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@@ -290,131 +290,3 @@ literature_pdfs/Self-sensing[[:space:]]performance[[:space:]]of[[:space:]]cement
 literature_pdfs/Self‐Sensing[[:space:]]Cementitious[[:space:]]Composites[[:space:]]with[[:space:]]Hierarchical[[:space:]]Carbon[[:space:]]Fiber‐Carbon[[:space:]]Nanotube[[:space:]]Composite[[:space:]]Fillers.pdf filter=lfs diff=lfs merge=lfs -text
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-papers/S87-EL~1.PDF filter=lfs diff=lfs merge=lfs -text
-papers/S88-ST~1.PDF filter=lfs diff=lfs merge=lfs -text
-papers/S89-Piezoresistivity[[:space:]]of[[:space:]]carbon[[:space:]]fiber[[:space:]]graphite[[:space:]]cement-based[[:space:]]composites[[:space:]]with[[:space:]]CCCW.pdf filter=lfs diff=lfs merge=lfs -text
-papers/S9-Electro-mechanical-self-sensing-response-of-ultra-high-_2018_Composites-Part.pdf filter=lfs diff=lfs merge=lfs -text
-papers/S90-EX~1.PDF filter=lfs diff=lfs merge=lfs -text
-papers/S91-A[[:space:]]comparative[[:space:]]study[[:space:]]on[[:space:]]the[[:space:]]influences[[:space:]]of[[:space:]]CNT[[:space:]]and[[:space:]]GNP[[:space:]]on[[:space:]]the[[:space:]]piezoresistivity[[:space:]]of[[:space:]]cement[[:space:]]composites.pdf filter=lfs diff=lfs merge=lfs -text
-papers/S92-Research-on-the-self-sensing-and-mechanical-properties-of_2021_Cement-and-Co.pdf filter=lfs diff=lfs merge=lfs -text
-papers/S93-Enhanced[[:space:]]effects[[:space:]]of[[:space:]]carbon-based[[:space:]]conductive[[:space:]]materials[[:space:]]on[[:space:]]the[[:space:]]piezoresistive[[:space:]]characteristics[[:space:]]of[[:space:]]cementitious[[:space:]]composites.pdf filter=lfs diff=lfs merge=lfs -text
-papers/S94-The[[:space:]]Utilization[[:space:]]of[[:space:]]Pearson’s[[:space:]]Method[[:space:]]to[[:space:]]Analyze[[:space:]]Piezoresistive[[:space:]]Effect[[:space:]]in[[:space:]]Self-Sensing[[:space:]]Cement[[:space:]]Composite[[:space:]]with[[:space:]]Graphite.pdf filter=lfs diff=lfs merge=lfs -text
-papers/S95-SE~1.PDF filter=lfs diff=lfs merge=lfs -text
-papers/S96-EL~1.PDF filter=lfs diff=lfs merge=lfs -text
-papers/S97-Self-sensing[[:space:]]GFRP-reinforced[[:space:]]concrete[[:space:]]beams[[:space:]]containing[[:space:]]carbon[[:space:]]nanotube-nano[[:space:]]carbon[[:space:]]black[[:space:]]composite[[:space:]]fillers.pdf filter=lfs diff=lfs merge=lfs -text
-papers/S98-MI~1.PDF filter=lfs diff=lfs merge=lfs -text
-papers/S99-Commercial[[:space:]]and[[:space:]]recycled[[:space:]]carbon-based[[:space:]]fillers[[:space:]]and[[:space:]]fibers[[:space:]]for[[:space:]]self-sensing[[:space:]]cement-based[[:space:]]composites.pdf filter=lfs diff=lfs merge=lfs -text
-papers/Self-sensing[[:space:]]enhancement[[:space:]]in[[:space:]]smart[[:space:]]ultra-high[[:space:]]performance[[:space:]]concrete[[:space:]]composites[[:space:]]via[[:space:]]multi-scale[[:space:]]carbon[[:space:]]black.pdf filter=lfs diff=lfs merge=lfs -text
-papers/Self-sensing[[:space:]]performance[[:space:]]of[[:space:]]cementitious[[:space:]]composites[[:space:]]with[[:space:]]functional[[:space:]]fillers[[:space:]]at[[:space:]]macro,[[:space:]]micro[[:space:]]and[[:space:]]nano[[:space:]]scales.pdf filter=lfs diff=lfs merge=lfs -text
-papers/Self‐Sensing[[:space:]]Cementitious[[:space:]]Composites[[:space:]]with[[:space:]]Hierarchical[[:space:]]Carbon[[:space:]]Fiber‐Carbon[[:space:]]Nanotube[[:space:]]Composite[[:space:]]Fillers.pdf filter=lfs diff=lfs merge=lfs -text
-papers/Silane[[:space:]]treatment[[:space:]]of[[:space:]]bagasse[[:space:]]fiber[[:space:]]for[[:space:]]reinforcement[[:space:]]of[[:space:]]cementitious[[:space:]]composites.pdf filter=lfs diff=lfs merge=lfs -text
-papers/The[[:space:]]effect[[:space:]]of[[:space:]]silane[[:space:]]surface[[:space:]]treatment[[:space:]]on[[:space:]]the[[:space:]]mechanical[[:space:]]properties[[:space:]]of[[:space:]]UHPFRC.pdf filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,12 +1,8 @@
-# ================================================================
+# vmm
 # 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
-# - Lightweight instrumentation (JSONL logs per RAG turn)
-# - Dark-blue theme + Evaluate tab + k-slider styling
-# - Citations use SHORT CODES (e.g., S71, S92) from filenames
+# - Predictor tab: identical behavior to your "second code"
+# - Literature tab: from your "first code" (Hybrid RAG + MMR)
+# - Hugging Face friendly: online PDF fetching OFF by default
 # ================================================================
 
