Deploy: pre-built index

README.md

@@ -1,12 +1,11 @@
 ---
-title: SloganAI NewAsaf
-emoji: 👀
-colorFrom: indigo
-colorTo: purple
+title: SloganAI-Fixed
+emoji: 🏷️
+colorFrom: yellow
+colorTo: green
 sdk: gradio
 sdk_version: 5.43.1
 app_file: app.py
 pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# SloganAI - Fixed

app.py

@@ -0,0 +1,335 @@
+
+import os, re
+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
+
+# ---- Config ----
+FLAN_PRIMARY = os.getenv("FLAN_PRIMARY", "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"))
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ASSETS_DIR = "assets"
+
+# ---- Lazy models ----
+_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:
+        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}")
+
+# ---- Data & PRE-BUILT FAISS from assets folder ----
+_DATA_DF = None
+_INDEX = None
+_EMBEDDINGS = None
+
+def _ensure_index():
+    global _DATA_DF, _INDEX, _EMBEDDINGS
+    if _INDEX is not None:
+        return
+    
+    # Load assets from the assets directory
+    try:
+        data_path = os.path.join(ASSETS_DIR, "data.parquet")
+        index_path = os.path.join(ASSETS_DIR, "faiss.index")
+        emb_path = os.path.join(ASSETS_DIR, "embeddings.npy")
+        
+        _DATA_DF = pd.read_parquet(data_path)
+        _INDEX = faiss.read_index(index_path)
+        _EMBEDDINGS = np.load(emb_path)
+        
+        print(f"[INFO] Loaded pre-built FAISS index. rows={len(_DATA_DF)}, dim={_INDEX.d}")
+        
+    except FileNotFoundError:
+        print("[ERROR] Pre-built assets not found. The space may fail to run.")
+        print("[INFO] Falling back to building a tiny demo index.")
+        _DATA_DF = 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"
+            ]
+        })
+        _ensure_models()
+        vecs = _EMBED_MODEL.encode(_DATA_DF["description"].astype(str).tolist(), normalize_embeddings=True).astype(np.float32)
+        _INDEX = faiss.IndexFlatIP(vecs.shape[1])
+        _INDEX.add(vecs)
+
+def recommend(query_text: str, top_k: int = 3) -> pd.DataFrame:
+    _ensure_index()
+    _ensure_models() # Make sure the embedder is ready
+    q_vec = _EMBED_MODEL.encode([query_text], normalize_embeddings=True).astype("float32")
+    scores, idxs = _INDEX.search(q_vec, top_k)
+    out = _DATA_DF.iloc[idxs[0]].copy()
+    out["score"] = scores[0]
+    return out[["name","tagline","description","score"]]
+
+# ---- Refined v2 slogan generator (unchanged 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:
+        _ensure_models() # Make sure the embedder is ready
+        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 []
+    _ensure_models() # Make sure the cross-encoder is ready
+    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:
+        _ensure_models() # Make sure the embedder is ready
+        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)
+        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:
+                _ensure_models() # Make sure the embedder is ready
+                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 UI ----
+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):
+    recs = recommend(user_input, top_k=3)
+    slogan = generate_slogan(user_input, neighbors_df=recs, n_samples=NUM_SLOGAN_SAMPLES)
+    recs = recs.reset_index(drop=True)
+    recs.loc[len(recs)] = {"name":"Synthetic Example","tagline":slogan,"description":user_input,"score":np.nan}
+    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")
+    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")
+            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()
+    _ensure_index()
+    demo.queue().launch()

requirements.txt

@@ -0,0 +1,8 @@
+gradio
+transformers
+sentence-transformers
+faiss-cpu
+pandas
+numpy
+torch
+pyarrow