Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer, util
+import torch
+
+# Load the SentenceTransformer model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+def find_relevant_words(query, data, top_k):
+    # Convert data string to list
+    word_list = [word.strip() for word in data.split(',')]
+    
+    # Create embeddings
+    query_embedding = model.encode(query, convert_to_tensor=True)
+    word_embeddings = model.encode(word_list, convert_to_tensor=True)
+    
+    # Compute cosine similarities
+    cos_scores = util.cos_sim(query_embedding, word_embeddings)[0]
+    
+    # Get top-k results
+    top_results = torch.topk(cos_scores, k=min(top_k, len(word_list)))
+    
+    results = []
+    for score, idx in zip(top_results.values, top_results.indices):
+        results.append(f"{word_list[idx]} (Score: {score:.4f})")
+    
+    return "\n".join(results)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=find_relevant_words,
+    inputs=[
+        gr.Textbox(label="Query"),
+        gr.Textbox(label="Data (comma-separated words)"),
+        gr.Slider(minimum=1, maximum=20, step=1, label="Top K", value=5)
+    ],
+    outputs=gr.Textbox(label="Results"),
+    title="Semantic Word Relevance Finder",
+    description="Enter a query and a list of words to find the most semantically relevant words."
+)
+
+# Launch the app
+iface.launch()