app.py

import gradio as gr
import requests
import json
import os

# Set your API key as an environment variable
# Important: In the Hugging Face Space, set this as a secret
HF_API_KEY = os.environ.get("HF_API_KEY", "")

def analyze_shampoo(prompt):
    """
    Send the prompt to the Hugging Face Inference API instead of loading the model locally
    """
    API_URL = "https://api-inference.huggingface.co/models/microsoft/phi-3-mini-4k-instruct"
    headers = {
        "Authorization": f"Bearer {HF_API_KEY}",
        "Content-Type": "application/json"
    }
    
    payload = {
        "inputs": prompt,
        "parameters": {
            "max_new_tokens": 2048,
            "temperature": 0.7,
            "top_p": 0.95,
            "return_full_text": False
        }
    }
    
    try:
        if not HF_API_KEY:
            return "Error: No API key provided. Please add the HF_API_KEY secret to your Space."
            
        response = requests.post(API_URL, headers=headers, json=payload)
        response.raise_for_status()  # Raise an exception for HTTP errors
        
        result = response.json()
        if isinstance(result, list) and len(result) > 0:
            # Extract the generated text
            return result[0].get('generated_text', 'No response generated')
        else:
            return f"Unexpected response format: {result}"
            
    except Exception as e:
        return f"Error: {str(e)}"

# Create the Gradio interface
interface = gr.Interface(
    fn=analyze_shampoo,
    inputs=gr.Textbox(lines=10, placeholder="Enter your analysis prompt here..."),
    outputs=gr.Textbox(lines=20),
    title="Phi-3 Shampoo Analyzer",
    description="Analyze shampoo ingredients based on user profiles using the Hugging Face Inference API"
)

interface.launch()