import streamlit as st
import requests

# Set your Hugging Face API key here
API_KEY = 'your_huggingface_api_key'
MODEL_URL = "https://api-inference.huggingface.co/models/microsoft/DialoGPT-medium"

# Function to get chatbot response from Hugging Face API
def query_huggingface_api(message):
    headers = {"Authorization": f"Bearer {API_KEY}"}
    payload = {"inputs": message}
    
    response = requests.post(MODEL_URL, headers=headers, json=payload)
    response_json = response.json()
    
    # Extract and return the chatbot response
    if isinstance(response_json, list):
        return response_json[0]['generated_text']
    else:
        return "Error: Unable to get response from the model."

# Set up Streamlit UI
st.title("Learning Chatbot")
st.subheader("Ask me anything related to learning!")

# User input
user_message = st.text_input("You: ")

# Initialize the conversation history in session state
if "history" not in st.session_state:
    st.session_state.history = []

if user_message:
    # Append the user's message to the conversation history
    st.session_state.history.append(f"You: {user_message}")

    # Combine the history for context in the conversation
    conversation_history = " ".join(st.session_state.history)

    # Query the Hugging Face API to get the response
    bot_response = query_huggingface_api(conversation_history)

    # Append the bot's response to the history
    st.session_state.history.append(f"Bot: {bot_response}")

    # Show conversation history
    for message in st.session_state.history:
        st.write(message)