Update app.py

app.py

@@ -1,7 +1,24 @@
-# ===================== app.py =====================
-# RAG core + logging + grid eval + Gradio UI wrapper
-
-import os, re, json, time, uuid, traceback
+# ================================================================
+# 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
+# - NEW: Lightweight instrumentation (JSONL logs per RAG turn)
+# - UPDATED THEME: Dark-blue tabs + Evaluate tab + k-slider styling
+# - PATCH: Per-question/aggregate File + JSON outputs now dark-themed via elem_id hooks
+# - OPTIONAL JS: Adds .eval-active class when Evaluate tab is selected
+# ================================================================
+
+# ---------------------- 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, time, uuid, subprocess, sys
 from pathlib import Path
 from typing import List, Dict, Any, Optional
 
@@ -9,12 +26,9 @@ import numpy as np
 import pandas as pd
 import gradio as gr
 
-from rag_eval_metrics import evaluate_rag  # must exist as rag_eval_metrics.py
-
+warnings.filterwarnings("ignore", category=UserWarning)
 
-# ===================== RAG CORE + LOGGING + GRID EVAL =====================
-
-# Optional dense + BM25
+# Optional deps (handled gracefully if missing)
 USE_DENSE = True
 try:
     from sentence_transformers import SentenceTransformer
@@ -27,7 +41,7 @@ except Exception:
     BM25Okapi = None
     print("rank_bm25 not installed; BM25 disabled (TF-IDF still works).")
 
-# Optional OpenAI (for LLM synthesis, not needed for grid search)
+# Optional OpenAI (for LLM paraphrase)
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 OPENAI_MODEL   = os.getenv("OPENAI_MODEL", "gpt-5")
 try:
@@ -35,44 +49,265 @@ try:
 except Exception:
     OpenAI = None
 
-LLM_AVAILABLE = (
-    OPENAI_API_KEY is not None and OPENAI_API_KEY.strip() != "" and OpenAI is not 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)
 
-# -------------------------- Paths & artifacts ---------------------------
-ARTIFACT_DIR    = Path("rag_artifacts"); ARTIFACT_DIR.mkdir(exist_ok=True)
-LOCAL_PDF_DIR   = Path("papers"); LOCAL_PDF_DIR.mkdir(exist_ok=True)
+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"
 
-LOG_PATH = ARTIFACT_DIR / "rag_logs.jsonl"
-
+LOCAL_PDF_DIR = Path("papers"); LOCAL_PDF_DIR.mkdir(exist_ok=True)
 USE_ONLINE_SOURCES = os.getenv("USE_ONLINE_SOURCES", "false").lower() == "true"
 
-# default hybrid weights
 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 not USE_DENSE else 0.40
+W_EMB_DEFAULT   = 0.00 if USE_DENSE is False else 0.40
 
-# ---------- basic text helpers ----------
 _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)]
 
-# ---------- PDF text extraction ----------
 def _extract_pdf_text(pdf_path: Path) -> str:
     try:
-        import fitz  # PyMuPDF
+        import fitz
         doc = fitz.open(pdf_path)
         out = []
         for i, page in enumerate(doc):
@@ -96,12 +331,10 @@ def chunk_by_sentence_windows(text: str, win_size=8, overlap=2) -> List[str]:
     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
+        if not window: break
         chunks.append(" ".join(window))
     return chunks
 
-# ---------- dense encoder ----------
 def _safe_init_st_model(name: str):
     global USE_DENSE
     if not USE_DENSE:
@@ -113,19 +346,12 @@ def _safe_init_st_model(name: str):
         USE_DENSE = False
         return None
 
-# ---------- build / load hybrid index ----------
 def build_or_load_hybrid(pdf_dir: Path):
-    from sklearn.feature_extraction.text import TfidfVectorizer
-    import joblib
-
-    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)
-    )
-
+    # 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)
@@ -135,8 +361,8 @@ def build_or_load_hybrid(pdf_dir: Path):
         return vectorizer, X_tfidf, meta, bm25_toks, emb
 
     rows, all_tokens = [], []
-    pdf_paths = list(pdf_dir.glob("**/*.pdf"))
-    print(f"Indexing PDFs in {pdf_dir} — found {len(pdf_paths)} file(s).")
+    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():
@@ -144,79 +370,56 @@ def build_or_load_hybrid(pdf_dir: Path):
         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"])
-        return None, None, meta, None, None
+        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,
+        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",
+        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")
-            )
+            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,
-                )
+                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
 
-    import joblib
     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
-)
+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")
-)
+st_query_model = _safe_init_st_model(os.getenv("EMB_MODEL_NAME", "sentence-transformers/all-MiniLM-L6-v2"))
 
-# ---------- hybrid retrieval ----------
 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 "?"
+    return (m[-1].group(1) if m else "?")
 
