# ================================================================
# Self-Sensing Concrete Assistant — Hybrid RAG + XGB + (opt) GPT-5
# FIXED for Windows/Conda import issues (transformers/quantizers)
# - Pins compatible versions (transformers 4.44.2, sbert 2.7.0, torch 2.x)
# - Disables TF/Flax backends; safe fallbacks if dense fails
# - Hybrid retrieval (BM25 + TF-IDF + Dense*) + MMR sentence selection
# - Local folder only (RAG reads from ./literature_pdfs); no online indexing
# - Optional GPT-5 synthesis strictly from selected cited sentences
# - Gradio UI with Prediction + Literature Q&A tabs
# ================================================================

# ---------------------- MUST RUN THESE FLAGS FIRST ----------------------
import os
os.environ["TRANSFORMERS_NO_TF"] = "1"      # don't import TensorFlow
os.environ["TRANSFORMERS_NO_FLAX"] = "1"    # don't import Flax/JAX
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# ------------------------------- Imports -----------------------------------
import re, json, time, joblib, warnings, math, hashlib
from pathlib import Path
from typing import List, Dict
import numpy as np
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import RobustScaler, OneHotEncoder
from sklearn.preprocessing import normalize as sk_normalize
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_extraction.text import TfidfVectorizer

from xgboost import XGBRegressor
from pypdf import PdfReader
import fitz  # PyMuPDF
import gradio as gr

USE_DENSE = True
try:
    from sentence_transformers import SentenceTransformer
except Exception as e:
    USE_DENSE = False
    print("⚠️ sentence-transformers unavailable; continuing with TF-IDF + BM25 only.\n", e)

from rank_bm25 import BM25Okapi
from openai import OpenAI

warnings.filterwarnings("ignore", category=UserWarning)

# ============================ Config =======================================
# --- Data & model paths ---
DATA_PATH       = "july3.xlsx"   # <- update if needed

# --- Local PDF folder for RAG (no online indexing) ---
LOCAL_PDF_DIR   = Path("./literature_pdfs")  # <- your local folder
LOCAL_PDF_DIR.mkdir(exist_ok=True)

# --- RAG artifacts (kept in working dir) ---
ARTIFACT_DIR    = Path("rag_artifacts"); ARTIFACT_DIR.mkdir(exist_ok=True)
MODEL_OUT       = "stress_gf_xgb.joblib"
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"

# --- Embedding model (fast CPU) ---
EMB_MODEL_NAME    = os.getenv("EMB_MODEL_NAME", "sentence-transformers/all-MiniLM-L6-v2")

# --- OpenAI (optional LLM synthesis) ---
OPENAI_MODEL      = os.getenv("OPENAI_MODEL", "gpt-4o-mini")  # e.g., "gpt-5-mini"
OPENAI_API_KEY    = os.getenv("OPENAI_API_KEY", None)   # set env var to enable LLM

# --- Retrieval weights (UI defaults adapt if dense disabled) ---
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

RANDOM_SEED = 42

# ==================== XGB Pipeline (Prediction) ============================
def make_onehot():
    try:
        return OneHotEncoder(handle_unknown="ignore", sparse_output=False)
    except TypeError:
        return OneHotEncoder(handle_unknown="ignore", sparse=False)

def rmse(y_true, y_pred):
    return mean_squared_error(y_true, y_pred)

def evaluate(m, X, y_log, name="Model"):
    y_pred_log = m.predict(X)
    y_pred = np.expm1(y_pred_log)
    y_true = np.expm1(y_log)
    r2  = r2_score(y_true, y_pred)
    r   = rmse(y_true, y_pred)
    mae = mean_absolute_error(y_true, y_pred)
    print(f"{name}: R²={r2:.3f}, RMSE={r:.3f}, MAE={mae:.3f}")
    return r2, r, mae

