app.py

import os, re, json
import numpy as np
import pandas as pd
import gradio as gr
import faiss
import torch
from typing import List
from sentence_transformers import SentenceTransformer, CrossEncoder
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# =========================
# Global Config
# =========================
# מודלים (אותו סטינג כמו במחברת; יש Fallback ל-base אם ה-Large לא נכנס לזיכרון)
FLAN_PRIMARY = os.getenv("FLAN_PRIMARY", "google/flan-t5-large")
FLAN_FALLBACK = "google/flan-t5-base"
EMBED_NAME = "sentence-transformers/all-mpnet-base-v2"
RERANK_NAME = "cross-encoder/stsb-roberta-base"

NUM_SLOGAN_SAMPLES = int(os.getenv("NUM_SLOGAN_SAMPLES", "16"))  # אפשר להעלות ל-32 אם יש GPU
INDEX_ROOT = os.path.join(os.path.dirname(__file__), "vector_store")  # איפה ששמנו את האינדקסים
DEFAULT_MODEL_FOR_INDEX = EMBED_NAME

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# =========================
# Lazy model loading (first call only)
# =========================
_GEN_TOK = None
_GEN_MODEL = None
_EMBED_MODEL = None
_RERANKER = None

def _ensure_models():
    global _GEN_TOK, _GEN_MODEL, _EMBED_MODEL, _RERANKER
    if _EMBED_MODEL is None:
        _EMBED_MODEL = SentenceTransformer(EMBED_NAME)
    if _RERANKER is None:
        _RERANKER = CrossEncoder(RERANK_NAME)

    if _GEN_MODEL is None:
        try:
            tok = AutoTokenizer.from_pretrained(FLAN_PRIMARY)
            mdl = AutoModelForSeq2SeqLM.from_pretrained(FLAN_PRIMARY)
            _GEN_TOK, _GEN_MODEL = tok, mdl.to(DEVICE)
            print(f"[INFO] Loaded generator: {FLAN_PRIMARY}")
        except Exception as e:
            print(f"[WARN] Failed to load {FLAN_PRIMARY}. Falling back to {FLAN_FALLBACK}. Error: {e}")
            tok = AutoTokenizer.from_pretrained(FLAN_FALLBACK)
            mdl = AutoModelForSeq2SeqLM.from_pretrained(FLAN_FALLBACK)
            _GEN_TOK, _GEN_MODEL = tok, mdl.to(DEVICE)
            print(f"[INFO] Loaded generator: {FLAN_FALLBACK}")

# =========================
# Index cache (so we don't read multiple times)
# =========================
_INDEX_CACHE = {}   # model_key -> (faiss_index, meta_df)

def _model_key(name: str) -> str:
    return name.replace("/", "_")

def _format_for_e5(texts, as_query=False):
    prefix = "query: " if as_query else "passage: "
    return [prefix + str(t) for t in texts]

def _load_index_for_model(model_name: str = DEFAULT_MODEL_FOR_INDEX):
    """Load FAISS index + meta once for a given model."""
    mkey = _model_key(model_name)
    if mkey in _INDEX_CACHE:
        return _INDEX_CACHE[mkey]

    base = os.path.join(INDEX_ROOT, mkey)
    idx_path = os.path.join(base, "index.faiss")
    meta_path = os.path.join(base, "meta.parquet")

    if not (os.path.exists(idx_path) and os.path.exists(meta_path)):
        # fallback: tiny demo index (3 rows) if user didn't push vector_store
        print(f"[WARN] Missing index for {model_name}. Using tiny demo in-memory index.")
        demo = pd.DataFrame({
            "name": ["HowDidIDo", "Museotainment", "Movitr"],
            "tagline": ["Online evaluation platform", "PacMan & Louvre meet", "Crowdsourced video translation"],
            "description": [
                "Public speaking, Presentation skills and interview practice",
                "Interactive AR museum tours",
                "Video translation with voice and subtitles"
            ]
        })
        model = SentenceTransformer(model_name)
        vecs = model.encode(demo["description"].tolist(), normalize_embeddings=True)
        dim = vecs.shape[1]
        index = faiss.IndexFlatIP(dim)
        index.add(np.asarray(vecs, dtype=np.float32))
        _INDEX_CACHE[mkey] = (index, demo)
        return _INDEX_CACHE[mkey]

    index = faiss.read_index(idx_path)
    meta_df = pd.read_parquet(meta_path)
    _INDEX_CACHE[mkey] = (index, meta_df)
    return _INDEX_CACHE[mkey]