-def hybrid_search(
-    query: str,
-    k: int = 8,
-    w_tfidf: float = W_TFIDF_DEFAULT,
-    w_bm25: float = W_BM25_DEFAULT,
-    w_emb: float = W_EMB_DEFAULT,
-) -> pd.DataFrame:
+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()
 
-    n_chunks = len(rag_meta)
-
-    # dense scores
+    # 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
@@ -225,23 +428,23 @@ def hybrid_search(
             dense_scores = emb_matrix @ q_emb
         except Exception as e:
             print("Dense query encoding failed:", e)
-            dense_scores = np.zeros(n_chunks); w_emb = 0.0
+            dense_scores = np.zeros(len(rag_meta), dtype=float); w_emb = 0.0
     else:
-        dense_scores = np.zeros(n_chunks); w_emb = 0.0
+        dense_scores = np.zeros(len(rag_meta), dtype=float); w_emb = 0.0
 
-    # tf-idf
+    # 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(n_chunks); w_tfidf = 0.0
+        tfidf_scores = np.zeros(len(rag_meta), dtype=float); w_tfidf = 0.0
 
-    # bm25
+    # BM25 scores
     if bm25 is not None:
-        q_tokens = [t.lower() for t in TOKEN_RE.findall(query)]
+        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(n_chunks); w_bm25 = 0.0
+        bm25_scores = np.zeros(len(rag_meta), dtype=float); w_bm25 = 0.0
 
     def _norm(x):
         x = np.asarray(x, dtype=float)
@@ -254,11 +457,10 @@ def hybrid_search(
     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
+    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]
@@ -266,18 +468,18 @@ def hybrid_search(
     hits["score"]       = combo[idx]
     return hits.reset_index(drop=True)
 
-# ---------- MMR sentence selection ----------
 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: int = 4,
-    pool_per_chunk: int = 6,
-    lambda_div: float = 0.7,
-) -> List[Dict[str, Any]]:
+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
@@ -287,19 +489,20 @@ def mmr_select_sentences(
             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
+            rel   = (S @ q_vec)
             def sim_fn(i, j): return float(S[i] @ S[j])
         else:
             from sklearn.feature_extraction.text import TfidfVectorizer
@@ -310,17 +513,23 @@ def mmr_select_sentences(
                 num = (S[i] @ S[j].T)
                 return float(num.toarray()[0, 0]) if hasattr(num, "toarray") else float(num)
     except Exception:
-        rel = np.ones(len(sent_texts))
+        # 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 = []
@@ -328,6 +537,8 @@ def mmr_select_sentences(
             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)
@@ -341,7 +552,8 @@ def compose_extractive(selected: List[Dict[str, Any]]) -> str:
         return ""
     return " ".join(f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected)
 
-# ---------- logging helpers ----------
+# ========================= NEW: Instrumentation helpers =========================
+LOG_PATH = ARTIFACT_DIR / "rag_logs.jsonl"
 OPENAI_IN_COST_PER_1K  = float(os.getenv("OPENAI_COST_IN_PER_1K", "0"))
 OPENAI_OUT_COST_PER_1K = float(os.getenv("OPENAI_COST_OUT_PER_1K", "0"))
 
@@ -355,22 +567,14 @@ def _safe_write_jsonl(path: Path, record: dict):
 def _calc_cost_usd(prompt_toks, completion_toks):
     if prompt_toks is None or completion_toks is None:
         return None
-    return (prompt_toks / 1000.0) * OPENAI_IN_COST_PER_1K + (
-        completion_toks / 1000.0
-    ) * OPENAI_OUT_COST_PER_1K
+    return (prompt_toks / 1000.0) * OPENAI_IN_COST_PER_1K + (completion_toks / 1000.0) * OPENAI_OUT_COST_PER_1K
 
-# ---------- optional LLM synthesis ----------
-def synthesize_with_llm(
-    question: str,
-    sentence_lines: List[str],
-    model: Optional[str] = None,
-    temperature: float = 0.2,
-):
+# ----------------- Modified to return (text, usage_dict) -----------------
+def synthesize_with_llm(question: str, sentence_lines: List[str], model: str = None, temperature: float = 0.2):
     if not LLM_AVAILABLE:
         return None, 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"
@@ -379,10 +583,9 @@ def synthesize_with_llm(
     )
     user_prompt = (
         f"Question: {question}\n\n"
-        "Use ONLY these sentences to answer; keep their inline citations:\n"
-        + "\n".join(f"- {s}" for s in sentence_lines)
+        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,
@@ -406,183 +609,124 @@ def synthesize_with_llm(
     except Exception:
         return None, None
 
