import streamlit as st
import os
import requests

# Define Hugging Face API details
API_URL = "https://api-inference.huggingface.co/models/Huzaifa367/chat-summarizer"
API_TOKEN = os.getenv("AUTH_TOKEN")
HEADERS = {"Authorization": f"Bearer {API_TOKEN}"}

# Function to query Hugging Face API
def query_huggingface(payload):
    try:
        response = requests.post(API_URL, headers=HEADERS, json=payload)
        response.raise_for_status()  # Raise exception for non-2xx status codes
        return response.json()
    except requests.exceptions.RequestException as e:
        st.error(f"Error querying Hugging Face API: {e}")
        return {"summary_text": f"Error querying Hugging Face API: {e}"}


def main():
    
    st.set_page_config(layout="centered")
    st.title("Chat Summarizer")

    # User input for chat message
    user_message = st.text_input("User Message", "Enter your message here...")

    # Process user input and query Hugging Face API
    if st.button("Summarize"):
        if user_message:
            # Construct input text for summarization (no system message)
            input_text = f"User: {user_message}"

            # Query Hugging Face API for summarization
            payload = {"inputs": input_text}
            response = query_huggingface(payload)

            # Extract summary text from the API response
            summary_text = response[0]["summary_text"] if isinstance(response, list) else response.get("summary_text", "")

            # Display summary text
            st.text_area("Summary", value=summary_text)

    with st.sidebar:        
        if st.button("Chat With PDF"):
            st.switch_page('app.py')

if __name__ == "__main__":
    main()