# --- Load data
df = pd.read_excel(DATA_PATH)
df.columns = df.columns.str.strip()

drop_cols = [
    'Loading rate (MPa/s)', 'Voltage (V) AC\\DC', 'Elastic Modulus (GPa)', 'Duration (hrs) of Dying Method'
]
df = df.drop(columns=[c for c in drop_cols if c in df.columns], errors='ignore')

main_variables = [
    'Filler1_Type', 'Filler1_Diameter_um', 'Filler1_Length_mm',
    'AvgFiller_Density_g/cm3', 'AvgFiller_weight_%', 'AvgFiller_Volume_%',
    'Filler1_Dimensions', 'Filler2_Type', 'Filler2_Diameter_um', 'Filler2_Length_mm',
    'Filler2_Dimensions', 'Sample_Volume_mm3', 'Electrode/Probe_Count', 'Electrode/Probe_Material',
    'W/B', 'S/B', 'GaugeLength_mm', 'Curing_Conditions', 'Num_ConductiveFillers',
    'DryingTemperature_C', 'DryingDuration_hrs', 'LoadingRate_MPa/s',
    'ElasticModulus_Gpa', 'Voltage_Type', 'Applied_Voltage_V'
]
target_col = 'Stress_GF_Mpa'

df = df[main_variables + [target_col]].copy()
df = df.dropna(subset=[target_col])
df = df[df[target_col] > 0]

numeric_cols = [
    'Filler1_Diameter_um', 'Filler1_Length_mm', 'AvgFiller_Density_g/cm3',
    'AvgFiller_weight_%', 'AvgFiller_Volume_%', 'Filler2_Diameter_um',
    'Filler2_Length_mm', 'Sample_Volume_mm3', 'Electrode/Probe_Count',
    'W/B', 'S/B', 'GaugeLength_mm', 'Num_ConductiveFillers',
    'DryingTemperature_C', 'DryingDuration_hrs', 'LoadingRate_MPa/s',
    'ElasticModulus_Gpa', 'Applied_Voltage_V'
]
categorical_cols = [
    'Filler1_Type', 'Filler1_Dimensions', 'Filler2_Type', 'Filler2_Dimensions',
    'Electrode/Probe_Material', 'Curing_Conditions', 'Voltage_Type'
]

for c in numeric_cols:
    df[c] = pd.to_numeric(df[c], errors='coerce')
for c in categorical_cols:
    df[c] = df[c].astype(str)

vt = VarianceThreshold(threshold=1e-3)
vt.fit(df[numeric_cols])
numeric_cols = [c for c in numeric_cols if c not in df[numeric_cols].columns[vt.variances_ < 1e-3]]

corr = df[numeric_cols].corr().abs()
upper = corr.where(np.triu(np.ones(corr.shape), k=1).astype(bool))
to_drop = [c for c in upper.columns if any(upper[c] > 0.95)]
numeric_cols = [c for c in numeric_cols if c not in to_drop]

X = df[main_variables].copy()
y = np.log1p(df[target_col])

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=RANDOM_SEED
)

BEST_PARAMS = {
    "regressor__subsample":        1.0,
    "regressor__reg_lambda":       5,
    "regressor__reg_alpha":        0.05,
    "regressor__n_estimators":     300,
    "regressor__max_depth":        6,
    "regressor__learning_rate":    0.1,
    "regressor__gamma":            0,
    "regressor__colsample_bytree": 1.0
}

def train_and_save_model():
    num_tf = Pipeline([('imputer', SimpleImputer(strategy='median')),
                       ('scaler', RobustScaler())])
    cat_tf = Pipeline([('imputer', SimpleImputer(strategy='most_frequent')),
                       ('onehot', make_onehot())])

    preprocessor = ColumnTransformer([
        ('num', num_tf, numeric_cols),
        ('cat', cat_tf, categorical_cols)
    ])

    xgb_pipe = Pipeline([
        ('preprocessor', preprocessor),
        ('regressor', XGBRegressor(random_state=RANDOM_SEED, n_jobs=-1, verbosity=0))
    ])
    xgb_pipe.set_params(**BEST_PARAMS).fit(X_train, y_train)

    joblib.dump(xgb_pipe, MODEL_OUT)
    print(f"✅ Trained new model and saved → {MODEL_OUT}")
    return xgb_pipe

def load_or_train_model():
    if os.path.exists(MODEL_OUT):
        print(f"📂 Loading existing model from {MODEL_OUT}")
        return joblib.load(MODEL_OUT)
    else:
        print("⚠️ No saved model found. Training a new one...")
        return train_and_save_model()

xgb_pipe = load_or_train_model()