-# ---------- main RAG reply (with config_id) ----------
 def rag_reply(
     question: str,
     k: int = 8,
     n_sentences: int = 4,
     include_passages: bool = False,
     use_llm: bool = False,
-    model: Optional[str] = None,
+    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,
-    config_id: Optional[str] = None,
+    w_bm25: float  = W_BM25_DEFAULT,
+    w_emb: float   = W_EMB_DEFAULT
 ) -> str:
     run_id = str(uuid.uuid4())
     t0_total = time.time()
     t0_retr  = time.time()
 
-    hits = hybrid_search(
-        question,
-        k=int(k),
-        w_tfidf=float(w_tfidf),
-        w_bm25=float(w_bm25),
-        w_emb=float(w_emb),
-    )
+    # --- Retrieval ---
+    hits = hybrid_search(question, k=k, w_tfidf=w_tfidf, w_bm25=w_bm25, w_emb=w_emb)
     t1_retr = time.time()
     latency_ms_retriever = int((t1_retr - t0_retr) * 1000)
 
     if hits is None or hits.empty:
-        final = "No indexed PDFs found."
+        final = "No indexed PDFs found. Upload PDFs to the 'papers/' folder and reload the Space."
         record = {
             "run_id": run_id,
-            "ts": int(time.time() * 1000),
+            "ts": int(time.time()*1000),
             "inputs": {
-                "question": question,
-                "top_k": int(k),
-                "n_sentences": int(n_sentences),
-                "w_tfidf": float(w_tfidf),
-                "w_bm25": float(w_bm25),
-                "w_emb": float(w_emb),
-                "use_llm": bool(use_llm),
-                "model": model,
-                "temperature": float(temperature),
-                "config_id": config_id,
+                "question": question, "top_k": int(k), "n_sentences": int(n_sentences),
+                "w_tfidf": float(w_tfidf), "w_bm25": float(w_bm25), "w_emb": float(w_emb),
+                "use_llm": bool(use_llm), "model": model, "temperature": float(temperature)
             },
             "retrieval": {"hits": [], "latency_ms_retriever": latency_ms_retriever},
             "output": {"final_answer": final, "used_sentences": []},
-            "latency_ms_total": int((time.time() - t0_total) * 1000),
-            "openai": None,
+            "latency_ms_total": int((time.time()-t0_total)*1000),
+            "openai": None
         }
         _safe_write_jsonl(LOG_PATH, record)
         return final
 
-    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). Add more PDFs or increase Top-K."
-    )
+    # Select sentences
+    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."
 
+    # Prepare retrieval list for logging
     retr_list = []
     for _, r in hits.iterrows():
-        retr_list.append(
-            {
-                "doc": Path(r["doc_path"]).name,
-                "page": _extract_page(r["text"]),
-                "score_tfidf": float(r.get("score_tfidf", 0.0)),
-                "score_bm25": float(r.get("score_bm25", 0.0)),
-                "score_dense": float(r.get("score_dense", 0.0)),
-                "combo_score": float(r.get("score", 0.0)),
-            }
-        )
-
-    # retrieval-only / strict quotations (used in grid search)
+        retr_list.append({
+            "doc": Path(r["doc_path"]).name,
+            "page": _extract_page(r["text"]),
+            "score_tfidf": float(r.get("score_tfidf", 0.0)),
+            "score_bm25": float(r.get("score_bm25", 0.0)),
+            "score_dense": float(r.get("score_dense", 0.0)),
+            "combo_score": float(r.get("score", 0.0)),
+        })
+
+    # Strict quotes only (no LLM)
     if strict_quotes_only:
         if not selected:
-            final = (
-                f"**Quoted Passages:**\n\n---\n"
-                + "\n\n".join(hits["text"].tolist()[:2])
-                + f"\n\n**Citations:** {header_cites}{coverage_note}"
-            )
+            final = f"**Quoted Passages:**\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2]) + f"\n\n**Citations:** {header_cites}{coverage_note}"
         else:
-            final = "**Quoted Passages:**\n- " + "\n- ".join(
-                f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected
-            )
+            final = "**Quoted Passages:**\n- " + "\n- ".join(f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected)
             final += f"\n\n**Citations:** {header_cites}{coverage_note}"
             if include_passages:
-                final += "\n\n---\n" + "\n\n".join(hits["text"].tolist()[:2])
+                final += "\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
 
