+DIM_CHOICES     = ["0D", "1D", "2D", "3D", CANON_NA]
+CURRENT_CHOICES = ["DC", "AC", CANON_NA]
+
+MODEL_CANDIDATES = [
+    "stress_gf_xgb.joblib",
+    "models/stress_gf_xgb.joblib",
+    "/home/user/app/stress_gf_xgb.joblib",
+    os.getenv("MODEL_PATH", "")
+]
+
+# ---------- Model caching + status ----------
+MODEL = None
+MODEL_STATUS = "🔴 Model not loaded"
+
+def _try_load_model():
+    global MODEL, MODEL_STATUS
+    for p in [x for x in MODEL_CANDIDATES if x]:
+        if os.path.exists(p):
+            try:
+                MODEL = joblib.load(p)
+                MODEL_STATUS = f"🟢 Loaded model: {Path(p).name}"
+                print("[ModelLoad] Loaded:", p)
+                return
+            except Exception as e:
+                print(f"[ModelLoad] Error from {p}: {e}")
+                traceback.print_exc()
+                MODEL = None
+    if MODEL is None:
+        MODEL_STATUS = "🔴 Model not found (place stress_gf_xgb.joblib at repo root or models/, or set MODEL_PATH)"
+        print("[ModelLoad]", MODEL_STATUS)
+
+_try_load_model()  # load at import time
+
+def _canon_cat(v: Any) -> str:
+    """Stable, canonical category placeholder normalization."""
+    if v is None:
+        return CANON_NA
+    s = str(v).strip()
+    if s == "" or s.upper() in {"N/A", "NONE", "NULL"}:
+        return CANON_NA
+    return s
+
+def _to_float_or_nan(v):
+    if v in ("", None):
+        return np.nan
+    try:
+        return float(str(v).replace(",", ""))
+    except Exception:
+        return np.nan
+
+def _coerce_to_row(form_dict: dict) -> pd.DataFrame:
+    row = {}
+    for col in MAIN_VARIABLES:
+        v = form_dict.get(col, None)
+        if col in NUMERIC_COLS:
+            row[col] = _to_float_or_nan(v)
+        elif col in CATEGORICAL_COLS:
+            row[col] = _canon_cat(v)
+        else:
+            s = str(v).strip() if v is not None else ""
+            row[col] = s if s else CANON_NA
+    return pd.DataFrame([row], columns=MAIN_VARIABLES)
+
+def _align_columns_to_model(df: pd.DataFrame, mdl) -> pd.DataFrame:
+    """
+    SAFE alignment:
+    - If mdl.feature_names_in_ exists AND is a subset of df.columns (raw names), reorder to it.
+    - Else, try a Pipeline step (e.g., 'preprocessor') with feature_names_in_ subset of df.columns.
+    - Else, DO NOT align (let the pipeline handle columns by name).
+    """
+    try:
+        feat = getattr(mdl, "feature_names_in_", None)
+        if isinstance(feat, (list, np.ndarray, pd.Index)):
+            feat = list(feat)
+            if all(c in df.columns for c in feat):
+                return df[feat]
+
+        if hasattr(mdl, "named_steps"):
+            for key in ["preprocessor", "columntransformer"]:
+                if key in mdl.named_steps:
+                    step = mdl.named_steps[key]
+                    feat2 = getattr(step, "feature_names_in_", None)
+                    if isinstance(feat2, (list, np.ndarray, pd.Index)):
+                        feat2 = list(feat2)
+                        if all(c in df.columns for c in feat2):
+                            return df[feat2]
+            # fallback to first step if it exposes input names
+            try:
+                first_key = list(mdl.named_steps.keys())[0]
+                step = mdl.named_steps[first_key]
+                feat3 = getattr(step, "feature_names_in_", None)
+                if isinstance(feat3, (list, np.ndarray, pd.Index)):
+                    feat3 = list(feat3)
+                    if all(c in df.columns for c in feat3):
+                        return df[feat3]
+            except Exception:
+                pass
+
+        return df
+    except Exception as e:
+        print(f"[Align] Skip aligning due to: {e}")
+        traceback.print_exc()
+        return df
+
+def predict_fn(**kwargs):
+    """
+    Always attempt prediction.
