import os
import requests
import streamlit as st

# Retrieve API keys from environment variables
HF_TOKEN = os.getenv("HF_TOKEN", "default_hf_token")

# Initialize Hugging Face API endpoint
HF_MODEL_URL = "https://api-inference.huggingface.co/models/Xenova/gpt-3.5-turbo"

# Function to get response from Hugging Face model
def get_response(user_query: str) -> str:
    """Get a response from the Hugging Face model for the given user query."""
    try:
        headers = {"Authorization": f"Bearer {HF_TOKEN}"}
        payload = {"inputs": user_query}
        response = requests.post(HF_MODEL_URL, headers=headers, json=payload)
        response.raise_for_status()
        result = response.json()
        
        # Check if result is a list and handle accordingly
        if isinstance(result, list):
            response_text = result[0].get("generated_text", "No response generated.")
        else:
            response_text = "Unexpected response format."
        
        return response_text

    except Exception as e:
        return f"Error: {e}"

# Streamlit UI for customer support chatbot
st.title("Customer Support Chatbot")

user_query = st.text_input("Enter your query:", "")

if st.button("Get Response"):
    with st.spinner("Processing..."):
        try:
            # Call the get_response function
            response = get_response(user_query)
            st.subheader("Chatbot Response")
            st.write(response)
        except Exception as e:
            st.error(f"Error fetching response: {e}")