         record = {
             "run_id": run_id,
-            "ts": int(time.time() * 1000),
+            "ts": int(time.time()*1000),
             "inputs": {
-                "question": question,
-                "top_k": int(k),
-                "n_sentences": int(n_sentences),
-                "w_tfidf": float(w_tfidf),
-                "w_bm25": float(w_bm25),
-                "w_emb": float(w_emb),
-                "use_llm": False,
-                "model": None,
-                "temperature": float(temperature),
-                "config_id": config_id,
+                "question": question, "top_k": int(k), "n_sentences": int(n_sentences),
+                "w_tfidf": float(w_tfidf), "w_bm25": float(w_bm25), "w_emb": float(w_emb),
+                "use_llm": False, "model": None, "temperature": float(temperature)
             },
             "retrieval": {"hits": retr_list, "latency_ms_retriever": latency_ms_retriever},
             "output": {
                 "final_answer": final,
-                "used_sentences": [
-                    {"sent": s["sent"], "doc": s["doc"], "page": s["page"]}
-                    for s in selected
-                ],
+                "used_sentences": [{"sent": s["sent"], "doc": s["doc"], "page": s["page"]} for s in selected]
             },
-            "latency_ms_total": int((time.time() - t0_total) * 1000),
-            "openai": None,
+            "latency_ms_total": int((time.time()-t0_total)*1000),
+            "openai": None
         }
         _safe_write_jsonl(LOG_PATH, record)
         return final
 
-    # extractive / LLM synthesis
+    # Extractive or LLM synthesis
     extractive = compose_extractive(selected)
     llm_usage = None
     llm_latency_ms = None
-
     if use_llm and selected:
         lines = [f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected]
         t0_llm = time.time()
-        llm_text, llm_usage = synthesize_with_llm(
-            question, lines, model=model, temperature=temperature
-        )
+        llm_text, llm_usage = synthesize_with_llm(question, lines, model=model, temperature=temperature)
         t1_llm = time.time()
         llm_latency_ms = int((t1_llm - t0_llm) * 1000)
 
         if llm_text:
-            final = (
-                f"**Answer (LLM synthesis):** {llm_text}\n\n"
-                f"**Citations:** {header_cites}{coverage_note}"
-            )
+            final = f"**Answer (LLM synthesis):** {llm_text}\n\n**Citations:** {header_cites}{coverage_note}"
             if include_passages:
-                final += "\n\n---\n" + "\n\n".join(hits["text"].tolist()[:2])
+                final += "\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
         else:
             if not extractive:
-                final = (
-                    f"**Answer:** Here are relevant passages.\n\n"
-                    f"**Citations:** {header_cites}{coverage_note}\n\n---\n"
-                    + "\n\n".join(hits["text"].tolist()[:2])
-                )
+                final = f"**Answer:** Here are relevant passages.\n\n**Citations:** {header_cites}{coverage_note}\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
             else:
-                final = (
-                    f"**Answer:** {extractive}\n\n"
-                    f"**Citations:** {header_cites}{coverage_note}"
-                )
+                final = f"**Answer:** {extractive}\n\n**Citations:** {header_cites}{coverage_note}"
                 if include_passages:
-                    final += "\n\n---\n" + "\n\n".join(hits["text"].tolist()[:2])
+                    final += "\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
     else:
         if not extractive:
-            final = (
-                f"**Answer:** Here are relevant passages.\n\n"
-                f"**Citations:** {header_cites}{coverage_note}\n\n---\n"
-                + "\n\n".join(hits["text"].tolist()[:2])
-            )
+            final = f"**Answer:** Here are relevant passages.\n\n**Citations:** {header_cites}{coverage_note}\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
         else:
-            final = (
-                f"**Answer:** {extractive}\n\n"
-                f"**Citations:** {header_cites}{coverage_note}"
-            )
+            final = f"**Answer:** {extractive}\n\n**Citations:** {header_cites}{coverage_note}"
             if include_passages:
-                final += "\n\n---\n" + "\n\n".join(hits["text"].tolist()[:2])
+                final += "\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
 
