Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from scipy.spatial.distance import cosine
+import pandas as pd
+
+# --- Simulate a small pre-trained Word2Vec model ---
+# Dummy word vectors for demonstration
+dummy_word_vectors = {
+    'cat': np.array([0.9, 0.7, 0.1, 0.2]),
+    'dog': np.array([0.8, 0.8, 0.3, 0.1]),
+    'kitten': np.array([0.85, 0.75, 0.15, 0.25]),
+    'puppy': np.array([0.75, 0.85, 0.25, 0.15]),
+    'fish': np.array([0.1, 0.2, 0.9, 0.8]),
+    'bird': np.array([0.2, 0.1, 0.8, 0.9]),
+    'ocean': np.array([0.05, 0.15, 0.95, 0.85]),
+    'sky': np.array([0.25, 0.05, 0.85, 0.95]),
+    'run': np.array([0.6, 0.3, 0.1, 0.1]),
+    'walk': np.array([0.55, 0.35, 0.15, 0.05]),
+    'jump': np.array([0.65, 0.25, 0.05, 0.15]),
+    'king': np.array([0.9, 0.1, 0.1, 0.8]),
+    'queen': np.array([0.8, 0.2, 0.2, 0.9]),
+    'man': np.array([0.9, 0.15, 0.05, 0.7]),
+    'woman': np.array([0.85, 0.1, 0.15, 0.85])
+}
+
+# Normalize vectors (important for cosine similarity)
+for word, vec in dummy_word_vectors.items():
+    dummy_word_vectors[word] = vec / np.linalg.norm(vec)
+
+# --- Function to find nearest neighbors ---
+def find_nearest_neighbors(search_word_input):
+    search_word = search_word_input.lower()
+
+    if search_word not in dummy_word_vectors:
+        return (
+            pd.DataFrame([{"Message": f"'{search_word}' not found in our dummy vocabulary. Try one of these: {', '.join(list(dummy_word_vectors.keys()))}"}]),
+            "Warning: Word not found!"
+        )
+
+    target_vector = dummy_word_vectors[search_word]
+    similarities = []
+    for word, vector in dummy_word_vectors.items():
+        if word != search_word: # Don't compare a word to itself
+            similarity = 1 - cosine(target_vector, vector)
+            similarities.append({"Word": word, "Cosine Similarity": similarity})
+
+    results_df = pd.DataFrame(similarities).sort_values(
+        by="Cosine Similarity", ascending=False
+    ).reset_index(drop=True)
+
+    # Format the DataFrame for better display in Gradio
+    results_df["Cosine Similarity"] = results_df["Cosine Similarity"].round(4)
+    results_df.columns = ["Neighbor Word", "Similarity Score"] # Rename for UI clarity
+
+    message = f"Found nearest neighbors for '{search_word}'!"
+    return results_df, message
+
+# --- Gradio Interface ---
+iface = gr.Interface(
+    fn=find_nearest_neighbors,
+    inputs=gr.Textbox(
+        label="Enter a word to explore its neighbors:",
+        placeholder="e.g., cat, king, fish"
+    ),
+    outputs=[
+        gr.DataFrame(
+            headers=["Neighbor Word", "Similarity Score"],
+            row_count=5, # Display up to 5 rows by default
+            wrap=True,
+            interactive=False,
+            label="Nearest Neighbors"
+        ),
+        gr.Markdown(
+            label="Status"
+        )
+    ],
+    title="🚀 Word Vector Explorer (Gradio POC)",
+    description=(
+        "Discover the semantic neighbors of words using word embeddings! "
+        "Type a word, and see its closest companions in the vector space."
+        "<br>_Note: This POC uses dummy word vectors. In a full version, this would connect to a large pre-trained Word2Vec model!_"
+    ),
+    allow_flagging="never", # Optional: disables the "Flag" button
+    examples=[
+        ["cat"],
+        ["king"],
+        ["fish"],
+        ["run"]
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()