app.py

# vmm
# Self-Sensing Concrete Assistant — Predictor (XGB) + Hybrid RAG
# - 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) ----------------------
import os
os.environ["TRANSFORMERS_NO_TF"] = "1"
os.environ["TRANSFORMERS_NO_FLAX"] = "1"
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# ------------------------------- Imports ------------------------------
import re, time, joblib, warnings, json
from pathlib import Path
from typing import List, Dict, Any

import numpy as np
import pandas as pd
import gradio as gr

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

# Optional deps (handled gracefully if missing)
USE_DENSE = True
try:
    from sentence_transformers import SentenceTransformer
except Exception:
    USE_DENSE = False

try:
    from rank_bm25 import BM25Okapi
except Exception:
    BM25Okapi = None
    print("rank_bm25 not installed; BM25 disabled (TF-IDF still works).")

# Optional OpenAI (for LLM paraphrase)
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-4o-mini").strip().strip('"').strip("'")

try:
    from openai import OpenAI
except Exception:
    OpenAI = None

print("openAI: ", OpenAI)

# ========================= Predictor (kept same as 2nd) =========================
CF_COL     = "Conductive Filler Conc. (wt%)"
TARGET_COL = "Stress GF (MPa-1)"

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", "NA"]
CURRENT_CHOICES = ["DC", "AC", "NA"]

MODEL_CANDIDATES = [
    "stress_gf_xgb.joblib",
    "models/stress_gf_xgb.joblib",
    "/home/user/app/stress_gf_xgb.joblib",
]

def _load_model_or_error():
    for p in MODEL_CANDIDATES:
        if os.path.exists(p):
            try:
                return joblib.load(p)
            except Exception as e:
                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:
            if v in ("", None):
                row[col] = np.nan
            else:
                try:
                    row[col] = float(v)
                except Exception:
                    row[col] = np.nan
        else:
            row[col] = "" if v in (None, "NA") else str(v).strip()
    return pd.DataFrame([row], columns=MAIN_VARIABLES)

def predict_fn(**kwargs):
    mdl = _load_model_or_error()
    if isinstance(mdl, str):
        return mdl
    X_new = _coerce_to_row(kwargs)
    try:
        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:
        return f"Prediction error: {e}"

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": "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("NA")
        elif col == "Current Type":
            cleared.append("NA")
        else:
            cleared.append("")
    return cleared

# ========================= 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"
BM25_TOK_PATH   = ARTIFACT_DIR / "bm25_tokens.joblib"
EMB_NPY_PATH    = ARTIFACT_DIR / "chunk_embeddings.npy"
RAG_META_PATH   = ARTIFACT_DIR / "chunks.parquet"

# 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 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]:
    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 (PyMuPDF preferred; pypdf fallback)
def _extract_pdf_text(pdf_path: Path) -> str:
    try:
        import fitz
        doc = fitz.open(pdf_path)
        out = []
        for i, page in enumerate(doc):
            out.append(f"[[PAGE={i+1}]]\n{page.get_text('text') or ''}")
        return "\n\n".join(out)
    except Exception:
        try:
            from pypdf import PdfReader
            reader = PdfReader(str(pdf_path))
            out = []
            for i, p in enumerate(reader.pages):
                txt = p.extract_text() or ""
                out.append(f"[[PAGE={i+1}]]\n{txt}")
            return "\n\n".join(out)
        except Exception as e:
            print(f"PDF read error ({pdf_path}): {e}")
            return ""

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

def _safe_init_st_model(name: str):
    global USE_DENSE
    if not USE_DENSE:
        return None
    try:
        return SentenceTransformer(name)
    except Exception as e:
        print("Dense embeddings unavailable:", e)
        USE_DENSE = False
        return None