+    # --------- Log full run ---------
     prompt_toks = llm_usage.get("prompt_tokens") if llm_usage else None
     completion_toks = llm_usage.get("completion_tokens") if llm_usage else None
     cost_usd = _calc_cost_usd(prompt_toks, completion_toks)
@@ -590,226 +734,488 @@ def rag_reply(
     total_ms = int((time.time() - t0_total) * 1000)
     record = {
         "run_id": run_id,
-        "ts": int(time.time() * 1000),
+        "ts": int(time.time()*1000),
         "inputs": {
-            "question": question,
-            "top_k": int(k),
-            "n_sentences": int(n_sentences),
-            "w_tfidf": float(w_tfidf),
-            "w_bm25": float(w_bm25),
-            "w_emb": float(w_emb),
-            "use_llm": bool(use_llm),
-            "model": model,
-            "temperature": float(temperature),
-            "config_id": config_id,
+            "question": question, "top_k": int(k), "n_sentences": int(n_sentences),
+            "w_tfidf": float(w_tfidf), "w_bm25": float(w_bm25), "w_emb": float(w_emb),
+            "use_llm": bool(use_llm), "model": model, "temperature": float(temperature)
         },
         "retrieval": {"hits": retr_list, "latency_ms_retriever": latency_ms_retriever},
         "output": {
             "final_answer": final,
-            "used_sentences": [
-                {"sent": s["sent"], "doc": s["doc"], "page": s["page"]}
-                for s in selected
-            ],
+            "used_sentences": [{"sent": s['sent'], "doc": s['doc'], "page": s['page']} for s in selected]
         },
         "latency_ms_total": total_ms,
         "latency_ms_llm": llm_latency_ms,
         "openai": {
             "prompt_tokens": prompt_toks,
             "completion_tokens": completion_toks,
-            "cost_usd": cost_usd,
-        }
-        if use_llm
-        else None,
+            "cost_usd": cost_usd
+        } if use_llm else None
     }
     _safe_write_jsonl(LOG_PATH, record)
     return final
 
