app.py

\
import os, json, numpy as np, pandas as pd
import gradio as gr
import faiss
import re
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

from logic.cleaning import clean_dataframe
from logic.search import SloganSearcher

# -------------------- Config --------------------
ASSETS_DIR   = "assets"
DATA_PATH    = "data/slogan.csv"
PROMPT_PATH  = "data/prompt.txt"

MODEL_NAME   = "sentence-transformers/all-MiniLM-L6-v2"
NORMALIZE    = True

GEN_MODEL    = "google/flan-t5-base"
NUM_GEN_CANDIDATES = 12
MAX_NEW_TOKENS     = 18
TEMPERATURE        = 0.7
TOP_P              = 0.9
REPETITION_PENALTY = 1.15

# choose the most relevant yet non-duplicate candidate
RELEVANCE_WEIGHT   = 0.7
NOVELTY_WEIGHT     = 0.3
DUPLICATE_MAX_SIM  = 0.92
NOVELTY_SIM_THRESHOLD = 0.80  # keep some distance from retrieved

META_PATH    = os.path.join(ASSETS_DIR, "meta.json")
PARQUET_PATH = os.path.join(ASSETS_DIR, "slogans_clean.parquet")
INDEX_PATH   = os.path.join(ASSETS_DIR, "faiss.index")
EMB_PATH     = os.path.join(ASSETS_DIR, "embeddings.npy")

def _log(m): print(f"[SLOGAN-SPACE] {m}", flush=True)

# -------------------- Asset build --------------------
def _build_assets():
    if not os.path.exists(DATA_PATH):
        raise FileNotFoundError(f"Dataset not found at {DATA_PATH} (CSV with columns: 'tagline', 'description').")
    os.makedirs(ASSETS_DIR, exist_ok=True)

    _log(f"Loading dataset: {DATA_PATH}")
    df = pd.read_csv(DATA_PATH)

    _log(f"Rows before cleaning: {len(df)}")
    df = clean_dataframe(df)
    _log(f"Rows after cleaning: {len(df)}")

    if "description" in df.columns and df["description"].notna().any():
        texts = df["description"].fillna(df["tagline"]).astype(str).tolist()
        text_col, fallback_col = "description", "tagline"
    else:
        texts = df["tagline"].astype(str).tolist()
        text_col, fallback_col = "tagline", "tagline"

    _log(f"Encoding with {MODEL_NAME} (normalize={NORMALIZE}) …")
    encoder = SentenceTransformer(MODEL_NAME)
    emb = encoder.encode(texts, batch_size=64, convert_to_numpy=True, normalize_embeddings=NORMALIZE)

    dim = emb.shape[1]
    index = faiss.IndexFlatIP(dim) if NORMALIZE else faiss.IndexFlatL2(dim)
    index.add(emb)

    _log("Persisting assets …")
    df.to_parquet(PARQUET_PATH, index=False)
    faiss.write_index(index, INDEX_PATH)
    np.save(EMB_PATH, emb)

    meta = {
        "model_name": MODEL_NAME,
        "dim": int(dim),
        "normalized": NORMALIZE,
        "metric": "ip" if NORMALIZE else "l2",
        "row_count": int(len(df)),
        "text_col": text_col,
        "fallback_col": fallback_col,
    }
    with open(META_PATH, "w") as f:
        json.dump(meta, f, indent=2)
    _log("Assets built successfully.")

def _ensure_assets():
    need = False
    for p in (META_PATH, PARQUET_PATH, INDEX_PATH):
        if not os.path.exists(p):
            _log(f"Missing asset: {p}")
            need = True
    if need:
        _log("Building assets from scratch …")
        _build_assets()
        return
    try:
        pd.read_parquet(PARQUET_PATH)
    except Exception as e:
        _log(f"Parquet read failed ({e}); rebuilding assets.")
        _build_assets()

# Build before UI
_ensure_assets()

# -------------------- Retrieval --------------------
searcher = SloganSearcher(assets_dir=ASSETS_DIR, use_rerank=False)
meta     = json.load(open(META_PATH))
_encoder = SentenceTransformer(meta["model_name"])

# -------------------- Generator --------------------
_gen_tokenizer = AutoTokenizer.from_pretrained(GEN_MODEL)
_gen_model     = AutoModelForSeq2SeqLM.from_pretrained(GEN_MODEL)

# keep this list small so we don't nuke relevant outputs
_BANNED_TERMS = {"portal", "e-commerce", "ecommerce", "shopping", "shop"}
_PUNCT_CHARS = ":;—–-,.!?“”\"'`"
_PUNCT_RE = re.compile(f"[{re.escape(_PUNCT_CHARS)}]")

_MIN_WORDS, _MAX_WORDS = 2, 8

def _load_prompt():
    if os.path.exists(PROMPT_PATH):
        with open(PROMPT_PATH, "r", encoding="utf-8") as f:
            return f.read()
    return (
        "You are a professional slogan writer.\n"
        "Write ONE original startup slogan under 8 words, Title Case, no punctuation.\n"
        "Do not copy examples.\n"
        "Description:\n{description}\nSlogan:"
    )

def _render_prompt(description: str, retrieved=None) -> str:
    tmpl = _load_prompt()
    if "{description}" in tmpl:
        prompt = tmpl.replace("{description}", description)
    else:
        prompt = f"{tmpl}\n\nDescription:\n{description}\nSlogan:"
    if retrieved:
        prompt += "\n\nDo NOT copy these existing slogans:\n"
        for s in retrieved[:3]:
            prompt += f"- {s}\n"
    return prompt