# Build or load index
def build_or_load_hybrid(pdf_dir: Path):
    have_cache = (TFIDF_VECT_PATH.exists() and TFIDF_MAT_PATH.exists()
                  and RAG_META_PATH.exists()
                  and (BM25_TOK_PATH.exists() or BM25Okapi is None)
                  and (EMB_NPY_PATH.exists() or not USE_DENSE))
    if have_cache:
        vectorizer = joblib.load(TFIDF_VECT_PATH)
        X_tfidf    = joblib.load(TFIDF_MAT_PATH)
        meta       = pd.read_parquet(RAG_META_PATH)
        bm25_toks  = joblib.load(BM25_TOK_PATH) if BM25Okapi is not None else None
        emb        = np.load(EMB_NPY_PATH) if (USE_DENSE and EMB_NPY_PATH.exists()) else None
        return vectorizer, X_tfidf, meta, bm25_toks, emb

    rows, all_tokens = [], []
    pdf_paths = list(Path(pdf_dir).glob("**/*.pdf"))
    print(f"Indexing PDFs in {pdf_dir} — found {len(pdf_paths)} files.")
    for pdf in pdf_paths:
        raw = _extract_pdf_text(pdf)
        if not raw.strip():
            continue
        for i, ch in enumerate(chunk_by_sentence_windows(raw, win_size=8, overlap=2)):
            rows.append({"doc_path": str(pdf), "chunk_id": i, "text": ch})
            all_tokens.append(tokenize(ch))
    if not rows:
        # 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),
        min_df=1, max_df=0.95,
        sublinear_tf=True, smooth_idf=True,
        lowercase=True,
        token_pattern=r"(?u)\b\w[\w\-\./%+#]*\b"
    )
    X_tfidf = vectorizer.fit_transform(meta["text"].tolist())

    emb = None
    if USE_DENSE:
        try:
            st_model = _safe_init_st_model(os.getenv("EMB_MODEL_NAME", "sentence-transformers/all-MiniLM-L6-v2"))
            if st_model is not None:
                from sklearn.preprocessing import normalize as sk_normalize
                em = st_model.encode(meta["text"].tolist(), batch_size=64, show_progress_bar=False, convert_to_numpy=True)
                emb = sk_normalize(em)
                np.save(EMB_NPY_PATH, emb)
        except Exception as e:
            print("Dense embedding failed:", e)
            emb = None

    # 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)
bm25 = BM25Okapi(bm25_tokens) if (BM25Okapi is not None and bm25_tokens is not None) else None
st_query_model = _safe_init_st_model(os.getenv("EMB_MODEL_NAME", "sentence-transformers/all-MiniLM-L6-v2"))

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

def hybrid_search(query: str, k=8, w_tfidf=W_TFIDF_DEFAULT, w_bm25=W_BM25_DEFAULT, w_emb=W_EMB_DEFAULT):
    if rag_meta is None or rag_meta.empty:
        return pd.DataFrame()

    # Dense scores
    if USE_DENSE and st_query_model is not None and emb_matrix is not None and w_emb > 0:
        try:
            from sklearn.preprocessing import normalize as sk_normalize
            q_emb = st_query_model.encode([query], convert_to_numpy=True)
            q_emb = sk_normalize(q_emb)[0]
            dense_scores = emb_matrix @ q_emb
        except Exception as e:
            print("Dense query encoding failed:", e)
            dense_scores = np.zeros(len(rag_meta), dtype=float); w_emb = 0.0
    else:
        dense_scores = np.zeros(len(rag_meta), dtype=float); w_emb = 0.0

    # TF-IDF scores
    if tfidf_vectorizer is not None and tfidf_matrix is not None:
        q_vec = tfidf_vectorizer.transform([query])
        tfidf_scores = (tfidf_matrix @ q_vec.T).toarray().ravel()
    else:
        tfidf_scores = np.zeros(len(rag_meta), dtype=float); w_tfidf = 0.0

    # BM25 scores
    if bm25 is not None:
        q_tokens = [t.lower() for t in re.findall(r"[A-Za-z0-9_#+\-/\.%]+", query)]
        bm25_scores = np.array(bm25.get_scores(q_tokens), dtype=float)
    else:
        bm25_scores = np.zeros(len(rag_meta), dtype=float); w_bm25 = 0.0

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

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

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

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

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

def mmr_select_sentences(question: str, hits: pd.DataFrame, top_n=4, pool_per_chunk=6, lambda_div=0.7):
    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]

    # Embedding-based relevance if available, else TF-IDF
    use_dense = USE_DENSE and st_query_model is not None
    if use_dense:
        try:
            from sklearn.preprocessing import normalize as sk_normalize
            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:
            use_dense = False

