#!/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()