 # ---------------------- Runtime flags (HF-safe) ----------------------
@@ -16,9 +12,9 @@ os.environ["TRANSFORMERS_NO_FLAX"] = "1"
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 # ------------------------------- Imports ------------------------------
-import re, joblib, warnings, json, traceback, time, uuid, subprocess, sys
+import re, time, joblib, warnings, json
 from pathlib import Path
-from typing import List, Dict, Any, Optional
+from typing import List, Dict, Any
 
 import numpy as np
 import pandas as pd
@@ -39,21 +35,20 @@ except Exception:
     BM25Okapi = None
     print("rank_bm25 not installed; BM25 disabled (TF-IDF still works).")
 
-# Optional OpenAI (for LLM synthesis)
+# Optional OpenAI (for LLM paraphrase)
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-OPENAI_MODEL   = os.getenv("OPENAI_MODEL", "gpt-5")
+OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-4o-mini").strip().strip('"').strip("'")
+
 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)
+print("openAI: ", OpenAI)
 
-# ========================= Predictor (kept) =========================
+# ========================= Predictor (kept same as 2nd) =========================
 CF_COL     = "Conductive Filler Conc. (wt%)"
 TARGET_COL = "Stress GF (MPa-1)"
-CANON_NA   = "NA"  # canonical placeholder for categoricals
 
 MAIN_VARIABLES = [
     "Filler 1 Type",
@@ -110,133 +105,54 @@ CATEGORICAL_COLS = {
     "Current Type"
 }
 
-DIM_CHOICES     = ["0D", "1D", "2D", "3D", CANON_NA]
-CURRENT_CHOICES = ["DC", "AC", CANON_NA]
+DIM_CHOICES     = ["0D", "1D", "2D", "3D", "NA"]
+CURRENT_CHOICES = ["DC", "AC", "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]:
+def _load_model_or_error():
+    for p in MODEL_CANDIDATES:
         if os.path.exists(p):
             try:
-                MODEL = joblib.load(p)
-                MODEL_STATUS = f"🟢 Loaded model: {Path(p).name}"
-                print("[ModelLoad] Loaded:", p)
-                return
+                return joblib.load(p)
             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
+                return f"Could not load model from {p}: {e}"
+    return ("Model file not found. Upload your trained pipeline as "
+            "stress_gf_xgb.joblib (or put it in models/).")
 
 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)
+            if v in ("", None):
+                row[col] = np.nan
+            else:
+                try:
+                    row[col] = float(v)
+                except Exception:
+                    row[col] = np.nan
         else:
-            s = str(v).strip() if v is not None else ""
-            row[col] = s if s else CANON_NA
+            row[col] = "" if v in (None, "NA") else str(v).strip()
     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
+    mdl = _load_model_or_error()
+    if isinstance(mdl, str):
+        return mdl
     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)
+        y_log = mdl.predict(X_new)        # model predicts log1p(target)
+        y = float(np.expm1(y_log)[0])     # back to original scale MPa^-1
+        if -1e-10 < y < 0:
+            y = 0.0
+        return y
     except Exception as e:
-        print(f"[Predict] {e}")
-        traceback.print_exc()
-        return 0.0
+        return f"Prediction error: {e}"
 