+    - Missing numerics -> NaN (imputer handles)
+    - Categoricals -> 'NA'
+    - If model missing or inference error -> 0.0 (keeps UI stable)
+    """
+    if MODEL is None:
+        return 0.0
+    X_new = _coerce_to_row(kwargs)
+    X_new = _align_columns_to_model(X_new, MODEL)
+    try:
+        y_raw = MODEL.predict(X_new)  # log1p or original scale depending on training
+        if getattr(MODEL, "target_is_log1p_", False):
+            y = np.expm1(y_raw)
+        else:
+            y = y_raw
+        y = float(np.asarray(y).ravel()[0])
+        return max(y, 0.0)
+    except Exception as e:
+        print(f"[Predict] {e}")
+        traceback.print_exc()
+        return 0.0
+
+EXAMPLE = {
+    "Filler 1 Type": "CNT",
+    "Filler 1 Dimensionality": "1D",
+    "Filler 1 Diameter (µm)": 0.02,
+    "Filler 1 Length (mm)": 1.2,
+    CF_COL: 0.5,
+    "Filler 2 Type": "",
+    "Filler 2 Dimensionality": CANON_NA,
+    "Filler 2 Diameter (µm)": None,
+    "Filler 2 Length (mm)": None,
+    "Specimen Volume (mm3)": 1000,
+    "Probe Count": 2,
+    "Probe Material": "Copper",
+    "W/B": 0.4,
+    "S/B": 2.5,
+    "Gauge Length (mm)": 20,
+    "Curing Condition": "28d water, 20°C",
+    "Number of Fillers": 1,
+    "Drying Temperature (°C)": 60,
+    "Drying Duration (hr)": 24,
+    "Loading Rate (MPa/s)": 0.1,
+    "Modulus of Elasticity (GPa)": 25,
+    "Current Type": "DC",
+    "Applied Voltage (V)": 5.0,
+}
+
+def _fill_example():
+    return [EXAMPLE.get(k, None) for k in MAIN_VARIABLES]
+
+def _clear_all():
+    cleared = []
+    for col in MAIN_VARIABLES:
+        if col in NUMERIC_COLS:
+            cleared.append(None)
+        elif col in {"Filler 1 Dimensionality", "Filler 2 Dimensionality"}:
+            cleared.append(CANON_NA)
+        elif col == "Current Type":
+            cleared.append(CANON_NA)
+        else:
+            cleared.append("")
+    return cleared
+
+# ========================= Hybrid RAG =========================
+ARTIFACT_DIR    = Path("rag_artifacts"); ARTIFACT_DIR.mkdir(exist_ok=True)
+TFIDF_VECT_PATH = ARTIFACT_DIR / "tfidf_vectorizer.joblib"
+TFIDF_MAT_PATH  = ARTIFACT_DIR / "tfidf_matrix.joblib"
+BM25_TOK_PATH   = ARTIFACT_DIR / "bm25_tokens.joblib"
+EMB_NPY_PATH    = ARTIFACT_DIR / "chunk_embeddings.npy"
+RAG_META_PATH   = ARTIFACT_DIR / "chunks.parquet"
+
+LOCAL_PDF_DIR = Path("papers"); LOCAL_PDF_DIR.mkdir(exist_ok=True)
+USE_ONLINE_SOURCES = os.getenv("USE_ONLINE_SOURCES", "false").lower() == "true"
+
+W_TFIDF_DEFAULT = 0.50 if not USE_DENSE else 0.30
+W_BM25_DEFAULT  = 0.50 if not USE_DENSE else 0.30
+W_EMB_DEFAULT   = 0.00 if USE_DENSE is False else 0.40
+
+_SENT_SPLIT_RE = re.compile(r"(?<=[.!?])\s+|\n+")
+TOKEN_RE       = re.compile(r"[A-Za-z0-9_#+\-/\.%]+")
+def sent_split(text: str) -> List[str]:
+    sents = [s.strip() for s in _SENT_SPLIT_RE.split(text) if s.strip()]
+    return [s for s in sents if len(s.split()) >= 5]
+def tokenize(text: str) -> List[str]:
+    return [t.lower() for t in TOKEN_RE.findall(text)]
+
+def _extract_pdf_text(pdf_path: Path) -> str:
+    try:
+        import fitz
+        doc = fitz.open(pdf_path)
+        out = []
+        for i, page in enumerate(doc):
+            out.append(f"[[PAGE={i+1}]]\n{page.get_text('text') or ''}")
+        return "\n\n".join(out)
+    except Exception:
+        try:
+            from pypdf import PdfReader
+            reader = PdfReader(str(pdf_path))
+            out = []
+            for i, p in enumerate(reader.pages):
+                txt = p.extract_text() or ""
+                out.append(f"[[PAGE={i+1}]]\n{txt}")
+            return "\n\n".join(out)
+        except Exception as e:
+            print(f"PDF read error ({pdf_path}): {e}")
+            return ""
+