# ======================= Hybrid RAG Indexing ================================
_SENT_SPLIT_RE = re.compile(r"(?<=[.!?])\s+|\n+")
TOKEN_RE = re.compile(r"[A-Za-z0-9_#+\-/\.%]+")

def sent_split(text: str) -> List[str]:
    sents = [s.strip() for s in _SENT_SPLIT_RE.split(text) if s.strip()]
    return [s for s in sents if len(s.split()) >= 5]

def tokenize(text: str) -> List[str]:
    return [t.lower() for t in TOKEN_RE.findall(text)]

def extract_text_pymupdf(pdf_path: Path) -> str:
    try:
        doc = fitz.open(pdf_path)
        buff = []
        for i, page in enumerate(doc):
            txt = page.get_text("text") or ""
            buff.append(f"[[PAGE={i+1}]]\n{txt}")
        return "\n\n".join(buff)
    except Exception:
        # Fallback to PyPDF
        try:
            reader = PdfReader(str(pdf_path))
            buff = []
            for i, p in enumerate(reader.pages):
                txt = p.extract_text() or ""
                buff.append(f"[[PAGE={i+1}]]\n{txt}")
            return "\n\n".join(buff)
        except Exception as e:
            print(f"PDF read error ({pdf_path}): {e}")
            return ""

def chunk_by_sentence_windows(text: str, win_size=8, overlap=2) -> List[str]:
    sents = sent_split(text)
    chunks = []
    step = max(1, win_size - overlap)
    for i in range(0, len(sents), step):
        window = sents[i:i+win_size]
        if not window: break
        chunks.append(" ".join(window))
    return chunks

def _safe_init_st_model(name: str):
    """Try to init SentenceTransformer; on failure, disable dense and return None."""
    global USE_DENSE
    if not USE_DENSE:
        return None
    try:
        m = SentenceTransformer(name)
        return m
    except Exception as e:
        print("⚠️ Could not initialize SentenceTransformer; disabling dense embeddings.\n", e)
        USE_DENSE = False
        return None

def _collect_pdf_paths(pdf_dir: Path) -> List[Path]:
    # Collect PDFs recursively from the local folder
    return list(Path(pdf_dir).glob("**/*.pdf"))

def build_or_load_hybrid(pdf_dir: Path):
    # If artifacts exist, load them
    have_cache = (TFIDF_VECT_PATH.exists() and TFIDF_MAT_PATH.exists()
                  and BM25_TOK_PATH.exists() and RAG_META_PATH.exists()
                  and (EMB_NPY_PATH.exists() or not USE_DENSE))
    if have_cache:
        vectorizer = joblib.load(TFIDF_VECT_PATH)
        X_tfidf    = joblib.load(TFIDF_MAT_PATH)
        meta       = pd.read_parquet(RAG_META_PATH)
        bm25_toks  = joblib.load(BM25_TOK_PATH)
        emb        = np.load(EMB_NPY_PATH) if (USE_DENSE and EMB_NPY_PATH.exists()) else None
        print("Loaded hybrid index.")
        return vectorizer, X_tfidf, meta, bm25_toks, emb

    # Fresh index
    rows, all_tokens = [], []
    pdf_paths = _collect_pdf_paths(pdf_dir)
    print(f"Indexing PDFs from {pdf_dir}. Found {len(pdf_paths)} files.")
    for pdf in pdf_paths:
        raw = extract_text_pymupdf(pdf)
        if not raw.strip():
            continue
        for i, ch in enumerate(chunk_by_sentence_windows(raw, win_size=8, overlap=2)):
            rows.append({"doc_path": str(pdf), "chunk_id": i, "text": ch})
            all_tokens.append(tokenize(ch))

    if not rows:
        raise RuntimeError(f"No PDF text found under: {pdf_dir}")

    meta = pd.DataFrame(rows)

    # TF-IDF
    vectorizer = TfidfVectorizer(
        ngram_range=(1,2),
        min_df=1, max_df=0.95,
        sublinear_tf=True, smooth_idf=True,
        lowercase=True,
        token_pattern=r"(?u)\b\w[\w\-\./%+#]*\b"
    )
    X_tfidf = vectorizer.fit_transform(meta["text"].tolist())

