import streamlit as st
from transformers import AutoModelForCausalLM
import torch
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer

# Load the model and tokenizer
model_name = "GRMenon/mental-health-mistral-7b-instructv0.2-finetuned-V2"
tokenizer = MistralTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def get_bot_response(user_input):
    # Tokenize user input
    inputs = tokenizer.encode(user_input, return_tensors="pt")

    # Generate response
    try:
        bot_response = model.generate(inputs, max_length=100, num_return_sequences=1)
    except Exception as e:
        return f"Sorry, an error occurred while generating a response: {e}"

    # Decode and return response
    return tokenizer.decode(bot_response[0], skip_special_tokens=True)

def main():
    st.title("Mental Health Chatbot")
    
    st.write("Welcome to the mental health chatbot. You can ask questions or share your thoughts, and I'll try to provide helpful responses.")
    
    user_input = st.text_input("Type your message here:", value="", key="user_input")
    
    if user_input:
        st.write(f"You: {user_input}")
        
        bot_response = get_bot_response(user_input)
        
        st.write(f"Chatbot: {bot_response}")

if __name__ == "__main__":
    main()