+def chunk_by_sentence_windows(text: str, win_size=8, overlap=2) -> List[str]:
+    sents = sent_split(text)
+    chunks, step = [], max(1, win_size - overlap)
+    for i in range(0, len(sents), step):
+        window = sents[i:i+win_size]
+        if not window: break
+        chunks.append(" ".join(window))
+    return chunks
+
+def _safe_init_st_model(name: str):
+    global USE_DENSE
+    if not USE_DENSE:
+        return None
+    try:
+        return SentenceTransformer(name)
+    except Exception as e:
+        print("Dense embeddings unavailable:", e)
+        USE_DENSE = False
+        return None
+
+def build_or_load_hybrid(pdf_dir: Path):
+    # Build or load the hybrid retriever cache
+    have_cache = (TFIDF_VECT_PATH.exists() and TFIDF_MAT_PATH.exists()
+                  and RAG_META_PATH.exists()
+                  and (BM25_TOK_PATH.exists() or BM25Okapi is None)
+                  and (EMB_NPY_PATH.exists() or not USE_DENSE))
+    if have_cache:
+        vectorizer = joblib.load(TFIDF_VECT_PATH)
+        X_tfidf    = joblib.load(TFIDF_MAT_PATH)
+        meta       = pd.read_parquet(RAG_META_PATH)
+        bm25_toks  = joblib.load(BM25_TOK_PATH) if BM25Okapi is not None else None
+        emb        = np.load(EMB_NPY_PATH) if (USE_DENSE and EMB_NPY_PATH.exists()) else None
+        return vectorizer, X_tfidf, meta, bm25_toks, emb
+
+    rows, all_tokens = [], []
+    pdf_paths = list(Path(pdf_dir).glob("**/*.pdf"))
+    print(f"Indexing PDFs in {pdf_dir} — found {len(pdf_paths)} files.")
+    for pdf in pdf_paths:
+        raw = _extract_pdf_text(pdf)
+        if not raw.strip():
+            continue
+        for i, ch in enumerate(chunk_by_sentence_windows(raw, win_size=8, overlap=2)):
+            rows.append({"doc_path": str(pdf), "chunk_id": i, "text": ch})
+            all_tokens.append(tokenize(ch))
+    if not rows:
+        meta = pd.DataFrame(columns=["doc_path", "chunk_id", "text"])
+        vectorizer = None; X_tfidf = None; emb = None; all_tokens = None
+        return vectorizer, X_tfidf, meta, all_tokens, emb
+
+    meta = pd.DataFrame(rows)
+    from sklearn.feature_extraction.text import TfidfVectorizer
+    vectorizer = TfidfVectorizer(
+        ngram_range=(1,2),
+        min_df=1, max_df=0.95,
+        sublinear_tf=True, smooth_idf=True,
+        lowercase=True,
+        token_pattern=r"(?u)\b\w[\w\-\./%+#]*\b"
+    )
+    X_tfidf = vectorizer.fit_transform(meta["text"].tolist())
+
+    emb = None
+    if USE_DENSE:
+        try:
+            st_model = _safe_init_st_model(os.getenv("EMB_MODEL_NAME", "sentence-transformers/all-MiniLM-L6-v2"))
+            if st_model is not None:
+                from sklearn.preprocessing import normalize as sk_normalize
+                em = st_model.encode(meta["text"].tolist(), batch_size=64, show_progress_bar=False, convert_to_numpy=True)
+                emb = sk_normalize(em)
+                np.save(EMB_NPY_PATH, emb)
+        except Exception as e:
+            print("Dense embedding failed:", e)
+            emb = None
+
+    joblib.dump(vectorizer, TFIDF_VECT_PATH)
+    joblib.dump(X_tfidf, TFIDF_MAT_PATH)
+    if BM25Okapi is not None:
+        joblib.dump(all_tokens, BM25_TOK_PATH)
+    meta.to_parquet(RAG_META_PATH, index=False)
+    return vectorizer, X_tfidf, meta, all_tokens, emb
+
+tfidf_vectorizer, tfidf_matrix, rag_meta, bm25_tokens, emb_matrix = build_or_load_hybrid(LOCAL_PDF_DIR)
+bm25 = BM25Okapi(bm25_tokens) if (BM25Okapi is not None and bm25_tokens is not None) else None
+st_query_model = _safe_init_st_model(os.getenv("EMB_MODEL_NAME", "sentence-transformers/all-MiniLM-L6-v2"))
+
+def _extract_page(text_chunk: str) -> str:
+    # Correct: [[PAGE=123]]
+    m = list(re.finditer(r"\[\[PAGE=(\d+)\]\]", text_chunk or ""))
+    return (m[-1].group(1) if m else "?")