 EXAMPLE = {
     "Filler 1 Type": "CNT",
@@ -245,7 +161,7 @@ EXAMPLE = {
     "Filler 1 Length (mm)": 1.2,
     CF_COL: 0.5,
     "Filler 2 Type": "",
-    "Filler 2 Dimensionality": CANON_NA,
+    "Filler 2 Dimensionality": "NA",
     "Filler 2 Diameter (µm)": None,
     "Filler 2 Length (mm)": None,
     "Specimen Volume (mm3)": 1000,
@@ -273,14 +189,15 @@ def _clear_all():
         if col in NUMERIC_COLS:
             cleared.append(None)
         elif col in {"Filler 1 Dimensionality", "Filler 2 Dimensionality"}:
-            cleared.append(CANON_NA)
+            cleared.append("NA")
         elif col == "Current Type":
-            cleared.append(CANON_NA)
+            cleared.append("NA")
         else:
             cleared.append("")
     return cleared
 
-# ========================= Hybrid RAG =========================
+# ========================= Hybrid RAG (from 1st code) =========================
+# Configuration
 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"
@@ -288,13 +205,16 @@ 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)
+# PDF source (HF-safe: rely on local /papers by default)
+LOCAL_PDF_DIR = Path("./literature_pdfs"); LOCAL_PDF_DIR.mkdir(exist_ok=True)
 USE_ONLINE_SOURCES = os.getenv("USE_ONLINE_SOURCES", "false").lower() == "true"
 
+# Retrieval 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 USE_DENSE is False else 0.40
+W_EMB_DEFAULT   = 0.00 if not USE_DENSE else 0.40
 
+# Simple text processing
 _SENT_SPLIT_RE = re.compile(r"(?<=[.!?])\s+|\n+")
 TOKEN_RE       = re.compile(r"[A-Za-z0-9_#+\-/\.%]+")
 def sent_split(text: str) -> List[str]:
@@ -303,6 +223,7 @@ def sent_split(text: str) -> List[str]:
 def tokenize(text: str) -> List[str]:
     return [t.lower() for t in TOKEN_RE.findall(text)]
 
+# PDF text extraction (PyMuPDF preferred; pypdf fallback)
 def _extract_pdf_text(pdf_path: Path) -> str:
     try:
         import fitz
@@ -344,8 +265,8 @@ def _safe_init_st_model(name: str):
         USE_DENSE = False
         return None
 
+# Build or load index
 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)
@@ -369,11 +290,13 @@ def build_or_load_hybrid(pdf_dir: Path):
             rows.append({"doc_path": str(pdf), "chunk_id": i, "text": ch})
             all_tokens.append(tokenize(ch))
     if not rows:
+        # create empty stub to avoid crashes; UI will message user to upload PDFs
         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),
@@ -397,11 +320,13 @@ def build_or_load_hybrid(pdf_dir: Path):
             print("Dense embedding failed:", e)
             emb = None
 
+    # Save artifacts
     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)
@@ -409,29 +334,9 @@ bm25 = BM25Okapi(bm25_tokens) if (BM25Okapi is not None and bm25_tokens is not N
 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 _short_doc_code(doc_path: str) -> str:
-    """
-    Turn a full filename like:
-      'S92-Research-on-the-self-sensing-and-mechanical-properties-of_2021_Cement-and-Co.pdf'
-    into a short code:
-      'S92'
-    For generic names, falls back to the first token of the stem.
-    """
-    if not doc_path:
-        return "Source"
-    name = Path(doc_path).name
-    stem = name.rsplit(".", 1)[0]
-    # Split on whitespace, hyphen, underscore
-    parts = re.split(r"[ \t\n\r\-_]+", stem)
-    for p in parts:
-        if p:
-            return p
-    return stem or "Source"
-
 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()
@@ -458,7 +363,7 @@ def hybrid_search(query: str, k=8, w_tfidf=W_TFIDF_DEFAULT, w_bm25=W_BM25_DEFAUL
 