# =========================
# Recommendation (top-3) using FAISS index you generated
# =========================
def recommend(query_text: str, model_name: str = DEFAULT_MODEL_FOR_INDEX, top_k: int = 3) -> pd.DataFrame:
    _ensure_models()
    index, meta = _load_index_for_model(model_name)

    # format for E5 if needed
    if model_name.startswith("intfloat/e5"):
        q_inp = _format_for_e5([query_text], as_query=True)
    else:
        q_inp = [query_text]

    q_vec = _EMBED_MODEL.encode(q_inp, normalize_embeddings=True)
    q_vec = np.asarray(q_vec, dtype=np.float32)
    scores, idxs = index.search(q_vec, top_k)
    scores, idxs = scores[0], idxs[0]
    out = meta.iloc[idxs].copy()
    out["score"] = scores
    # make sure columns exist in output (name, tagline, description)
    cols = [c for c in ["row_id","name","tagline","description","score"] if c in out.columns or c=="score"]
    return out[cols] if "score" in out.columns else out

# =========================
# Advanced Slogan Generator (your Refined v2 logic)
# =========================
BLOCK_PATTERNS = [
    r"^[A-Z][a-z]+ [A-Z][a-z]+ (Platform|Solution|System|Application|Marketplace)$",
    r"^[A-Z][a-z]+ [A-Z][a-z]+$",
    r"^[A-Z][a-z]+$",
]
HARD_BLOCK_WORDS = {
    "platform","solution","system","application","marketplace",
    "ai-powered","ai powered","empower","empowering",
    "artificial intelligence","machine learning","augmented reality","virtual reality",
}
GENERIC_WORDS = {"app","assistant","smart","ai","ml","ar","vr","decentralized","blockchain"}
MARKETING_VERBS = {"build","grow","simplify","discover","create","connect","transform","unlock","boost","learn","move","clarify"}
BENEFIT_WORDS   = {"faster","smarter","easier","better","safer","clearer","stronger","together","confidently","simply","instantly"}
GOOD_SLOGANS_TO_AVOID_DUP = {
    "smarter care, faster decisions",
    "checkout built for small brands",
    "less guessing. more healing.",
    "built to grow with your cart.",
    "stand tall. feel better.",
    "train your brain to win.",
    "your body. your algorithm.",
    "play smarter. grow brighter.",
    "style that thinks with you."
}

def _tokens(s: str) -> List[str]:
    return re.findall(r"[a-z0-9]{3,}", s.lower())

def _jaccard(a: List[str], b: List[str]) -> float:
    A, B = set(a), set(b)
    return 0.0 if not A or not B else len(A & B) / len(A | B)

def _titlecase_soft(s: str) -> str:
    out = []
    for w in s.split():
        out.append(w if w.isupper() else w.capitalize())
    return " ".join(out)

def _is_blocked_slogan(s: str) -> bool:
    if not s: return True
    s_strip = s.strip()
    for pat in BLOCK_PATTERNS:
        if re.match(pat, s_strip):
            return True
    s_low = s_strip.lower()
    for w in HARD_BLOCK_WORDS:
        if w in s_low:
            return True
    if s_low in GOOD_SLOGANS_TO_AVOID_DUP:
        return True
    return False

def _generic_penalty(s: str) -> float:
    hits = sum(1 for w in GENERIC_WORDS if w in s.lower())
    return min(1.0, 0.25 * hits)

def _for_penalty(s: str) -> float:
    return 0.3 if re.search(r"\bfor\b", s.lower()) else 0.0

def _neighbor_context(neighbors_df: pd.DataFrame) -> str:
    if neighbors_df is None or neighbors_df.empty:
        return ""
    examples = []
    for _, row in neighbors_df.head(3).iterrows():
        tg = str(row.get("tagline", "")).strip()
        if 5 <= len(tg) <= 70:
            examples.append(f"- {tg}")
    return "\n".join(examples)