+
+def hybrid_search(query: str, k=8, w_tfidf=W_TFIDF_DEFAULT, w_bm25=W_BM25_DEFAULT, w_emb=W_EMB_DEFAULT):
+    if rag_meta is None or rag_meta.empty:
+        return pd.DataFrame()
+
+    # Dense scores
+    if USE_DENSE and st_query_model is not None and emb_matrix is not None and w_emb > 0:
+        try:
+            from sklearn.preprocessing import normalize as sk_normalize
+            q_emb = st_query_model.encode([query], convert_to_numpy=True)
+            q_emb = sk_normalize(q_emb)[0]
+            dense_scores = emb_matrix @ q_emb
+        except Exception as e:
+            print("Dense query encoding failed:", e)
+            dense_scores = np.zeros(len(rag_meta), dtype=float); w_emb = 0.0
+    else:
+        dense_scores = np.zeros(len(rag_meta), dtype=float); w_emb = 0.0
+
+    # TF-IDF scores
+    if tfidf_vectorizer is not None and tfidf_matrix is not None:
+        q_vec = tfidf_vectorizer.transform([query])
+        tfidf_scores = (tfidf_matrix @ q_vec.T).toarray().ravel()
+    else:
+        tfidf_scores = np.zeros(len(rag_meta), dtype=float); w_tfidf = 0.0
+
+    # BM25 scores
+    if bm25 is not None:
+        q_tokens = [t.lower() for t in re.findall(r"[A-Za-z0-9_#+\-\/\.%]+", query)]
+        bm25_scores = np.array(bm25.get_scores(q_tokens), dtype=float)
+    else:
+        bm25_scores = np.zeros(len(rag_meta), dtype=float); w_bm25 = 0.0
+
+    def _norm(x):
+        x = np.asarray(x, dtype=float)
+        if np.allclose(x.max(), x.min()):
+            return np.zeros_like(x)
+        return (x - x.min()) / (x.max() - x.min())
+
+    s_dense = _norm(dense_scores)
+    s_tfidf = _norm(tfidf_scores)
+    s_bm25  = _norm(bm25_scores)
+
+    total_w = (w_tfidf + w_bm25 + w_emb) or 1.0
+    w_tfidf, w_bm25, w_emb = w_tfidf/total_w, w_bm25/total_w, w_emb/total_w
+
+    combo = w_emb * s_dense + w_tfidf * s_tfidf + w_bm25 * s_bm25
+    idx = np.argsort(-combo)[:k]
+    hits = rag_meta.iloc[idx].copy()
+    hits["score_dense"] = s_dense[idx]
+    hits["score_tfidf"] = s_tfidf[idx]
+    hits["score_bm25"]  = s_bm25[idx]
+    hits["score"]       = combo[idx]
+    return hits.reset_index(drop=True)
+
+def split_sentences(text: str) -> List[str]:
+    sents = sent_split(text)
+    return [s for s in sents if 6 <= len(s.split()) <= 60]
+
+def mmr_select_sentences(question: str, hits: pd.DataFrame, top_n=4, pool_per_chunk=6, lambda_div=0.7):
+    """
+    Robust MMR sentence picker:
+    - Handles empty pools
+    - Clamps top_n to pool size
+    - Avoids 'list index out of range'
+    """
+    # Build pool
+    pool = []
+    for _, row in hits.iterrows():
+        doc  = Path(row["doc_path"]).name
+        page = _extract_page(row["text"])
+        sents = split_sentences(row["text"])
+        if not sents:
+            continue
+        for s in sents[:max(1, int(pool_per_chunk))]:
+            pool.append({"sent": s, "doc": doc, "page": page})
+
+    if not pool:
+        return []
+
+    # Relevance vectors
+    sent_texts = [p["sent"] for p in pool]
+    use_dense = USE_DENSE and st_query_model is not None
+    try:
+        if use_dense:
+            from sklearn.preprocessing import normalize as sk_normalize
+            enc = st_query_model.encode([question] + sent_texts, convert_to_numpy=True)
+            q_vec = sk_normalize(enc[:1])[0]
+            S     = sk_normalize(enc[1:])
+            rel   = (S @ q_vec)
+            def sim_fn(i, j): return float(S[i] @ S[j])
