import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Biomistral 7b model and tokenizer
model_name = "biomistral/Biomistral-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# Define the text generation function
def generate_text(prompt, max_length=500, num_return_sequences=1, temperature=0.7):
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    output = model.generate(
        input_ids,
        max_length=max_length,
        num_return_sequences=num_return_sequences,
        temperature=temperature,
        pad_token_id=tokenizer.eos_token_id,
    )
    generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)
    return generated_text

# Streamlit app
def main():
    st.title("Doctor Chatbot (Powered by Biomistral 7b)")
    st.write("Welcome to the Doctor Chatbot. Please describe your symptoms or ask a medical question, and I'll provide a response.")

    user_input = st.text_area("Enter your symptoms or question:")

    if user_input:
        with st.spinner("Generating response..."):
            generated_text = generate_text(user_input)
            st.write(generated_text[0])

if __name__ == "__main__":
    main()