import streamlit as st
import requests
import os

# Hugging Face API details
API_URL = "https://api-inference.huggingface.co/models/facebook/blenderbot-400M-distill"

# Access the secret API key stored as 'rag'
api_key = os.getenv('rag')

headers = {"Authorization": f"Bearer {api_key}"}

# Function to query the model
def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

# Streamlit UI for Mental Health Chatbot
st.title("Mental Health Chatbot")
st.write("""
This chatbot provides responses to mental health-related queries. 
Please note that this is an AI-based tool and is not a substitute for professional mental health support.
""")

# User input
user_input = st.text_input("How can I help you today?")

if st.button("Get Response"):
    if user_input:
        # Query the model
        output = query({"inputs": user_input})
        
        # Print the entire output for debugging purposes
        st.write("**API Response:**", output)
        
        # Check the structure of the response and adjust this line accordingly
        if 'generated_text' in output:
            st.write(f"**Response:** {output['generated_text']}")
        elif isinstance(output, list) and len(output) > 0:
            st.write(f"**Response:** {output[0].get('generated_text', 'No response available.')}")
        else:
            st.write("**Response:** Unable to retrieve a valid response.")
    else:
        st.write("Please enter a query to get a response.")