+        else:
+            from sklearn.feature_extraction.text import TfidfVectorizer
+            vect = TfidfVectorizer().fit(sent_texts + [question])
+            Q = vect.transform([question]); S = vect.transform(sent_texts)
+            rel = (S @ Q.T).toarray().ravel()
+            def sim_fn(i, j):
+                num = (S[i] @ S[j].T)
+                return float(num.toarray()[0, 0]) if hasattr(num, "toarray") else float(num)
+    except Exception:
+        # Fallback: uniform relevance if vectorization fails
+        rel = np.ones(len(sent_texts), dtype=float)
+        def sim_fn(i, j): return 0.0
+
+    # Normalize lambda_div
+    lambda_div = float(np.clip(lambda_div, 0.0, 1.0))
+
+    # Select first by highest relevance
+    remain = list(range(len(pool)))
+    if not remain:
+        return []
+    first = int(np.argmax(rel))
+    selected_idx = [first]
+    selected     = [pool[first]]
+    remain.remove(first)
+
+    # Clamp top_n
+    max_pick = min(int(top_n), len(pool))
+    while len(selected) < max_pick and remain:
+        cand_scores = []
+        for i in remain:
+            div_i = max(sim_fn(i, j) for j in selected_idx) if selected_idx else 0.0
+            score = lambda_div * float(rel[i]) - (1.0 - lambda_div) * div_i
+            cand_scores.append((score, i))
+        if not cand_scores:
+            break
+        cand_scores.sort(reverse=True)
+        _, best_i = cand_scores[0]
+        selected_idx.append(best_i)
+        selected.append(pool[best_i])
+        remain.remove(best_i)
+
+    return selected
+
+def compose_extractive(selected: List[Dict[str, Any]]) -> str:
+    if not selected:
+        return ""
+    return " ".join(f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected)
+
+def synthesize_with_llm(question: str, sentence_lines: List[str], model: str = None, temperature: float = 0.2) -> str:
+    if not LLM_AVAILABLE:
+        return None
+    client = OpenAI(api_key=OPENAI_API_KEY)
+    model = model or OPENAI_MODEL
+    SYSTEM_PROMPT = (
+        "You are a scientific assistant for self-sensing cementitious materials.\n"
+        "Answer STRICTLY using the provided sentences.\n"
+        "Do not invent facts. Keep it concise (3–6 sentences).\n"
+        "Retain inline citations like (Doc.pdf, p.X) exactly as given."
+    )
+    user_prompt = (
+        f"Question: {question}\n\n"
+        f"Use ONLY these sentences to answer; keep their inline citations:\n" +
+        "\n".join(f"- {s}" for s in sentence_lines)
+    )
+    try:
+        resp = client.responses.create(
+            model=model,
+            input=[
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": user_prompt},
+            ],
+            temperature=temperature,
+        )
+        return getattr(resp, "output_text", None) or str(resp)
+    except Exception:
+        return None
+
+def rag_reply(
+    question: str,
+    k: int = 8,
+    n_sentences: int = 4,
+    include_passages: bool = False,
+    use_llm: bool = False,
+    model: str = None,
+    temperature: float = 0.2,
+    strict_quotes_only: bool = False,
+    w_tfidf: float = W_TFIDF_DEFAULT,
+    w_bm25: float  = W_BM25_DEFAULT,
+    w_emb: float   = W_EMB_DEFAULT
+) -> str:
+    hits = hybrid_search(question, k=k, w_tfidf=w_tfidf, w_bm25=w_bm25, w_emb=w_emb)
+    if hits is None or hits.empty:
+        return "No indexed PDFs found. Upload PDFs to the 'papers/' folder and reload the Space."