    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])

    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, Any]]) -> str:
    if not selected:
        return ""
    return " ".join(f"{s['sent']} ({s['doc']}, p.{s['page']})" for s in selected)

def synthesize_with_llm(question: str, sentence_lines: List[str], model: str = None, temperature: float = 0.2) -> str:
    if OPENAI_API_KEY is None or OpenAI is None:
        return 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 like (Doc.pdf, p.X) exactly as given."
    )
    user_prompt = (
        f"Question: {question}\n\n"
        f"Use ONLY these sentences to answer; keep their inline citations:\n" +
        "\n".join(f"- {s}" for s in sentence_lines)
    )
    try:
        resp = client.responses.create(
            model=model,
            input=[
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": user_prompt},
            ],
            temperature=temperature,
        )
        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 = 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:
    hits = hybrid_search(question, k=k, w_tfidf=w_tfidf, w_bm25=w_bm25, w_emb=w_emb)
    if hits is None or hits.empty:
        return "No indexed PDFs found. Upload PDFs to the 'papers/' folder and reload the Space."

    selected = mmr_select_sentences(question, hits, top_n=int(n_sentences), pool_per_chunk=6, lambda_div=0.7)
    header_cites = "; ".join(f"{Path(r['doc_path']).name} (p.{_extract_page(r['text'])})" for _, r in hits.head(6).iterrows())
    srcs = {Path(r['doc_path']).name for _, r in hits.iterrows()}
    coverage_note = "" if len(srcs) >= 3 else f"\n\n> Note: Only {len(srcs)} unique source(s) contributed. Add more PDFs or increase Top-K."

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

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

    if not extractive:
        return f"**Answer:** Here are relevant passages.\n\n**Citations:** {header_cites}{coverage_note}\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])

    msg = f"**Answer:** {extractive}\n\n**Citations:** {header_cites}{coverage_note}"
    if include_passages:
        msg += "\n\n---\n" + "\n\n".join(hits['text'].tolist()[:2])
    return msg

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

# ========================= UI (predictor styling kept) =========================
CSS = """
/* Blue to green gradient background */
.gradio-container {
    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="#1e3a8a",
    body_text_color="#e0f2fe",
    input_background_fill="#172554",
    input_border_color="#1e40af",
    button_primary_background_fill="#2563eb",
    button_primary_text_color="#ffffff",
    button_secondary_background_fill="#14532d",
    button_secondary_text_color="#ecfdf5",
)

with gr.Blocks(css=CSS, theme=theme, fill_height=True) as demo:
    gr.Markdown(
        "<h1 style='margin:0'>Self-Sensing Concrete Assistant</h1>"
        "<p style='opacity:.9'>"
        "Left 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>"
    )

    with gr.Tabs():
        # ------------------------- Predictor Tab -------------------------
        with gr.Tab("🔮 Predict Gauge Factor (XGB)"):
            with gr.Row():
                with gr.Column(scale=7):
                    with gr.Accordion("Primary conductive filler", open=True, elem_classes=["card"]):
                        f1_type = gr.Textbox(label="Filler 1 Type", placeholder="e.g., CNT, Graphite, Steel fiber")
                        f1_diam = gr.Number(label="Filler 1 Diameter (µm)")
                        f1_len  = gr.Number(label="Filler 1 Length (mm)")
                        cf_conc = gr.Number(label=f"{CF_COL}", info="Weight percent of total binder")
                        f1_dim  = gr.Dropdown(DIM_CHOICES, value="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="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="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)", precision=6)
                        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^-1) on original scale (model trains on log1p).\n"
                            "- Missing values are safely imputed per-feature.\n"
                            "- Trained columns:\n"
                            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,
                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)
            btn_demo.click(lambda: _fill_example(), inputs=None, outputs=inputs_in_order)

        # ------------------------- Literature Tab -------------------------
        with gr.Tab("📚 Ask the Literature (Hybrid RAG + MMR)"):
            gr.Markdown(
                "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")
            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=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, 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."
            )

# ------------- Launch -------------
if __name__ == "__main__":
    # queue() helps HF Spaces with concurrency; show_error suggests upload PDFs if none
    demo.queue().launch()