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import gradio as gr |
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import re |
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from functools import lru_cache |
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import gensim.downloader as api |
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from gensim.models import KeyedVectors |
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import pandas as pd |
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MODEL_OPTIONS = { |
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"glove-wiki-gigaword-50": "50d GloVe (Wikipedia+Gigaword) — small & fast", |
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"glove-wiki-gigaword-100": "100d GloVe (Wikipedia+Gigaword) — balanced", |
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"glove-wiki-gigaword-200": "200d GloVe (Wikipedia+Gigaword)", |
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"glove-wiki-gigaword-300": "300d GloVe (Wikipedia+Gigaword)", |
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"word2vec-google-news-300": "300d Google News Word2Vec — large (~1.6GB)" |
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} |
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TOKEN_RE = re.compile(r"[+\-]|[^+\-\s]+") |
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@lru_cache(maxsize=4) |
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def get_model(name: str) -> KeyedVectors: |
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"""Load/download a pre-trained embedding with caching.""" |
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return api.load(name) |
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def parse_expression(expr: str): |
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tokens = TOKEN_RE.findall(expr.strip()) |
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if not tokens: |
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return [], [] |
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pos, neg, sign = [], [], '+' |
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for tok in tokens: |
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tok = tok.strip() |
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if tok in ['+', '-']: |
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sign = tok |
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continue |
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(pos if sign == '+' else neg).append(tok) |
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return pos, neg |
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def compute_expression(model_name: str, expr: str, topn: int, exclude_inputs: bool): |
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try: |
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model = get_model(model_name) |
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except Exception as e: |
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return None, f"❌ Failed to load model '{model_name}': {e}" |
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pos, neg = parse_expression(expr or "") |
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if not pos and not neg: |
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return None, "⚠️ Please enter at least one word." |
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pos_in = [w for w in pos if w in model.key_to_index] |
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neg_in = [w for w in neg if w in model.key_to_index] |
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oov = [w for w in pos + neg if w not in model.key_to_index] |
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if not pos_in and not neg_in: |
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return None, "❌ All words are out-of-vocabulary for this model. Try different words or a different model." |
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try: |
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results = model.most_similar(positive=pos_in, negative=neg_in, topn=topn + len(pos_in) + len(neg_in)) |
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except Exception as e: |
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return None, f"❌ Computation error: {e}" |
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if exclude_inputs: |
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inputs = {w.lower() for w in pos_in + neg_in} |
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results = [(w, s) for (w, s) in results if w.lower() not in inputs] |
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results = results[:topn] |
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df = pd.DataFrame(results, columns=["Word", "Cosine similarity"]) if results else None |
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info_bits = [ |
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f"**Model:** `{model_name}` (dim={model.vector_size})", |
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f"**Positive:** {', '.join(pos_in) if pos_in else '—'}", |
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f"**Negative:** {', '.join(neg_in) if neg_in else '—'}", |
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] |
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if oov: |
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info_bits.append(f"**Out-of-vocabulary skipped:** {', '.join(oov)}") |
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info = "\n\n".join(info_bits) |
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return df, info |
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def compute_abc(model_name: str, a: str, b: str, c: str, topn: int, exclude_inputs: bool): |
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try: |
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model = get_model(model_name) |
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except Exception as e: |
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return None, f"❌ Failed to load model '{model_name}': {e}" |
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used, missing = [], [] |
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vec = None |
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for word, sign in [(a, +1), (b, +1), (c, -1)]: |
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w = (word or '').strip() |
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if not w: |
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continue |
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if w in model.key_to_index: |
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used.append((w, sign)) |
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v = model.get_vector(w) |
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vec = (v if vec is None else vec + sign * v) |
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else: |
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missing.append(w) |
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if vec is None: |
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return None, "❌ No valid words to compute a vector." |
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results = model.similar_by_vector(vec, topn=topn + len(used)) |
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if exclude_inputs: |
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inputs = {w for w, _ in used} |
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results = [(w, s) for (w, s) in results if w not in inputs] |
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results = results[:topn] |
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df = pd.DataFrame(results, columns=["Word", "Cosine similarity"]) if results else None |
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info_bits = [ |
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f"**Model:** `{model_name}` (dim={model.vector_size})", |
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f"**Used:** {', '.join([('+' if s>0 else '−') + w for w,s in used]) if used else '—'}", |
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] |
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if missing: |
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info_bits.append(f"**Out-of-vocabulary skipped:** {', '.join(missing)}") |
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info = "\n\n".join(info_bits) |
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return df, info |
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with gr.Blocks(title="Word Embeddings Playground — Gradio") as demo: |
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gr.Markdown(""" |
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# 🧠 Word Embeddings Playground |
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Type equations like `king + woman - man` and explore nearest words using pre-trained Gensim embeddings. |
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""") |
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with gr.Row(): |
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model_name = gr.Dropdown( |
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choices=list(MODEL_OPTIONS.keys()), |
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value="glove-wiki-gigaword-100", |
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label="Model", |
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info="If downloads stall, try a smaller model first (50d/100d)." |
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) |
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topn = gr.Slider(5, 50, value=10, step=1, label="Top N similar results") |
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exclude_inputs = gr.Checkbox(value=True, label="Exclude input words from results") |
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with gr.Tab("Expression: A + B − C + …"): |
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expr = gr.Textbox(value="king + woman - man", label="Expression (use + and -)") |
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compute_btn = gr.Button("Compute", variant="primary") |
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out_df = gr.Dataframe(headers=["Word", "Cosine similarity"], interactive=False) |
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out_info = gr.Markdown() |
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examples = gr.Examples( |
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examples=[ |
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["king + woman - man"], |
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["paris - france + italy"], |
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["walk + past - present"], |
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["big - bigger + small"], |
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["programmer + woman - man"], |
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], |
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inputs=[expr], |
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label="Examples" |
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) |
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compute_btn.click( |
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fn=compute_expression, |
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inputs=[model_name, expr, topn, exclude_inputs], |
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outputs=[out_df, out_info] |
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) |
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with gr.Tab("Advanced: A + B − C"): |
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with gr.Row(): |
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a = gr.Textbox(value="king", label="Word A (+)") |
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b = gr.Textbox(value="woman", label="Word B (+)") |
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c = gr.Textbox(value="man", label="Word C (−)") |
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compute_btn2 = gr.Button("Compute A + B − C") |
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out_df2 = gr.Dataframe(headers=["Word", "Cosine similarity"], interactive=False) |
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out_info2 = gr.Markdown() |
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compute_btn2.click( |
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fn=compute_abc, |
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inputs=[model_name, a, b, c, topn, exclude_inputs], |
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outputs=[out_df2, out_info2] |
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) |
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gr.Markdown("Built with **Gradio** + **Gensim**. Models load via `gensim.downloader`; first-time downloads can take a while depending on size.") |
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if __name__ == "__main__": |
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demo.launch() |
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