     # BM25 scores
     if bm25 is not None:
-        q_tokens = [t.lower() for t in re.findall(r"[A-Za-z0-9_#+\-\/\.%]+", 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(len(rag_meta), dtype=float); w_bm25 = 0.0
@@ -490,114 +395,71 @@ def split_sentences(text: str) -> List[str]:
     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_code = _short_doc_code(row["doc_path"])
+        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_code, "page": page})
-
+        for s in split_sentences(row["text"])[: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]
+
+    # Embedding-based relevance if available, else TF-IDF
     use_dense = USE_DENSE and st_query_model is not None
-    try:
-        if use_dense:
+    if use_dense:
+        try:
             from sklearn.preprocessing import normalize as sk_normalize
-            enc = st_query_model.encode([question] + sent_texts, convert_to_numpy=True)
+            texts = [question] + sent_texts
+            enc = st_query_model.encode(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
+        except Exception:
+            use_dense = False
 
-    # Normalize lambda_div
-    lambda_div = float(np.clip(lambda_div, 0.0, 1.0))
+    if not use_dense:
+        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): return float((S[i] @ S[j].T).toarray()[0, 0])
 
-    # Select first by highest relevance
+    selected, selected_idx = [], []
     remain = list(range(len(pool)))
-    if not remain:
-        return []
     first = int(np.argmax(rel))
-    selected_idx = [first]
-    selected     = [pool[first]]
-    remain.remove(first)
+    selected.append(pool[first]); selected_idx.append(first); remain.remove(first)
 
-    # Clamp top_n
-    max_pick = min(int(top_n), len(pool))
-    while len(selected) < max_pick and remain:
+    while len(selected) < top_n 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
+            sim_to_sel = max(sim_fn(i, j) for j in selected_idx) if selected_idx else 0.0
+            score = lambda_div * rel[i] - (1 - lambda_div) * sim_to_sel
             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)
-
+        best_i = cand_scores[0][1]
+        selected.append(pool[best_i]); selected_idx.append(best_i); remain.remove(best_i)
     return selected
 
 def compose_extractive(selected: List[Dict[str, Any]]) -> str:
     if not selected:
         return ""
-    # Citations inside answer are short codes only, e.g. (S92), (S71)
-    return " ".join(f"{s['sent']} ({s['doc']})" for s in selected)
-
-# ========================= 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"))
+    return " ".join(f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected)
 
-def _safe_write_jsonl(path: Path, record: dict):
-    try:
-        with open(path, "a", encoding="utf-8") as f:
-            f.write(json.dumps(record, ensure_ascii=False) + "\n")
-    except Exception as e:
-        print("[Log] write failed:", e)
-
-def _calc_cost_usd(prompt_toks, completion_toks):
-    if prompt_toks is None or completion_toks is None:
+def synthesize_with_llm(question: str, sentence_lines: List[str], model: str = None, temperature: float = 0.2) -> str:
+    if OPENAI_API_KEY is None or OpenAI is None:
         return None
-    return (prompt_toks / 1000.0) * OPENAI_IN_COST_PER_1K + (completion_toks / 1000.0) * OPENAI_OUT_COST_PER_1K
-
-# ----------------- 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
+    print("calling LLM api")    
     client = OpenAI(api_key=OPENAI_API_KEY)
     model = model or OPENAI_MODEL
+    print("using: ", 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 exactly as given (e.g., (S92), (S92; S71))."
+        "Retain inline citations like (Doc.pdf, p.X) exactly as given."
     )
     user_prompt = (
         f"Question: {question}\n\n"
@@ -613,186 +475,61 @@ def synthesize_with_llm(question: str, sentence_lines: List[str], model: str = N
             ],
             temperature=temperature,
         )
-        out_text = getattr(resp, "output_text", None) or str(resp)
-        usage = None
-        try:
-            u = getattr(resp, "usage", None)
-            if u:
-                pt = getattr(u, "prompt_tokens", None) if hasattr(u, "prompt_tokens") else u.get("prompt_tokens", None)
-                ct = getattr(u, "completion_tokens", None) if hasattr(u, "completion_tokens") else u.get("completion_tokens", None)
-                usage = {"prompt_tokens": pt, "completion_tokens": ct}
-        except Exception:
-            usage = None
-        return out_text, usage
-    except Exception:
-        return None, None
+        print(resp.output_text)
+        return getattr(resp, "output_text", None) or str(resp)
+    except Exception as e:
+        print("error in LLM synthesis:", e)
+        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,
+    use_llm: bool = True,
+    model: str = "gpt-4o-mini",
     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:
-    run_id = str(uuid.uuid4())
-    t0_total = time.time()
-    t0_retr  = time.time()
-
-    # --- 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. Upload PDFs to the 'papers/' folder and reload the Space."
-        record = {
-            "run_id": run_id,
-            "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)
-            },
-            "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
-        }
-        _safe_write_jsonl(LOG_PATH, record)
-        return final
-
-    # Select sentences
+        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."
 