    # Dense (optional)
    emb = None
    if USE_DENSE:
        try:
            st_model_tmp = _safe_init_st_model(EMB_MODEL_NAME)
            if st_model_tmp is not None:
                em = st_model_tmp.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:
            emb = None
            print("⚠️ Dense embeddings failed; continuing without them.\n", e)

    # Save artifacts
    joblib.dump(vectorizer, TFIDF_VECT_PATH)
    joblib.dump(X_tfidf, TFIDF_MAT_PATH)
    joblib.dump(all_tokens, BM25_TOK_PATH)
    meta.to_parquet(RAG_META_PATH, index=False)

    print(f"Indexed {len(meta)} chunks from {meta['doc_path'].nunique()} PDFs.")
    return vectorizer, X_tfidf, meta, all_tokens, emb

# ---------- Auto reindex if new/modified PDFs are detected ----------
from datetime import datetime

def auto_reindex_if_needed(pdf_dir: Path):
    """Rebuilds RAG index if new or modified PDFs are detected."""
    meta_path = RAG_META_PATH
    pdfs = _collect_pdf_paths(pdf_dir)
    if not meta_path.exists():
        print("No existing index found — indexing now...")
        # Remove stale artifacts if any partial set exists
        for p in [TFIDF_VECT_PATH, TFIDF_MAT_PATH, BM25_TOK_PATH, EMB_NPY_PATH]:
            try:
                if p.exists(): p.unlink()
            except Exception:
                pass
        return  # build will happen below
    last_index_time = datetime.fromtimestamp(meta_path.stat().st_mtime)
    recent = [p for p in pdfs if datetime.fromtimestamp(p.stat().st_mtime) > last_index_time]
    if recent:
        print(f"Found {len(recent)} new/updated PDFs — rebuilding index...")
        # Clear artifacts to force rebuild
        for p in [TFIDF_VECT_PATH, TFIDF_MAT_PATH, BM25_TOK_PATH, EMB_NPY_PATH, RAG_META_PATH]:
            try:
                if p.exists(): p.unlink()
            except Exception:
                pass

# Build hybrid index (local only)
auto_reindex_if_needed(LOCAL_PDF_DIR)
tfidf_vectorizer, tfidf_matrix, rag_meta, bm25_tokens, emb_matrix = build_or_load_hybrid(LOCAL_PDF_DIR)
bm25 = BM25Okapi(bm25_tokens)
st_query_model = _safe_init_st_model(EMB_MODEL_NAME)  # safe init; may set USE_DENSE=False

# If dense failed at runtime, update default weights in case UI uses them
if not USE_DENSE:
    W_TFIDF_DEFAULT, W_BM25_DEFAULT, W_EMB_DEFAULT = 0.50, 0.50, 0.00

def _extract_page(text_chunk: str) -> str:
    m = list(re.finditer(r"\[\[PAGE=(\d+)\]\]", text_chunk))
    return (m[-1].group(1) if m else "?")

# ---------------------- Hybrid search --------------------------------------
def hybrid_search(query: str, k=8, w_tfidf=W_TFIDF_DEFAULT, w_bm25=W_BM25_DEFAULT, w_emb=W_EMB_DEFAULT):
    # Dense (optional)
    if USE_DENSE and st_query_model is not None and emb_matrix is not None and w_emb > 0:
        try:
            q_emb = st_query_model.encode([query], convert_to_numpy=True)
            q_emb = sk_normalize(q_emb)[0]
            dense_scores = emb_matrix @ q_emb
        except Exception as e:
            print("⚠️ Dense query encoding failed; ignoring dense this run.\n", e)
            dense_scores = np.zeros(len(rag_meta), dtype=float)
            w_emb = 0.0
    else:
        dense_scores = np.zeros(len(rag_meta), dtype=float)
        w_emb = 0.0  # force off