+
+    selected = mmr_select_sentences(question, hits, top_n=int(n_sentences), pool_per_chunk=6, lambda_div=0.7)
+    header_cites = "; ".join(f"{Path(r['doc_path']).name} (p.{_extract_page(r['text'])})" for _, r in hits.head(6).iterrows())
+    srcs = {Path(r['doc_path']).name for _, r in hits.iterrows()}
+    coverage_note = "" if len(srcs) >= 3 else f"\n\n> Note: Only {len(srcs)} unique source(s) contributed. Add more PDFs or increase Top-K."
+
+    if strict_quotes_only:
+        if not selected:
+            return f"**Quoted Passages:**\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2]) + f"\n\n**Citations:** {header_cites}{coverage_note}"
+        msg = "**Quoted Passages:**\n- " + "\n- ".join(f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected)
+        msg += f"\n\n**Citations:** {header_cites}{coverage_note}"
+        if include_passages:
+            msg += "\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
+        return msg
+
+    extractive = compose_extractive(selected)
+    if use_llm and selected:
+        lines = [f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected]
+        llm_text = synthesize_with_llm(question, lines, model=model, temperature=temperature)
+        if llm_text:
+            msg = f"**Answer (LLM synthesis):** {llm_text}\n\n**Citations:** {header_cites}{coverage_note}"
+            if include_passages:
+                msg += "\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
+            return msg
+
+    if not extractive:
+        return f"**Answer:** Here are relevant passages.\n\n**Citations:** {header_cites}{coverage_note}\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
+
+    msg = f"**Answer:** {extractive}\n\n**Citations:** {header_cites}{coverage_note}"
+    if include_passages:
+        msg += "\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
+    return msg
+
+def rag_chat_fn(message, history, top_k, n_sentences, include_passages,
+                use_llm, model_name, temperature, strict_quotes_only,
+                w_tfidf, w_bm25, w_emb):
+    if not message or not message.strip():
+        return "Ask a literature question (e.g., *How does CNT length affect gauge factor?*)"
+    try:
+        return rag_reply(
+            question=message,
+            k=int(top_k),
+            n_sentences=int(n_sentences),
+            include_passages=bool(include_passages),
+            use_llm=bool(use_llm),
+            model=(model_name or None),
+            temperature=float(temperature),
+            strict_quotes_only=bool(strict_quotes_only),
+            w_tfidf=float(w_tfidf),
+            w_bm25=float(w_bm25),
+            w_emb=float(w_emb),
+        )
+    except Exception as e:
+        return f"RAG error: {e}"
+
+# ========================= UI (science-oriented styling) =========================
+CSS = """
+/* Science-oriented: crisp contrast + readable numerics */
+* {font-family: ui-sans-serif, system-ui, -apple-system, 'Segoe UI', Roboto, 'Helvetica Neue', Arial;}
+.gradio-container {
+    background: linear-gradient(135deg, #0b1020 0%, #0c2b1a 60%, #0a2b4d 100%) !important;
+}
+.card {background: rgba(255,255,255,0.06) !important; border: 1px solid rgba(255,255,255,0.14); border-radius: 12px;}
+label {color: #e8f7ff !important; text-shadow: 0 1px 0 rgba(0,0,0,0.35); cursor: pointer;}
+input[type="number"] {font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", monospace;}
+
+/* Checkbox clickability fixes */
+input[type="checkbox"], .gr-checkbox, .gr-checkbox > * { pointer-events: auto !important; }
+.gr-checkbox label, .gr-check-radio label { pointer-events: auto !important; cursor: pointer; }
+#rag-tab input[type="checkbox"] { accent-color: #60a5fa !important; }
+
+/* RAG tab background and elements */
+#rag-tab .block, #rag-tab .group, #rag-tab .accordion {
+    background: linear-gradient(160deg, #1f2937 0%, #14532d 55%, #0b3b68 100%) !important;
+    border-radius: 12px;
+    border: 1px solid rgba(255,255,255,0.14);
+}
+#rag-tab input, #rag-tab textarea, #rag-tab select, #rag-tab .scroll-hide, #rag-tab .chatbot textarea {
+    background: rgba(17, 24, 39, 0.85) !important;
+    border: 1px solid #60a5fa !important;