-    # Header citations: short codes only, joined by '; ' (e.g., "S55; S71; S92")
-    header_codes = []
-    for _, r in hits.head(6).iterrows():
-        code = _short_doc_code(r["doc_path"])
-        if code not in header_codes:
-            header_codes.append(code)
-    header_cites = "; ".join(header_codes)
-    src_codes = set(header_codes)
-    coverage_note = "" if len(src_codes) >= 3 else f"\n\n> Note: Only {len(src_codes)} unique source(s) contributed. Add more PDFs or increase Top-K."
-
-    # Prepare retrieval list for logging (full filenames kept here)
-    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)),
-        })
-
-    # Strict quotes only (no LLM)
     if strict_quotes_only:
         if not selected:
-            final = (
-                "**Quoted Passages:**\n\n---\n" +
-                "\n\n".join(hits['text'].tolist()[:2]) +
-                f"\n\n**Citations:** {header_cites}{coverage_note}"
-            )
-        else:
-            bullets = "\n- ".join(f"{s['sent']} ({s['doc']})" for s in selected)
-            final = f"**Quoted Passages:**\n- {bullets}\n\n**Citations:** {header_cites}{coverage_note}"
-            if include_passages:
-                final += "\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
-
-        record = {
-            "run_id": run_id,
-            "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)
-            },
-            "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]
-            },
-            "latency_ms_total": int((time.time()-t0_total)*1000),
-            "openai": None
-        }
-        _safe_write_jsonl(LOG_PATH, record)
-        return final
-
-    # Extractive or LLM synthesis
+            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)
-    llm_usage = None
-    llm_latency_ms = None
     if use_llm and selected:
-        # Lines already carry short-code citations, e.g. "... (S92)"
-        lines = [f"{s['sent']} ({s['doc']})" for s in selected]
-        t0_llm = time.time()
-        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)
-
+        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:
-            final = f"**Answer (LLM synthesis):** {llm_text}\n\n**Citations:** {header_cites}{coverage_note}"
+            msg = 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])
-        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])
-                )
-            else:
-                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])
-    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])
-            )
-        else:
-            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])
-
-    # --------- 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)
-
-    total_ms = int((time.time() - t0_total) * 1000)
-    record = {
-        "run_id": run_id,
-        "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)
-        },
-        "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]
-        },
-        "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
-    }
-    _safe_write_jsonl(LOG_PATH, record)
-    return final
+                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,
@@ -816,225 +553,24 @@ def rag_chat_fn(message, history, top_k, n_sentences, include_passages,
     except Exception as e:
         return f"RAG error: {e}"
 
-# ========================= UI (science-oriented styling) =========================
+# ========================= UI (predictor styling kept) =========================
 CSS = """
-/* Science-oriented: crisp contrast + readable numerics */
-* {font-family: ui-sans-serif, system-ui, -apple-system, 'Segoe UI', Roboto, 'Helvetica Neue', Arial;}
+/* Blue to green gradient background */
 .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;
-}
-
-/* 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;
-}
-
-/* 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;
-  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;
+    background: linear-gradient(135deg, #1e3a8a 0%, #166534 60%, #15803d 100%) !important;
 }
+* {font-family: ui-sans-serif, system-ui, -apple-system, 'Segoe UI', Roboto, 'Helvetica Neue', Arial;}
+.card {background: rgba(255,255,255,0.07) !important; border: 1px solid rgba(255,255,255,0.12);}
+label.svelte-1ipelgc {color: #e0f2fe !important;}
 """
 
 theme = gr.themes.Soft(
     primary_hue="blue",
     neutral_hue="green"
 ).set(
-    body_background_fill="#0b1020",
+    body_background_fill="#1e3a8a",
     body_text_color="#e0f2fe",
-    input_background_fill="#0f172a",
+    input_background_fill="#172554",
     input_border_color="#1e40af",
     button_primary_background_fill="#2563eb",
     button_primary_text_color="#ffffff",
@@ -1043,37 +579,12 @@ theme = gr.themes.Soft(
 )
 
 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 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. "
-        "Answers cite short document codes (e.g., <code>S71</code>, <code>S92</code>)."
+        "Left tab: ML prediction for Stress Gauge Factor (kept identical to your deployed predictor). "
+        "Right tab: Literature Q&A via Hybrid RAG (BM25 + TF-IDF + optional dense) with MMR sentence selection. "
+        "Upload PDFs into <code>papers/</code> in your Space repo."
         "</p>"
     )
 