-# ---------- automated grid evaluation over weights ----------
-def run_weight_grid_eval(
-    gold_csv: str,
-    weight_grid: List[Dict[str, float]],
-    k: int = 8,
-    n_sentences: int = 4,
-) -> None:
-    """
-    Automatically evaluate many (w_tfidf, w_bm25, w_emb) combinations
-    on the full gold question set.
-
-    - Reads questions from gold_csv (column 'question')
-    - For each configuration in weight_grid, calls rag_reply(...)
-      with use_llm=False and strict_quotes_only=True
-    - All runs are logged into rag_logs.jsonl with a 'config_id'
-      and the exact weights.
-    """
-    gold_df = pd.read_csv(gold_csv)
-    if "question" not in gold_df.columns:
-        raise ValueError("gold_csv must contain a 'question' column.")
-    questions = gold_df["question"].astype(str).tolist()
-
-    for cfg in weight_grid:
-        wt = float(cfg.get("w_tfidf", 0.0))
-        wb = float(cfg.get("w_bm25", 0.0))
-        we = float(cfg.get("w_emb", 0.0))
-        cid = cfg.get("id") or f"tfidf{wt}_bm25{wb}_emb{we}"
-
-        print(
-            f"\n[GridEval] Running config {cid} "
-            f"(w_tfidf={wt}, w_bm25={wb}, w_emb={we}, k={k})"
-        )
-
-        for q in questions:
-            _ = rag_reply(
-                question=q,
-                k=int(k),
-                n_sentences=int(n_sentences),
-                include_passages=False,
-                use_llm=False,
-                model=None,
-                temperature=0.0,
-                strict_quotes_only=True,
-                w_tfidf=wt,
-                w_bm25=wb,
-                w_emb=we,
-                config_id=cid,
-            )
-
-print("✅ RAG core + grid evaluation helpers loaded.")
-
-
-# ===================== GRADIO UI WRAPPER =====================
-
-# --- Chat wrapper ---
-def rag_chat_fn(
-    message,
-    history,
-    top_k,
-    n_sentences,
-    include_passages,
-    w_tfidf,
-    w_bm25,
-    w_emb,
-):
+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?*)"
-    return rag_reply(
-        question=message,
-        k=int(top_k),
-        n_sentences=int(n_sentences),
-        include_passages=bool(include_passages),
-        use_llm=False,
-        model=None,
-        temperature=0.2,
-        strict_quotes_only=False,
-        w_tfidf=float(w_tfidf),
-        w_bm25=float(w_bm25),
-        w_emb=float(w_emb),
-        config_id=None,
-    )
-
-# --- Evaluation wrapper: returns log + file paths ---
-def run_eval_ui(gold_file, k):
-    if gold_file is None:
-        gold_path = Path("gold.csv")
-        if not gold_path.exists():
-            return (
-                "**No gold.csv provided or found in the working directory.**\n"
-                "Upload a file or place gold.csv next to app.py.",
-                None,
-                None,
-                None,
-            )
-        gold_csv = str(gold_path)
-    else:
-        gold_csv = gold_file.name
-
-    logs_jsonl = str(LOG_PATH)
-    out_dir = str(ARTIFACT_DIR)
-
-    # run evaluation and write artifacts
-    evaluate_rag(
-        gold_csv,
-        logs_jsonl,
-        k=int(k),
-        out_dir=out_dir,
-        group_by_weights=True,
-    )
-
-    base = ARTIFACT_DIR
-    perq = base / "metrics_per_question.csv"
-    agg  = base / "metrics_aggregate.json"
-    surf = base / "metrics_by_weights.csv"
-
-    perq_path = str(perq) if perq.exists() else None
-    agg_path  = str(agg) if agg.exists() else None
-    surf_path = str(surf) if surf.exists() else None
-
-    log_msg = (
-        "✅ Evaluation finished.\n\n"
-        f"- Per-question metrics: `{perq}`\n"
-        f"- Aggregate metrics: `{agg}`\n"
-        f"- Config surface: `{surf}`\n\n"
-        "Use the download links below to save the files."
-    )
-
-    return log_msg, perq_path, agg_path, surf_path
-
+    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 styling */
+#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;
+}
+
+/* Evaluate tab dark/high-contrast styling */
+#eval-tab .block, #eval-tab .group, #eval-tab .accordion {
+    background: linear-gradient(165deg, #0a0f1f 0%, #0d1a31 60%, #0a1c2e 100%) !important;
+    border-radius: 12px;
+    border: 1px solid rgba(139, 197, 255, 0.28);
+}
+#eval-tab label, #eval-tab .markdown, #eval-tab .prose, #eval-tab p, #eval-tab span {
+    color: #e6f2ff !important;
+}
+#eval-tab input, #eval-tab .gr-file, #eval-tab .scroll-hide, #eval-tab textarea, #eval-tab select {
+    background: rgba(8, 13, 26, 0.9) !important;
+    border: 1px solid #3b82f6 !important;
+    color: #dbeafe !important;
+}
+#eval-tab input[type="range"] { accent-color: #22c55e !important; }
+#eval-tab button { 
+    border-radius: 10px !important; 
+    font-weight: 700 !important; 
+    background: #0ea5e9 !important; 
+    color: #001321 !important; 
+    border: 1px solid #7dd3fc !important;
+}
+#eval-tab .gr-json, #eval-tab .markdown pre, #eval-tab .markdown code {
+    background: rgba(2, 6, 23, 0.85) !important;
+    color: #e2e8f0 !important;
+    border: 1px solid rgba(148, 163, 184, 0.3) !important;
+    border-radius: 10px !important;
+}
+
+/* Predictor output emphasis */
+#pred-out .wrap { font-size: 20px; font-weight: 700; color: #ecfdf5; }
+
+/* Tab header: darker blue theme for all tabs */
+.gradio-container .tab-nav button[role="tab"] {
+  background: #0b1b34 !important;
+  color: #cfe6ff !important;
+  border: 1px solid #1e3a8a !important;
+}
+.gradio-container .tab-nav button[role="tab"][aria-selected="true"] {
+  background: #0e2a57 !important;
+  color: #e0f2fe !important;
+  border-color: #3b82f6 !important;
+}
+
+/* Evaluate tab: enforce dark-blue text for labels/marks */
+#eval-tab .label, 
+#eval-tab label, 
+#eval-tab .gr-slider .label,
+#eval-tab .wrap .label,
+#eval-tab .prose, 
+#eval-tab .markdown, 
+#eval-tab p, 
+#eval-tab span {
+  color: #cfe6ff !important; /* softer than pure white */
+}
+
+/* Target the specific k-slider label strongly */
+#k-slider .label, 
+#k-slider label, 
+#k-slider .wrap .label {
+  color: #cfe6ff !important;
+  text-shadow: 0 1px 0 rgba(0,0,0,0.35);
+}
+
+/* Slider track/thumb (dark blue gradient + blue thumb) */
+#eval-tab input[type="range"] { 
+  accent-color: #3b82f6 !important; /* fallback */
+}
+
+/* WebKit */
+#eval-tab input[type="range"]::-webkit-slider-runnable-track {
+  height: 6px;
+  background: linear-gradient(90deg, #0b3b68, #1e3a8a);
+  border-radius: 4px;
+}
+#eval-tab input[type="range"]::-webkit-slider-thumb {
+  -webkit-appearance: none;
+  appearance: none;
+  margin-top: -6px;            /* centers thumb on 6px track */
+  width: 18px; height: 18px;
+  background: #1d4ed8;
+  border: 1px solid #60a5fa;
+  border-radius: 50%;
+}
+
+/* Firefox */
+#eval-tab input[type="range"]::-moz-range-track {
+  height: 6px;
+  background: linear-gradient(90deg, #0b3b68, #1e3a8a);
+  border-radius: 4px;
+}
+#eval-tab input[type="range"]::-moz-range-thumb {
+  width: 18px; height: 18px;
+  background: #1d4ed8;
+  border: 1px solid #60a5fa;
+  border-radius: 50%;
+}
+
+/* ======== PATCH: Style the File + JSON outputs by ID ======== */
+#perq-file, #agg-file {
+  background: rgba(8, 13, 26, 0.9) !important;
+  border: 1px solid #3b82f6 !important;
+  border-radius: 12px !important;
+  padding: 8px !important;
+}
+#perq-file * , #agg-file * { color: #dbeafe !important; }
+#perq-file a, #agg-file a {
+  background: #0e2a57 !important;
+  color: #e0f2fe !important;
+  border: 1px solid #60a5fa !important;
+  border-radius: 8px !important;
+  padding: 6px 10px !important;
+  text-decoration: none !important;
+}
+#perq-file a:hover, #agg-file a:hover {
+  background: #10356f !important;
+  border-color: #93c5fd !important;
+}
+/* File preview wrappers (covers multiple Gradio render modes) */
+#perq-file .file-preview, #agg-file .file-preview,
+#perq-file .wrap, #agg-file .wrap {
+  background: rgba(2, 6, 23, 0.85) !important;
+  border-radius: 10px !important;
+  border: 1px solid rgba(148,163,184,.3) !important;
+}
+
+/* JSON output: dark panel + readable text */
+#agg-json {
+  background: rgba(2, 6, 23, 0.85) !important;
+  border: 1px solid rgba(148,163,184,.35) !important;
+  border-radius: 12px !important;
+  padding: 8px !important;
+}
+#agg-json *, #agg-json .json, #agg-json .wrap { color: #e6f2ff !important; }
+#agg-json pre, #agg-json code {
+  background: rgba(4, 10, 24, 0.9) !important;
+  color: #e2e8f0 !important;
+  border: 1px solid rgba(148,163,184,.35) !important;
+  border-radius: 10px !important;
+}
+/* Tree/overflow modes */
+#agg-json [data-testid="json-tree"],
+#agg-json [role="tree"],
+#agg-json .overflow-auto {
+  background: rgba(4, 10, 24, 0.9) !important;
+  color: #e6f2ff !important;
+  border-radius: 10px !important;
+  border: 1px solid rgba(148,163,184,.35) !important;
+}
+
+/* Eval log markdown */
+#eval-log, #eval-log * { color: #cfe6ff !important; }
+#eval-log pre, #eval-log code {
+  background: rgba(2, 6, 23, 0.85) !important;
+  color: #e2e8f0 !important;
+  border: 1px solid rgba(148,163,184,.3) !important;
+  border-radius: 10px !important;
+}
+
+/* When Evaluate tab is active and JS has added .eval-active, bump contrast subtly */
+#eval-tab.eval-active .block, 
+#eval-tab.eval-active .group {
+  border-color: #60a5fa !important;
+}
+#eval-tab.eval-active .label {
+  color: #e6f2ff !important;
+}
+"""
+
+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",
+)
 