    # TF-IDF
    q_vec = tfidf_vectorizer.transform([query])
    tfidf_scores = (tfidf_matrix @ q_vec.T).toarray().ravel()

    # BM25
    q_tokens = [t.lower() for t in TOKEN_RE.findall(query)]
    bm25_scores = np.array(bm25.get_scores(q_tokens), dtype=float)

    def _norm(x):
        x = np.asarray(x, dtype=float)
        if np.allclose(x.max(), x.min()):
            return np.zeros_like(x)
        return (x - x.min()) / (x.max() - x.min())

    s_dense = _norm(dense_scores)
    s_tfidf = _norm(tfidf_scores)
    s_bm25  = _norm(bm25_scores)

    total_w = (w_tfidf + w_bm25 + w_emb) or 1.0
    w_tfidf, w_bm25, w_emb = w_tfidf/total_w, w_bm25/total_w, w_emb/total_w

    combo = w_emb * s_dense + w_tfidf * s_tfidf + w_bm25 * s_bm25
    idx = np.argsort(-combo)[:k]
    hits = rag_meta.iloc[idx].copy()
    hits["score_dense"] = s_dense[idx]
    hits["score_tfidf"] = s_tfidf[idx]
    hits["score_bm25"]  = s_bm25[idx]
    hits["score"]       = combo[idx]
    return hits.reset_index(drop=True)

# -------------- Sentence selection with MMR (diversity) --------------------
def split_sentences(text: str) -> List[str]:
    sents = sent_split(text)
    return [s for s in sents if 6 <= len(s.split()) <= 60]

def mmr_select_sentences(question: str, hits: pd.DataFrame, top_n=4, pool_per_chunk=6, lambda_div=0.7):
    pool = []
    for _, row in hits.iterrows():
        doc = Path(row["doc_path"]).name
        page = _extract_page(row["text"])
        for s in split_sentences(row["text"])[:pool_per_chunk]:
            pool.append({"sent": s, "doc": doc, "page": page})
    if not pool:
        return []

    sent_texts = [p["sent"] for p in pool]

    if USE_DENSE and st_query_model is not None:
        try:
            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])
        except Exception as e:
            print("⚠️ Dense sentence encoding failed; falling back to TF-IDF for MMR.\n", e)
            Q = tfidf_vectorizer.transform([question])
            S = tfidf_vectorizer.transform(sent_texts)
            rel = (S @ Q.T).toarray().ravel()
            def sim_fn(i, j): return float((S[i] @ S[j].T).toarray()[0, 0])
    else:
        Q = tfidf_vectorizer.transform([question])
        S = tfidf_vectorizer.transform(sent_texts)
        rel = (S @ Q.T).toarray().ravel()
        def sim_fn(i, j): return float((S[i] @ S[j].T).toarray()[0, 0])

    selected, selected_idx = [], []
    remain = list(range(len(pool)))
    first = int(np.argmax(rel))
    selected.append(pool[first]); selected_idx.append(first); remain.remove(first)

    while len(selected) < top_n and remain:
        cand_scores = []
        for i in remain:
            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))
        cand_scores.sort(reverse=True)
        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:
    if not selected:
        return ""
    lines = [f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected]
    return " ".join(lines)

# ------------------- Optional GPT-5 synthesis ------------------------------
# ------------------- Optional GPT-4o/GPT-5 synthesis ------------------------------
def synthesize_with_llm(question: str, sentence_lines: List[str], model: str = None, temperature: float = 0.2) -> str:
    if OPENAI_API_KEY is None:
        print("Skipping ChatGPT")
        return None  # not configured → skip synthesis

    from openai import OpenAI
    client = OpenAI(api_key=OPENAI_API_KEY)
    if model is None:
        model = OPENAI_MODEL

    # --- Stronger, clean academic prompt ---
    SYSTEM_PROMPT = (
        "You are a scientific writing assistant specializing in self-sensing cementitious materials.\n"
        "Write a short, fluent, and informative paragraph (3–6 sentences) answering the question using ONLY the provided evidence.\n"
        "Rephrase and synthesize ideas; do not copy sentences verbatim.\n"
        "Include parenthetical citations exactly as given (e.g., '(Paper.pdf, p.4)')."
    )

    user_prompt = (
        f"Question: {question}\n\n"
        "Evidence:\n" +
        "\n".join(f"- {s}" for s in sentence_lines)
    )

    try:
        print("🔍 Calling GPT synthesis...")
        response = client.chat.completions.create(
            model=model,
            temperature=temperature,
            messages=[
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": user_prompt},
            ],
        )

        answer = response.choices[0].message.content.strip()
        return answer

    except Exception as e:
        print(f"❌ LLM synthesis error: {e}")
        return None