def _title_case(s: str) -> str:
    small = {"and","or","for","of","the","to","in","on","with","a","an"}
    words = [w for w in s.split() if w]
    out = []
    for i,w in enumerate(words):
        lw = w.lower()
        if i>0 and lw in small: out.append(lw)
        else: out.append(lw.capitalize())
    return " ".join(out)

def _strip_punct(s: str) -> str:
    return _PUNCT_RE.sub("", s)

def _strict_ok(s: str) -> bool:
    if not s: return False
    wc = len(s.split())
    if wc < _MIN_WORDS or wc > _MAX_WORDS: return False
    lo = s.lower()
    if any(term in lo for term in _BANNED_TERMS): return False
    if lo in {"the","a","an"}: return False
    return True

def _postprocess_strict(texts):
    cleaned, seen = [], set()
    for t in texts:
        s = t.replace("Slogan:", "").strip().strip('"').strip("'")
        s = " ".join(s.split())
        s = _strip_punct(s)          # remove punctuation instead of rejecting
        s = _title_case(s)
        if _strict_ok(s):
            k = s.lower()
            if k not in seen:
                seen.add(k); cleaned.append(s)
    return cleaned

def _postprocess_relaxed(texts):
    # fallback if strict returns nothing: keep 2–8 words, strip punctuation, Title Case
    cleaned, seen = [], set()
    for t in texts:
        s = t.strip().strip('"').strip("'")
        s = _strip_punct(s)
        s = " ".join(s.split())
        wc = len(s.split())
        if _MIN_WORDS <= wc <= _MAX_WORDS:
            s = _title_case(s)
            k = s.lower()
            if k not in seen:
                seen.add(k); cleaned.append(s)
    return cleaned

def _generate_candidates(description: str, retrieved_texts, n: int = NUM_GEN_CANDIDATES):
    prompt = _render_prompt(description, retrieved_texts)

    # only block very generic junk at decode time
    bad_ids = _gen_tokenizer(list(_BANNED_TERMS), add_special_tokens=False).input_ids

    inputs = _gen_tokenizer([prompt], return_tensors="pt", padding=True, truncation=True)
    outputs = _gen_model.generate(
        **inputs,
        do_sample=True,
        temperature=TEMPERATURE,
        top_p=TOP_P,
        num_return_sequences=n,
        max_new_tokens=MAX_NEW_TOKENS,
        no_repeat_ngram_size=3,
        repetition_penalty=REPETITION_PENALTY,
        bad_words_ids=bad_ids if bad_ids else None,
        eos_token_id=_gen_tokenizer.eos_token_id,
    )
    texts = _gen_tokenizer.batch_decode(outputs, skip_special_tokens=True)

    cands = _postprocess_strict(texts)
    if not cands:
        cands = _postprocess_relaxed(texts)  # <- graceful fallback
    return cands

def _pick_best(candidates, retrieved_texts, description):
    """Weighted relevance to description minus duplication vs retrieved."""
    if not candidates:
        return None
    c_emb = _encoder.encode(candidates, convert_to_numpy=True, normalize_embeddings=True)
    d_emb = _encoder.encode([description], convert_to_numpy=True, normalize_embeddings=True)[0]
    rel = c_emb @ d_emb  # cosine sim to description

    if retrieved_texts:
        R = _encoder.encode(retrieved_texts, convert_to_numpy=True, normalize_embeddings=True)
        dup = np.max(R @ c_emb.T, axis=0)  # max sim to any retrieved
    else:
        dup = np.zeros(len(candidates), dtype=np.float32)

    # penalize near-duplicates outright
    mask = dup < DUPLICATE_MAX_SIM
    if mask.any():
        scores = RELEVANCE_WEIGHT * rel[mask] - NOVELTY_WEIGHT * dup[mask]
        best_idx = np.argmax(scores)
        return [c for i, c in enumerate(candidates) if mask[i]][best_idx]

    # else: pick most relevant that still clears a basic novelty bar, else top score
    scores = RELEVANCE_WEIGHT * rel - NOVELTY_WEIGHT * dup
    order = np.argsort(-scores)
    for i in order:
        if dup[i] < NOVELTY_SIM_THRESHOLD:
            return candidates[i]
    return candidates[order[0]]

# -------------------- Inference pipeline --------------------
def run_pipeline(user_description: str):
    if not user_description or not user_description.strip():
        return "Please enter a description."
    retrieved_df = searcher.search(user_description, top_k=3, rerank_top_n=10)
    retrieved_texts = retrieved_df["display"].tolist() if not retrieved_df.empty else []
    gens = _generate_candidates(user_description, retrieved_texts, NUM_GEN_CANDIDATES)
    chosen = _pick_best(gens, retrieved_texts, user_description) or (gens[0] if gens else "—")
    lines = []
    lines.append("### 🔎 Top 3 similar slogans")
    if retrieved_texts:
        for i, s in enumerate(retrieved_texts, 1):
            lines.append(f"{i}. {s}")
    else:
        lines.append("_No similar slogans found._")
    lines.append("\n### ✨ AI-generated suggestion")
    lines.append(chosen)
    return "\n".join(lines)

# -------------------- UI --------------------
with gr.Blocks(title="Slogan Finder") as demo:
    gr.Markdown("# 🔎 Slogan Finder\nDescribe your product/company; get 3 similar slogans + 1 AI-generated suggestion.")
    query = gr.Textbox(label="Describe your product/company", placeholder="AI-powered patient financial navigation platform...")
    btn = gr.Button("Get slogans", variant="primary")
    out = gr.Markdown()
    btn.click(run_pipeline, inputs=[query], outputs=out)

demo.queue(max_size=64).launch()