@@ -1083,27 +594,27 @@ with gr.Blocks(css=CSS, theme=theme, fill_height=True) as demo:
             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 *")
+                        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="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")
+                        f2_dim  = gr.Dropdown(DIM_CHOICES, value="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 *")
+                        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)")
@@ -1111,14 +622,13 @@ with gr.Blocks(css=CSS, theme=theme, fill_height=True) as demo:
 
                     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")
+                        E_mod     = gr.Number(label="Modulus of Elasticity (GPa)")
+                        current   = gr.Dropdown(CURRENT_CHOICES, value="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>")
+                        out_pred = gr.Number(label="Predicted Stress GF (MPa-1)", precision=6)
                         with gr.Row():
                             btn_pred  = gr.Button("Predict", variant="primary")
                             btn_clear = gr.Button("Clear")
@@ -1126,14 +636,14 @@ with gr.Blocks(css=CSS, theme=theme, fill_height=True) as demo:
 
                     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"
+                            "- Pipeline: ColumnTransformer -> (RobustScaler + OneHot) -> XGBoost\n"
+                            "- Target: Stress GF (MPa^-1) on original scale (model trains on log1p).\n"
                             "- Missing values are safely imputed per-feature.\n"
                             "- Trained columns:\n"
-                            f"  `{', '.join(MAIN_VARIABLES)}`",
-                            elem_classes=["prose"]
+                            f"  `{', '.join(MAIN_VARIABLES)}`"
                         )
 
+            # Wire predictor buttons
             inputs_in_order = [
                 f1_type, f1_diam, f1_len, cf_conc,
                 f1_dim,  f2_type, f2_diam, f2_len,
@@ -1148,108 +658,38 @@ with gr.Blocks(css=CSS, theme=theme, fill_height=True) as demo:
                 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_clear.click(lambda: _clear_all(), inputs=None, outputs=inputs_in_order)
             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")))
+        with gr.Tab("📚 Ask the Literature (Hybrid RAG + MMR)"):
             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 short document codes such as <code>S71</code>, <code>S92</code>."
+                "Upload PDFs into the repository folder <code>papers/</code> then reload the Space. "
+                "Answers cite (Doc.pdf, p.X). Toggle strict quotes or optional LLM paraphrasing."
             )
             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)
-
+                include_passages = gr.Checkbox(value=False, label="Include supporting passages")
             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)
-
+                w_emb   = gr.Slider(0.0, 1.0, value=W_EMB_DEFAULT,   step=0.05, label="Dense weight (set 0 if disabled)")
+            with gr.Accordion("LLM & Controls", open=False):
+                strict_quotes_only = gr.Checkbox(value=False, label="Strict quotes only (no paraphrasing)")
+                use_llm     = gr.Checkbox(value=True, label="Use LLM to paraphrase selected sentences")
+                model_name  = gr.Textbox(value=os.getenv("OPENAI_MODEL", OPENAI_MODEL),
+                                         label="LLM model", placeholder="e.g., gpt-5 or gpt-5-mini")
+                temperature = gr.Slider(0.0, 1.0, value=0.2, step=0.05, label="Temperature")
             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
-                ],
+                additional_inputs=[top_k, n_sentences, include_passages, use_llm, model_name,
+                                   temperature, strict_quotes_only, w_tfidf, w_bm25, w_emb],
                 title="Literature Q&A",
-                description="Hybrid retrieval with diversity. Answers carry inline short-code citations (e.g., (S92), (S71))."
+                description="Hybrid retrieval with diversity. Answers carry inline (Doc, p.X) citations. Toggle strict/LLM modes."
             )
 
-        # ====== Evaluate (Gold vs Logs) ======
-        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 as _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__":
+    # queue() helps HF Spaces with concurrency; show_error suggests upload PDFs if none
     demo.queue().launch()
-
-    # After launch: export a simple list of PDFs as paper_list.csv
-    import os as _os
-    import pandas as _pd
-    folder = "papers"
-    files = sorted(_os.listdir(folder)) if _os.path.exists(folder) else []
-    _pd.DataFrame({"doc": files}).to_csv("paper_list.csv", index=False)
-    print("✅ Saved paper_list.csv with", len(files), "papers")

papers/1-s2.0-S095006181732278X-main.pdf

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papers/Development of self-sensing ultra-high-performance concrete using hybrid carbon black and carbon nanofibers.pdf

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