# ------------------------ RAG reply ----------------------------------------
def rag_reply(
    question: str,
    k: int = 8,
    n_sentences: int = 4,
    include_passages: bool = False,
    use_llm: bool = False,
    model: str = None,
    temperature: float = 0.2,
    strict_quotes_only: bool = False,
    w_tfidf: float = W_TFIDF_DEFAULT,
    w_bm25: float  = W_BM25_DEFAULT,
    w_emb: float   = W_EMB_DEFAULT
) -> str:
    hits = hybrid_search(question, k=k, w_tfidf=w_tfidf, w_bm25=w_bm25, w_emb=w_emb)
    if hits.empty:
        return "No relevant passages found. Add more PDFs in literature_pdfs/ or adjust your query."

    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()
    )
    # Coverage note (helps debugging thin answers)
    srcs = {Path(r['doc_path']).name for _, r in hits.iterrows()}
    coverage_note = ""
    if len(srcs) < 3:
        coverage_note = f"\n\n> Note: Only {len(srcs)} unique source(s) contributed. Add more PDFs or increase Top-K."

    if strict_quotes_only:
        if not selected:
            return f"**Quoted Passages:**\n\n---\n" + "\n\n".join(hits["text"].tolist()[:2]) + \
                   f"\n\n**Citations:** {header_cites}{coverage_note}"
        msg = "**Quoted Passages:**\n- " + "\n- ".join(f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected)
        msg += f"\n\n**Citations:** {header_cites}{coverage_note}"
        if include_passages:
            msg += "\n\n---\n" + "\n\n".join(hits["text"].tolist()[:2])
        return msg

    # Extractive baseline
    extractive = compose_extractive(selected)

    # Optional LLM synthesis
    if use_llm and selected:
        lines = [f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected]
        llm_text = synthesize_with_llm(question, lines, model=model, temperature=temperature)
        if llm_text:
            msg = f"**Answer (GPT-5 synthesis):** {llm_text}\n\n**Citations:** {header_cites}{coverage_note}"
            if include_passages:
                msg += "\n\n---\n" + "\n\n".join(hits["text"].tolist()[:2])
            return msg

    # Fallback: purely extractive
    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

# =========================== Gradio UI =====================================
INPUT_COLS = [
    "Filler1_Type", "Filler1_Dimensions", "Filler1_Diameter_um", "Filler1_Length_mm",
    "Filler2_Type", "Filler2_Dimensions", "Filler2_Diameter_um", "Filler2_Length_mm",
    "AvgFiller_Density_g/cm3", "AvgFiller_weight_%", "AvgFiller_Volume_%",
    "Sample_Volume_mm3", "Electrode/Probe_Count", "Electrode/Probe_Material",
    "W/B", "S/B", "GaugeLength_mm", "Curing_Conditions", "Num_ConductiveFillers",
    "DryingTemperature_C", "DryingDuration_hrs", "LoadingRate_MPa/s",
    "ElasticModulus_Gpa", "Voltage_Type", "Applied_Voltage_V"
]
NUMERIC_INPUTS = {
    "Filler1_Diameter_um","Filler1_Length_mm","Filler2_Diameter_um","Filler2_Length_mm",
    "AvgFiller_Density_g/cm3","AvgFiller_weight_%","AvgFiller_Volume_%","Sample_Volume_mm3",
    "Electrode/Probe_Count","W/B","S/B","GaugeLength_mm","Num_ConductiveFillers",
    "DryingTemperature_C","DryingDuration_hrs","LoadingRate_MPa/s","ElasticModulus_Gpa",
    "Applied_Voltage_V"
}
CAT_DIM_CHOICES = ["0D","1D","2D","3D","NA"]

def _coerce_row(args):
    row = {c: v for c, v in zip(INPUT_COLS, args)}
    clean = {}
    for k, v in row.items():
        if k in NUMERIC_INPUTS:
            if v in ("", None): clean[k] = None
            else:
                try: clean[k] = float(v)
                except: clean[k] = None
        else:
            clean[k] = "" if v is None else str(v).strip()
    return pd.DataFrame([clean], columns=INPUT_COLS)

def _load_model():
    if not os.path.exists(MODEL_OUT):
        raise FileNotFoundError(f"Model file not found at '{MODEL_OUT}'. Retrain above.")
    return joblib.load(MODEL_OUT)

