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Update app.py
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app.py
CHANGED
@@ -3,7 +3,7 @@ import gradio as gr
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from datasets import load_dataset
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from sklearn.metrics.pairwise import cosine_similarity
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# -------- Config
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SAMPLE_SIZE = int(os.getenv("SAMPLE_SIZE", "5000"))
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RANDOM_STATE = 42
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DEFAULT_INPUT = "I am so happy with this product"
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@@ -18,7 +18,19 @@ def clean_text(text: str) -> str:
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text = re.sub(r"\s+", " ", text).strip()
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return text
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def _l2norm(x: np.ndarray) -> np.ndarray:
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return x / (np.linalg.norm(x, axis=1, keepdims=True) + 1e-12)
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# -------- Load sample data once --------
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@@ -45,27 +57,31 @@ def load_generator():
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set_seed(RANDOM_STATE)
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return pipeline("text-generation", model="distilgpt2")
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#
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EMBEDDERS = {
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"MiniLM (fast)": "sentence-transformers/all-MiniLM-L6-v2",
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"MPNet (heavier)": "sentence-transformers/all-mpnet-base-v2",
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"DistilRoBERTa (paraphrase)": "sentence-transformers/paraphrase-distilroberta-base-v1",
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}
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# Cache for
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_CORPUS_CACHE = {}
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def
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if model_name in _CORPUS_CACHE:
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return _CORPUS_CACHE[model_name]
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emb = model.encode(texts, show_progress_bar=False, convert_to_numpy=True)
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emb = _l2norm(emb)
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_CORPUS_CACHE[model_name] = emb
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return emb
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def top3_for_each_model(user_input: str, selected_models: list
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df = load_sample_df()
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texts = df["clean_text"].tolist()
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rows = []
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@@ -73,8 +89,7 @@ def top3_for_each_model(user_input: str, selected_models: list[str]):
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try:
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model = load_sentence_model(EMBEDDERS[name])
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corpus_emb = ensure_corpus_embeddings(name, texts)
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q = model
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q = _l2norm(q)
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sims = cosine_similarity(q, corpus_emb)[0]
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top_idx = sims.argsort()[-3:][::-1]
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for rank, i in enumerate(top_idx, start=1):
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@@ -90,35 +105,40 @@ def top3_for_each_model(user_input: str, selected_models: list[str]):
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"Model": name, "Rank": "-", "Similarity": "-",
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"Tweet (clean)": f"[Error: {e}]", "Tweet (orig)": ""
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})
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return out
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gen = load_generator()
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outputs = gen(
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prompt,
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num_return_sequences=n_sequences,
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do_sample=True,
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temperature=temperature,
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pad_token_id=50256,
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)
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candidates = [o["generated_text"].strip() for o in outputs]
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cand_vecs = scorer.encode(candidates, show_progress_bar=False, convert_to_numpy=True); cand_vecs = _l2norm(cand_vecs)
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sims = cosine_similarity(q, cand_vecs)[0]
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best_idx = int(sims.argmax())
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table = pd.DataFrame({
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"Rank": np.argsort(-sims) + 1,
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"Similarity": np.sort(sims)[::-1],
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"Generated Tweet": [c for _, c in sorted(zip(-sims, candidates))]
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})
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return best, best_score, table
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# ---------------- UI ----------------
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with gr.Blocks(title="Sentiment140 Embeddings + Generation") as demo:
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@@ -147,7 +167,7 @@ Type a tweet, get similar tweets from Sentiment140, and generate a new one.
