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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()
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