Upload gradio_demo.py with huggingface_hub

gradio_demo.py

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+#!/usr/bin/env python3
+"""
+Gradio demo for the Shopping Assistant model
+"""
+import gradio as gr
+import requests
+import numpy as np
+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
+    
+    # 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\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 classify_query(query, api_token=None, model_id="selvaonline/shopping-assistant"):
+    """
+    Classify a shopping query using the model
+    """
+    results = query_model(query, api_token, model_id)
+    return process_results(results, query)
+
+def create_gradio_interface(api_token=None, model_id="selvaonline/shopping-assistant"):
+    """
+    Create a Gradio interface for the Shopping Assistant model
+    """
+    # Define the interface
+    demo = gr.Interface(
+        fn=lambda query: classify_query(query, api_token, model_id),
+        inputs=gr.Textbox(
+            lines=2, 
+            placeholder="Enter your shopping query here...",
+            label="Shopping Query"
+        ),
+        outputs=gr.Markdown(label="Results"),
+        title="Shopping Assistant",
+        description="""
+        This demo shows how to use the Shopping Assistant model to classify shopping queries into categories.
+        Enter a shopping query below to see which categories it belongs to.
+        
+        Examples:
+        - "I'm looking for headphones"
+        - "Do you have any kitchen appliance deals?"
+        - "Show me the best laptop deals"
+        - "I need a new smart TV"
+        """,
+        examples=[
+            ["I'm looking for headphones"],
+            ["Do you have any kitchen appliance deals?"],
+            ["Show me the best laptop deals"],
+            ["I need a new smart TV"]
+        ],
+        theme=gr.themes.Soft()
+    )
+    
+    return demo
+
+def main():
+    parser = argparse.ArgumentParser(description="Gradio demo for the Shopping Assistant model")
+    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")
+    parser.add_argument("--share", action="store_true", help="Create a public link")
+    args = parser.parse_args()
+    
+    print(f"Starting Gradio demo for model {args.model_id}")
+    demo = create_gradio_interface(args.token, args.model_id)
+    demo.launch(share=args.share)
+
+if __name__ == "__main__":
+    main()