app.py

import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Set up the device to use CPU only
device = torch.device("cpu")

# Load model and tokenizer, then move the model to the appropriate device
model = AutoModelForCausalLM.from_pretrained("adi2606/MenstrualQA").to(device)
tokenizer = AutoTokenizer.from_pretrained("adi2606/MenstrualQA")

# Function to generate a response from the chatbot
def generate_response(message: str, temperature: float = 0.4, repetition_penalty: float = 1.1) -> str:
    # Apply the chat template and convert to PyTorch tensors
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": message}
    ]
    input_ids = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_tensors="pt"
    ).to(device)

    # Generate the response
    output = model.generate(
        input_ids,
        max_length=512,
        temperature=temperature,
        repetition_penalty=repetition_penalty,
        do_sample=True
    ) 

    # Decode the generated output
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text

# Streamlit app layout
st.title("Menstrual QA Chatbot")
st.write("Ask any question related to menstrual health.")

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

if st.button("Send"):
    if user_input:
        with st.spinner("Generating response..."):
            response = generate_response(user_input)
        st.write(f"Chatbot: {response}")
    else:
        st.write("Please enter a question.")