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with gr.Row():
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n_seq = gr.Slider(3, 15, value=8, step=1, label="Number of candidates")
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max_len = gr.Slider(30, 120, value=60, step=1, label="Max length")
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temp = gr.Slider(0.5, 1.5, value=0.9, step=0.05, label="Temperature")
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scorer_model = gr.Dropdown(list(EMBEDDERS.keys()), value="MiniLM (fast)", label="Scorer embedding")
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from datasets import load_dataset
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from sklearn.metrics.pairwise import cosine_similarity
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# -------- Config --------
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SAMPLE_SIZE = int(os.getenv("SAMPLE_SIZE", "5000"))
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RANDOM_STATE = 42
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DEFAULT_INPUT = "I am so happy with this product"
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text = re.sub(r"\s+", " ", text).strip()
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return text
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def _to_numpy(x):
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try:
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import torch
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if hasattr(torch, "Tensor") and isinstance(x, torch.Tensor):
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return x.detach().cpu().numpy()
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except Exception:
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pass
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return np.asarray(x)
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def _l2norm(x: np.ndarray) -> np.ndarray:
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x = x.astype(np.float32, copy=False)
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if x.ndim == 1:
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x = x.reshape(1, -1)
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return x / (np.linalg.norm(x, axis=1, keepdims=True) + 1e-12)
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# -------- Load sample data once --------
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set_seed(RANDOM_STATE)
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return pipeline("text-generation", model="distilgpt2")
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# HF model ids
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EMBEDDERS = {
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"MiniLM (fast)": "sentence-transformers/all-MiniLM-L6-v2",
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"MPNet (heavier)": "sentence-transformers/all-mpnet-base-v2",
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"DistilRoBERTa (paraphrase)": "sentence-transformers/paraphrase-distilroberta-base-v1",
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}
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# Cache for corpus embeddings per model
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_CORPUS_CACHE = {}
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def _encode_norm(model, texts):
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"""Encode with any sentence-transformers version and return (n,d) L2-normalized numpy."""
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out = model.encode(texts, show_progress_bar=False)
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out = _to_numpy(out)
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return _l2norm(out)
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def ensure_corpus_embeddings(model_name: str, texts: list):
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if model_name in _CORPUS_CACHE:
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return _CORPUS_CACHE[model_name]
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model = load_sentence_model(EMBEDDERS[model_name])
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emb = _encode_norm(model, texts)
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_CORPUS_CACHE[model_name] = emb
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return emb
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def top3_for_each_model(user_input: str, selected_models: list):
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df = load_sample_df()
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texts = df["clean_text"].tolist()
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rows = []
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try:
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model = load_sentence_model(EMBEDDERS[name])
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corpus_emb = ensure_corpus_embeddings(name, texts)
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q = _encode_norm(model, [clean_text(user_input)])
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sims = cosine_similarity(q, corpus_emb)[0]
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top_idx = sims.argsort()[-3:][::-1]
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for rank, i in enumerate(top_idx, start=1):
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"Model": name, "Rank": "-", "Similarity": "-",
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"Tweet (clean)": f"[Error: {e}]", "Tweet (orig)": ""
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})
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return pd.DataFrame(rows, columns=["Model","Rank","Similarity","Tweet (clean)","Tweet (orig)"])
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# -------- Generator + scoring (with progress) --------
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def generate_and_pick_best(prompt: str, n_sequences: int, max_length: int,
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temperature: float, scorer_model_name: str,
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progress=gr.Progress()):
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progress(0.0, desc="Loading models…")
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gen = load_generator()
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scorer = load_sentence_model(EMBEDDERS[scorer_model_name])
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progress(0.3, desc="Generating candidates…")
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outputs = gen(
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prompt,
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max_new_tokens=int(max_length), # number of NEW tokens to generate
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num_return_sequences=int(n_sequences),
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do_sample=True,
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temperature=float(temperature),
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pad_token_id=50256,
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)
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candidates = [o["generated_text"].strip() for o in outputs]
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progress(0.7, desc="Scoring candidates…")
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q = _encode_norm(scorer, [prompt])
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cand_vecs = _encode_norm(scorer, candidates)
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sims = cosine_similarity(q, cand_vecs)[0]
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best_idx = int(sims.argmax())
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table = pd.DataFrame({
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"Rank": np.argsort(-sims) + 1,
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"Similarity": np.sort(sims)[::-1],
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"Generated Tweet": [c for _, c in sorted(zip(-sims, candidates))]
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})
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progress(1.0)
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return candidates[best_idx], float(sims[best_idx]), table
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# ---------------- UI ----------------
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with gr.Blocks(title="Sentiment140 Embeddings + Generation") as demo:
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with gr.Row():
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n_seq = gr.Slider(3, 15, value=8, step=1, label="Number of candidates")
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max_len = gr.Slider(30, 120, value=60, step=1, label="Max length (new tokens)")
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temp = gr.Slider(0.5, 1.5, value=0.9, step=0.05, label="Temperature")
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scorer_model = gr.Dropdown(list(EMBEDDERS.keys()), value="MiniLM (fast)", label="Scorer embedding")
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