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demo.py

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+#!/usr/bin/env python3
+"""
+Demo script for using the Shopping Assistant model with the Hugging Face Inference API
+"""
+import requests
+import json
+import argparse
+
+def query_model(text, api_token=None, model_id="selvaonline/shopping-assistant"):
+    """
+    Query the model using the Hugging Face Inference API
+    """
+    api_url = f"https://api-inference.huggingface.co/models/{model_id}"
+    
+    headers = {}
+    if api_token:
+        headers["Authorization"] = f"Bearer {api_token}"
+    
+    payload = {
+        "inputs": text,
+        "options": {
+            "wait_for_model": True
+        }
+    }
+    
+    response = requests.post(api_url, headers=headers, json=payload)
+    
+    if response.status_code == 200:
+        return response.json()
+    else:
+        print(f"Error: {response.status_code}")
+        print(response.text)
+        return None
+
+def process_results(results, text):
+    """
+    Process the results from the Inference API
+    """
+    if not results or not isinstance(results, list) or len(results) == 0:
+        return f"No results found for '{text}'"
+    
+    # The API returns logits, we need to convert them to probabilities
+    import numpy as np
+    
+    # Apply sigmoid to convert logits to probabilities
+    probabilities = 1 / (1 + np.exp(-np.array(results[0])))
+    
+    # Define the categories (should match the model's categories)
+    categories = ["electronics", "clothing", "home", "kitchen", "toys", "other"]
+    
+    # Get the top categories
+    top_categories = []
+    for i, score in enumerate(probabilities):
+        if score > 0.5:  # Threshold for multi-label classification
+            top_categories.append((categories[i], float(score)))
+    
+    # Sort by score
+    top_categories.sort(key=lambda x: x[1], reverse=True)
+    
+    # Format the results
+    if top_categories:
+        result = f"Top categories for '{text}':\n"
+        for category, score in top_categories:
+            result += f"  {category}: {score:.4f}\n"
+        
+        result += f"\nBased on your query, I would recommend looking for deals in the {top_categories[0][0]} category."
+    else:
+        result = f"No categories found for '{text}'. Please try a different query."
+    
+    return result
+
+def main():
+    parser = argparse.ArgumentParser(description="Demo for using the Shopping Assistant model with the Hugging Face Inference API")
+    parser.add_argument("--text", type=str, required=True, help="Text to classify")
+    parser.add_argument("--token", type=str, help="Hugging Face API token")
+    parser.add_argument("--model-id", type=str, default="selvaonline/shopping-assistant", help="Hugging Face model ID")
+    args = parser.parse_args()
+    
+    # Query the model
+    results = query_model(args.text, args.token, args.model_id)
+    
+    # Process and print the results
+    print(process_results(results, args.text))
+
+if __name__ == "__main__":
+    main()