app.py

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import os, json, numpy as np, pandas as pd
import gradio as gr
import faiss

from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

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

ASSETS_DIR = "assets"
DATA_PATH  = "data/slogan.csv"

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

GEN_MODEL_NAME = "google/flan-t5-base"
NUM_GEN_CANDIDATES = 6
MAX_NEW_TOKENS = 24
TEMPERATURE = 0.9
TOP_P = 0.95
NOVELTY_SIM_THRESHOLD = 0.80

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)

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()

_ensure_assets()

searcher = SloganSearcher(assets_dir=ASSETS_DIR, use_rerank=False)

meta = json.load(open(META_PATH))
_encoder = SentenceTransformer(meta["model_name"])

_gen_tokenizer = AutoTokenizer.from_pretrained(GEN_MODEL_NAME)
_gen_model = AutoModelForSeq2SeqLM.from_pretrained(GEN_MODEL_NAME)

# ---- Prompt (adjust if you want your exact wording) ----
def _prompt_for(description: str) -> str:
    return (
        "You are a professional slogan writer. "
        "Write ONE original, catchy startup slogan under 8 words, Title Case, no punctuation. "
        "Do not copy examples. Description:\n"
        f"{description}\nSlogan:"
    )

def _generate_candidates(description: str, n: int = NUM_GEN_CANDIDATES):
    prompt = _prompt_for(description)
    inputs = _gen_tokenizer([prompt]*n, 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,
        eos_token_id=_gen_tokenizer.eos_token_id,
    )
    texts = _gen_tokenizer.batch_decode(outputs, skip_special_tokens=True)
    return [t.replace("Slogan:", "").strip().strip('"') for t in texts if t.strip()]

def _pick_most_novel(candidates, retrieved_texts):
    if not candidates:
        return None
    R = _encoder.encode(retrieved_texts, convert_to_numpy=True, normalize_embeddings=True) if retrieved_texts else None
    best, best_novelty = None, -1e9
    for c in candidates:
        c_emb = _encoder.encode([c], convert_to_numpy=True, normalize_embeddings=True)
        if R is None or len(retrieved_texts) == 0:
            max_sim = 0.0
        else:
            sims = np.dot(R, c_emb[0])  # cosine
            max_sim = float(np.max(sims))
        novelty = 1.0 - max_sim
        if (max_sim < NOVELTY_SIM_THRESHOLD and novelty > best_novelty) or best is None and novelty > best_novelty:
            best, best_novelty = c, novelty
    return best

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, NUM_GEN_CANDIDATES)
    generated = _pick_most_novel(gens, retrieved_texts) 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(generated)
    return "\n".join(lines)

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()