+    color: #e5f2ff !important;
+}
+#rag-tab input[type="range"] { accent-color: #22c55e !important; }
+#rag-tab button { border-radius: 10px !important; font-weight: 600 !important; }
+#rag-tab .chatbot {
+    background: rgba(15, 23, 42, 0.6) !important;
+    border: 1px solid rgba(148, 163, 184, 0.35) !important;
+}
+#rag-tab .message.user {
+    background: rgba(34, 197, 94, 0.15) !important;
+    border-left: 3px solid #22c55e !important;
+}
+#rag-tab .message.bot {
+    background: rgba(59, 130, 246, 0.15) !important;
+    border-left: 3px solid #60a5fa !important;
+    color: #eef6ff !important;
+}
+
+/* Predictor output emphasis */
+#pred-out .wrap { font-size: 20px; font-weight: 700; color: #ecfdf5; }
+"""
+
+theme = gr.themes.Soft(
+    primary_hue="blue",
+    neutral_hue="green"
+).set(
+    body_background_fill="#0b1020",
+    body_text_color="#e0f2fe",
+    input_background_fill="#0f172a",
+    input_border_color="#1e40af",
+    button_primary_background_fill="#2563eb",
+    button_primary_text_color="#ffffff",
+    button_secondary_background_fill="#14532d",
+    button_secondary_text_color="#ecfdf5",
+)
+
+with gr.Blocks(css=CSS, theme=theme, fill_height=True) as demo:
+    gr.Markdown(
+        "<h1 style='margin:0'>Self-Sensing Concrete Assistant</h1>"
+        "<p style='opacity:.9'>"
+        "Left: ML prediction for Stress Gauge Factor (original scale, MPa<sup>-1</sup>). "
+        "Right: Literature Q&A via Hybrid RAG (BM25 + TF-IDF + optional dense) with MMR sentence selection."
+        "</p>"
+    )
+
+    with gr.Tabs():
+        # ------------------------- Predictor Tab -------------------------
+        with gr.Tab("🔮 Predict Gauge Factor (XGB)"):
+            with gr.Row():
+                with gr.Column(scale=7):
+                    with gr.Accordion("Primary conductive filler", open=True, elem_classes=["card"]):
+                        f1_type = gr.Textbox(label="Filler 1 Type *", placeholder="e.g., CNT, Graphite, Steel fiber")
+                        f1_diam = gr.Number(label="Filler 1 Diameter (µm) *")
+                        f1_len  = gr.Number(label="Filler 1 Length (mm) *")
+                        cf_conc = gr.Number(label=f"{CF_COL} *", info="Weight percent of total binder")
+                        f1_dim  = gr.Dropdown(DIM_CHOICES, value=CANON_NA, label="Filler 1 Dimensionality *")
+
+                    with gr.Accordion("Secondary filler (optional)", open=False, elem_classes=["card"]):
+                        f2_type = gr.Textbox(label="Filler 2 Type", placeholder="Optional")
+                        f2_diam = gr.Number(label="Filler 2 Diameter (µm)")
+                        f2_len  = gr.Number(label="Filler 2 Length (mm)")
+                        f2_dim  = gr.Dropdown(DIM_CHOICES, value=CANON_NA, label="Filler 2 Dimensionality")
+
+                    with gr.Accordion("Mix design & specimen", open=False, elem_classes=["card"]):
+                        spec_vol  = gr.Number(label="Specimen Volume (mm3) *")
+                        probe_cnt = gr.Number(label="Probe Count *")
+                        probe_mat = gr.Textbox(label="Probe Material *", placeholder="e.g., Copper, Silver paste")
+                        wb        = gr.Number(label="W/B *")
+                        sb        = gr.Number(label="S/B *")
+                        gauge_len = gr.Number(label="Gauge Length (mm) *")
+                        curing    = gr.Textbox(label="Curing Condition *", placeholder="e.g., 28d water, 20°C")
+                        n_fillers = gr.Number(label="Number of Fillers *")
+
+                    with gr.Accordion("Processing", open=False, elem_classes=["card"]):
+                        dry_temp = gr.Number(label="Drying Temperature (°C)")
+                        dry_hrs  = gr.Number(label="Drying Duration (hr)")
+
+                    with gr.Accordion("Mechanical & electrical loading", open=False, elem_classes=["card"]):
+                        load_rate = gr.Number(label="Loading Rate (MPa/s)")
+                        E_mod     = gr.Number(label="Modulus of Elasticity (GPa) *")
+                        current   = gr.Dropdown(CURRENT_CHOICES, value=CANON_NA, label="Current Type")