def predict_fn(*args):
    try:
        mdl = _load_model()
        X_new = _coerce_row(args)
        y_log = mdl.predict(X_new)
        y = float(np.expm1(y_log)[0])
        if -1e-8 < y < 0: y = 0.0
        return y
    except Exception as e:
        return f"Error during prediction: {e}"

def rag_chat_fn(message, history, top_k, n_sentences, include_passages,
                use_llm, model_name, temperature, strict_quotes_only,
                w_tfidf, w_bm25, w_emb):
    if not message or not message.strip():
        return "Ask a literature question (e.g., *How does CNT length affect gauge factor?*)"
    try:
        return rag_reply(
            question=message,
            k=int(top_k),
            n_sentences=int(n_sentences),
            include_passages=bool(include_passages),
            use_llm=bool(use_llm),
            model=(model_name or None),
            temperature=float(temperature),
            strict_quotes_only=bool(strict_quotes_only),
            w_tfidf=float(w_tfidf),
            w_bm25=float(w_bm25),
            w_emb=float(w_emb),
        )
    except Exception as e:
        return f"RAG error: {e}"

with gr.Blocks() as demo:
    gr.Markdown("# 🧪 Self-Sensing Concrete Assistant — Hybrid RAG (Accurate Q&A)")
    gr.Markdown(
        "- **Prediction**: XGBoost pipeline for **Stress Gauge Factor (MPa)**.\n"
        "- **Literature (Hybrid RAG)**: BM25 + TF-IDF + Dense embeddings with **MMR** sentence selection.\n"
        "- **Strict mode** shows only quoted sentences with citations; **GPT-5** can paraphrase strictly from those quotes.\n"
        "- **Local-only RAG**: drop PDFs into `literature_pdfs/` and the index will auto-refresh on restart."
    )

    with gr.Tabs():
        with gr.Tab("🔮 Predict Gauge Factor (XGB)"):
            with gr.Row():
                with gr.Column():
                    inputs = [
                        gr.Textbox(label="Filler1_Type", placeholder="e.g., CNT, Graphite, Steel fiber"),
                        gr.Dropdown(CAT_DIM_CHOICES, label="Filler1_Dimensions", value="NA"),
                        gr.Number(label="Filler1_Diameter_um"),
                        gr.Number(label="Filler1_Length_mm"),
                        gr.Textbox(label="Filler2_Type", placeholder="Optional"),
                        gr.Dropdown(CAT_DIM_CHOICES, label="Filler2_Dimensions", value="NA"),
                        gr.Number(label="Filler2_Diameter_um"),
                        gr.Number(label="Filler2_Length_mm"),
                        gr.Number(label="AvgFiller_Density_g/cm3"),
                        gr.Number(label="AvgFiller_weight_%"),
                        gr.Number(label="AvgFiller_Volume_%"),
                        gr.Number(label="Sample_Volume_mm3"),
                        gr.Number(label="Electrode/Probe_Count"),
                        gr.Textbox(label="Electrode/Probe_Material", placeholder="e.g., Copper, Silver paste"),
                        gr.Number(label="W/B"),
                        gr.Number(label="S/B"),
                        gr.Number(label="GaugeLength_mm"),
                        gr.Textbox(label="Curing_Conditions", placeholder="e.g., 28d water, 20°C"),
                        gr.Number(label="Num_ConductiveFillers"),
                        gr.Number(label="DryingTemperature_C"),
                        gr.Number(label="DryingDuration_hrs"),
                        gr.Number(label="LoadingRate_MPa/s"),
                        gr.Number(label="ElasticModulus_Gpa"),
                        gr.Textbox(label="Voltage_Type", placeholder="AC / DC"),
                        gr.Number(label="Applied_Voltage_V"),
                    ]
                with gr.Column():
                    out_pred = gr.Number(label="Predicted Stress_GF (MPa)", precision=6)
                    gr.Button("Predict", variant="primary").click(predict_fn, inputs, out_pred)

        with gr.Tab("📚 Ask the Literature (Hybrid RAG + MMR)"):
            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")
            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 (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=False, label="Use GPT-5 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, 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 (Doc, p.X) citations. Toggle strict/LLM modes."
            )

# Note: add share=True to expose publicly (for iframe embedding)
demo.queue().launch()