-# ------------- Build Gradio UI -----------------
-with gr.Blocks(title="Self-Sensing Concrete RAG") as demo:
+with gr.Blocks(css=CSS, theme=theme, fill_height=True) as demo:
+    # Optional: JS to toggle .eval-active when Evaluate tab selected
+    gr.HTML("""
+    <script>
+    (function(){
+      const applyEvalActive = () => {
+        const selected = document.querySelector('.tab-nav button[role="tab"][aria-selected="true"]');
+        const evalPanel = document.querySelector('#eval-tab');
+        if (!evalPanel) return;
+        if (selected && /Evaluate/.test(selected.textContent)) {
+          evalPanel.classList.add('eval-active');
+        } else {
+          evalPanel.classList.remove('eval-active');
+        }
+      };
+      document.addEventListener('click', function(e) {
+        if (e.target && e.target.getAttribute('role') === 'tab') {
+          setTimeout(applyEvalActive, 50);
+        }
+      }, true);
+      document.addEventListener('DOMContentLoaded', applyEvalActive);
+      setTimeout(applyEvalActive, 300);
+    })();
+    </script>
+    """)
+    
     gr.Markdown(
-        "<h1>Self-Sensing Concrete Assistant — Hybrid RAG</h1>"
-        "<p>Ask questions about self-sensing concrete; answers are grounded in your local PDFs.</p>"
+        "<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():
-        # --------- RAG Chat tab ---------
-        with gr.Tab("📚 RAG Chat"):
+        # ------------------------- Predictor Tab -------------------------
+        with gr.Tab("🔮 Predict Gauge Factor (XGB)"):
             with gr.Row():
-                top_k = gr.Slider(3, 15, value=8, step=1, label="Top-K chunks")
-                n_sentences = gr.Slider(2, 8, value=4, step=1, label="Answer length (sentences)")
-                include_passages = gr.Checkbox(
-                    value=False, label="Include supporting passages"
-                )
+                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=W_EMB_DEFAULT,   step=0.05, label="Dense 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, w_tfidf, w_bm25, w_emb],
-                title="Hybrid RAG Q&A",
-                description="Hybrid BM25 + TF-IDF + dense retrieval with MMR sentence selection."
+                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."
             )
 