+                        voltage   = gr.Number(label="Applied Voltage (V)")
+
+                with gr.Column(scale=5):
+                    with gr.Group(elem_classes=["card"]):
+                        out_pred = gr.Number(label="Predicted Stress GF (MPa-1)", value=0.0, precision=6, elem_id="pred-out")
+                        gr.Markdown(f"<small>{MODEL_STATUS}</small>")
+                        with gr.Row():
+                            btn_pred  = gr.Button("Predict", variant="primary")
+                            btn_clear = gr.Button("Clear")
+                            btn_demo  = gr.Button("Fill Example")
+
+                    with gr.Accordion("About this model", open=False, elem_classes=["card"]):
+                        gr.Markdown(
+                            "- Pipeline: ColumnTransformer → (RobustScaler + OneHot) → XGBoost\n"
+                            "- Target: Stress GF (MPa<sup>-1</sup>) on original scale (model may train on log1p; saved flag used at inference).\n"
+                            "- Missing values are safely imputed per-feature.\n"
+                            "- Trained columns:\n"
+                            f"  `{', '.join(MAIN_VARIABLES)}`",
+                            elem_classes=["prose"]
+                        )
+
+            inputs_in_order = [
+                f1_type, f1_diam, f1_len, cf_conc,
+                f1_dim,  f2_type, f2_diam, f2_len,
+                f2_dim,  spec_vol, probe_cnt, probe_mat,
+                wb, sb, gauge_len, curing, n_fillers,
+                dry_temp, dry_hrs, load_rate,
+                E_mod, current, voltage
+            ]
+
+            def _predict_wrapper(*vals):
+                data = {k: v for k, v in zip(MAIN_VARIABLES, vals)}
+                return predict_fn(**data)
+
+            btn_pred.click(_predict_wrapper, inputs=inputs_in_order, outputs=out_pred)
+            btn_clear.click(lambda: _clear_all(), inputs=None, outputs=inputs_in_order).then(lambda: 0.0, outputs=out_pred)
+            btn_demo.click(lambda: _fill_example(), inputs=None, outputs=inputs_in_order)
+
+        # ------------------------- Literature Tab -------------------------
+        with gr.Tab("📚 Ask the Literature (Hybrid RAG + MMR)", elem_id="rag-tab"):
+            pdf_count = len(list(LOCAL_PDF_DIR.glob("**/*.pdf")))
+            gr.Markdown(
+                f"Using local folder <code>papers/</code> — **{pdf_count} PDF(s)** indexed. "
+                "Upload more PDFs and reload the Space to expand coverage. Answers cite (Doc.pdf, p.X)."
+            )
+            with gr.Row():
+                top_k = gr.Slider(5, 12, value=8, step=1, label="Top-K chunks")
+                n_sentences = gr.Slider(2, 6, value=4, step=1, label="Answer length (sentences)")
+                include_passages = gr.Checkbox(value=False, label="Include supporting passages", interactive=True)
+
+            with gr.Accordion("Retriever weights (advanced)", open=False):
+                w_tfidf = gr.Slider(0.0, 1.0, value=W_TFIDF_DEFAULT, step=0.05, label="TF-IDF weight")
+                w_bm25  = gr.Slider(0.0, 1.0, value=W_BM25_DEFAULT,  step=0.05, label="BM25 weight")
+                w_emb   = gr.Slider(0.0, 1.0, value=(0.0 if not USE_DENSE else 0.40), step=0.05, label="Dense weight (set 0 if disabled)")
+
+            # Hidden states (unchanged)
+            state_use_llm     = gr.State(LLM_AVAILABLE)
+            state_model_name  = gr.State(os.getenv("OPENAI_MODEL", OPENAI_MODEL))
+            state_temperature = gr.State(0.2)
+            state_strict      = gr.State(False)
+
+            gr.ChatInterface(
+                fn=rag_chat_fn,
+                additional_inputs=[
+                    top_k, n_sentences, include_passages,
+                    state_use_llm, state_model_name, state_temperature, state_strict,
+                    w_tfidf, w_bm25, w_emb
+                ],
+                title="Literature Q&A",
+                description="Hybrid retrieval with diversity. Answers carry inline (Doc, p.X) citations."
+            )
+
+# ------------- Launch -------------
+if __name__ == "__main__":
+    demo.queue().launch()