gradio_demo.py

#!/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()