def _copies_neighbor(s: str, neighbors_df: pd.DataFrame) -> bool:
    if neighbors_df is None or neighbors_df.empty:
        return False
    s_low = s.lower()
    s_toks = _tokens(s_low)
    for _, row in neighbors_df.iterrows():
        t = str(row.get("tagline", "")).strip()
        if not t:
            continue
        t_low = t.lower()
        if s_low == t_low:
            return True
        if _jaccard(s_toks, _tokens(t_low)) >= 0.7:
            return True
    try:
        s_vec = _EMBED_MODEL.encode([s])[0]; s_vec = s_vec / np.linalg.norm(s_vec)
        for _, row in neighbors_df.head(3).iterrows():
            t = str(row.get("tagline", "")).strip()
            if not t: continue
            t_vec = _EMBED_MODEL.encode([t])[0]; t_vec = t_vec / np.linalg.norm(t_vec)
            if float(np.dot(s_vec, t_vec)) >= 0.85:
                return True
    except Exception:
        pass
    return False

def _clean_slogan(text: str, max_words: int = 8) -> str:
    text = text.strip().split("\n")[0]
    text = re.sub(r"[\"“”‘’]", "", text)
    text = re.sub(r"\s+", " ", text).strip()
    text = re.sub(r"^\W+|\W+$", "", text)
    words = text.split()
    if len(words) > max_words:
        text = " ".join(words[:max_words])
    return text

def _score_candidates(query: str, cands: List[str], neighbors_df: pd.DataFrame) -> List[tuple]:
    if not cands:
        return []
    ce_scores = np.asarray(_RERANKER.predict([(query, s) for s in cands]), dtype=np.float32) / 5.0
    q_toks = _tokens(query)
    results = []

    neighbor_vecs = []
    if neighbors_df is not None and not neighbors_df.empty:
        for _, row in neighbors_df.head(3).iterrows():
            t = str(row.get("tagline","")).strip()
            if t:
                v = _EMBED_MODEL.encode([t])[0]
                neighbor_vecs.append(v / np.linalg.norm(v))

    for i, s in enumerate(cands):
        words = s.split()
        brevity   = 1.0 - min(1.0, abs(len(words) - 5) / 5.0)   # best ~5 words
        wl = set(w.lower() for w in words)
        m_hits = len(wl & MARKETING_VERBS)
        b_hits = len(wl & BENEFIT_WORDS)
        marketing = min(1.0, 0.2*m_hits + 0.2*b_hits)
        g_pen = _generic_penalty(s)
        f_pen = _for_penalty(s)

        n_pen = 0.0
        if neighbor_vecs:
            try:
                s_vec = _EMBED_MODEL.encode([s])[0]; s_vec = s_vec / np.linalg.norm(s_vec)
                sim_max = max(float(np.dot(s_vec, nv)) for nv in neighbor_vecs) if neighbor_vecs else 0.0
                n_pen = sim_max
            except Exception:
                n_pen = 0.0

        overlap = _jaccard(q_toks, _tokens(s))
        anti_copy = 1.0 - overlap

        score = (
            0.55*float(ce_scores[i]) +
            0.20*brevity +
            0.15*marketing +
            0.03*anti_copy -
            0.07*g_pen -
            0.03*f_pen -
            0.10*n_pen
        )
        results.append((s, float(score)))
    return results

def generate_slogan(query_text: str, neighbors_df: pd.DataFrame = None, n_samples: int = NUM_SLOGAN_SAMPLES) -> str:
    _ensure_models()
    ctx = _neighbor_context(neighbors_df)
    prompt = (
        "You are a creative brand copywriter. Write short, original, memorable startup slogans (max 8 words).\n"
        "Forbidden words: app, assistant, platform, solution, system, marketplace, AI, machine learning, augmented reality, virtual reality, decentralized, empower.\n"
        "Focus on clear benefits and vivid verbs. Do not copy the description. Return ONLY a list, one slogan per line.\n\n"
        "Good Examples:\n"
        "Description: AI assistant for doctors to prioritize patient cases\n"
        "Slogan: Less Guessing. More Healing.\n\n"
        "Description: Payments for small online stores\n"
        "Slogan: Built to Grow with Your Cart.\n\n"
        "Description: Neurotech headset to boost focus\n"
        "Slogan: Train Your Brain to Win.\n\n"
        "Description: Interior design suggestions with AI\n"
        "Slogan: Style That Thinks With You.\n\n"
        "Bad Examples (avoid these): Innovative AI Platform / Smart App for Everyone / Empowering Small Businesses\n\n"
    )
    if ctx:
        prompt += f"Similar taglines (style only):\n{ctx}\n\n"
    prompt += f"Description: {query_text}\nSlogans:"