-        # --------- Evaluation tab ---------
-        with gr.Tab("📏 Evaluate RAG"):
-            gr.Markdown(
-                "Upload **gold.csv** and compute retrieval metrics against "
-                "`rag_artifacts/rag_logs.jsonl`.\n\n"
-                "When you click **Run Evaluation**, the CSV/JSON files will be created "
-                "and appear as download links below."
-            )
-            gold_file = gr.File(label="gold.csv", file_types=[".csv"])
-            k_slider  = gr.Slider(3, 15, value=8, step=1, label="k for Hit/Recall/nDCG")
+        # ====== Evaluate (Gold vs Logs) — darker, higher-contrast ======
+        with gr.Tab("📏 Evaluate (Gold vs Logs)", elem_id="eval-tab"):
+            gr.Markdown("Upload your **gold.csv** and compute metrics against the app logs.")
+            with gr.Row():
+                gold_file = gr.File(label="gold.csv", file_types=[".csv"], interactive=True)
+                k_slider  = gr.Slider(3, 12, value=8, step=1, label="k for Hit/Recall/nDCG", elem_id="k-slider")
+            with gr.Row():
+                btn_eval = gr.Button("Compute Metrics", variant="primary")
+            with gr.Row():
+                out_perq = gr.File(label="Per-question metrics (CSV)", elem_id="perq-file")
+                out_agg  = gr.File(label="Aggregate metrics (JSON)", elem_id="agg-file")
+            out_json = gr.JSON(label="Aggregate summary", elem_id="agg-json")
+            out_log  = gr.Markdown(label="Run log", elem_id="eval-log")
+
+            def _run_eval_inproc(gold_path: str, k: int = 8):
+                import json as _json
+                out_dir = str(ARTIFACT_DIR)
+                logs = str(LOG_PATH)
+                cmd = [
+                    sys.executable, "rag_eval_metrics.py",
+                    "--gold_csv", gold_path,
+                    "--logs_jsonl", logs,
+                    "--k", str(k),
+                    "--out_dir", out_dir
+                ]
+                try:
+                    p = subprocess.run(cmd, capture_output=True, text=True, check=False)
+                    stdout = p.stdout or ""
+                    stderr = p.stderr or ""
+                    perq = ARTIFACT_DIR / "metrics_per_question.csv"
+                    agg  = ARTIFACT_DIR / "metrics_aggregate.json"
+                    agg_json = {}
+                    if agg.exists():
+                        agg_json = _json.loads(agg.read_text(encoding="utf-8"))
+                    report = "```\n" + (stdout.strip() or "(no stdout)") + ("\n" + stderr.strip() if stderr else "") + "\n```"
+                    return (str(perq) if perq.exists() else None,
+                            str(agg)  if agg.exists()  else None,
+                            agg_json,
+                            report)
+                except Exception as e:
+                    return (None, None, {}, f"**Eval error:** {e}")
+
+            def _eval_wrapper(gf, k):
+                from pathlib import Path
+                if gf is None:
+                    default_gold = Path("gold.csv")
+                    if not default_gold.exists():
+                        return None, None, {}, "**No gold.csv provided or found in repo root.**"
+                    gold_path = str(default_gold)
+                else:
+                    gold_path = gf.name
+                return _run_eval_inproc(gold_path, int(k))
+
+            btn_eval.click(_eval_wrapper, inputs=[gold_file, k_slider],
+                           outputs=[out_perq, out_agg, out_json, out_log])
+
+# ------------- Launch -------------
+if __name__ == "__main__":
+    demo.queue().launch()
+    import os
+import pandas as pd
 
-            btn_eval  = gr.Button("Run Evaluation", variant="primary")
-            eval_out  = gr.Markdown(label="Evaluation log")
+# Folder where your RAG files are stored
+folder = "papers"  # change if needed
 
-            perq_file = gr.File(label="Per-question metrics (CSV)")
-            agg_file  = gr.File(label="Aggregate metrics (JSON)")
-            surf_file = gr.File(label="Config surface (CSV)")
+# List all files in the folder
+files = sorted(os.listdir(folder))
 
-            btn_eval.click(
-                fn=run_eval_ui,
-                inputs=[gold_file, k_slider],
-                outputs=[eval_out, perq_file, agg_file, surf_file],
-            )
+# Save them to a CSV file
+pd.DataFrame({"doc": files}).to_csv("paper_list.csv", index=False)
+
+print("✅ Saved paper_list.csv with", len(files), "papers")
 
-# ------------- Launch app -----------------
-if __name__ == "__main__":
-    demo.queue().launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-    )