    input_ids = _GEN_TOK(prompt, return_tensors="pt").input_ids.to(DEVICE)
    outputs = _GEN_MODEL.generate(
        input_ids,
        max_new_tokens=24,
        do_sample=True,
        top_k=60,
        top_p=0.92,
        temperature=1.2,
        num_return_sequences=n_samples,
        repetition_penalty=1.08
    )
    raw_cands = [_GEN_TOK.decode(o, skip_special_tokens=True) for o in outputs]

    cand_set = set()
    for txt in raw_cands:
        for line in txt.split("\n"):
            s = _clean_slogan(line)
            if not s:
                continue
            if len(s.split()) < 2 or len(s.split()) > 8:
                continue
            if _is_blocked_slogan(s):
                continue
            if _copies_neighbor(s, neighbors_df):
                continue
            cand_set.add(_titlecase_soft(s))

    if not cand_set:
        return _clean_slogan(_GEN_TOK.decode(outputs[0], skip_special_tokens=True))

    scored = _score_candidates(query_text, sorted(cand_set), neighbors_df)
    if not scored:
        return _clean_slogan(_GEN_TOK.decode(outputs[0], skip_special_tokens=True))

    scored.sort(key=lambda x: x[1], reverse=True)
    return scored[0][0]

# =========================
# Gradio Pipeline
# =========================
EXAMPLES = [
    "AI coach for improving public speaking skills",
    "Augmented reality app for interactive museum tours",
    "Voice-controlled task manager for remote teams",
    "Machine learning system for predicting crop yields",
    "Platform for AI-assisted interior design suggestions",
]

def pipeline(user_input: str):
    # 1) Top-3 recommendations from your FAISS index (mpnet by default)
    recs = recommend(user_input, model_name=DEFAULT_MODEL_FOR_INDEX, top_k=3)

    # 2) Generate slogan using the neighbors as style context
    slogan = generate_slogan(user_input, neighbors_df=recs, n_samples=NUM_SLOGAN_SAMPLES)

    # 3) Append the generated item as the 4th row
    recs = recs.reset_index(drop=True)
    # Ensure columns exist
    if "name" not in recs.columns:        recs["name"] = ""
    if "tagline" not in recs.columns:     recs["tagline"] = ""
    if "description" not in recs.columns: recs["description"] = ""

    recs.loc[len(recs)] = {
        "row_id": np.nan,
        "name": "Synthetic Example",
        "tagline": slogan,
        "description": user_input,
        "score": np.nan
    }
    # Second output: the slogan itself (visible headline)
    return recs[["name","tagline","description","score"]], slogan

with gr.Blocks(title="SloganAI — Recommendations + Slogan Generator") as demo:
    gr.Markdown("## SloganAI — Top-3 Recommendations + A High-Quality Generated Slogan\nEnter a startup idea, click **Submit**, or try an example.")
    with gr.Row():
        with gr.Column(scale=1):
            inp = gr.Textbox(label="Enter a startup description", lines=3, placeholder="e.g., AI coach for improving public speaking skills")
            ex  = gr.Examples(EXAMPLES, inputs=inp, label="One‑click examples")
            btn = gr.Button("Submit", variant="primary")
        with gr.Column(scale=2):
            out_df = gr.Dataframe(headers=["Name","Tagline","Description","Score"], label="Top 3 + Generated")
            out_sg = gr.Textbox(label="Generated Slogan", interactive=False)

    btn.click(fn=pipeline, inputs=inp, outputs=[out_df, out_sg])

if __name__ == "__main__":
    _ensure_models()
    demo.queue().launch()