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, max_input_length: int = 256) -> str:
    inputs = tokenizer(
        message, 
        return_tensors="pt", 
        padding=True, 
        truncation=True, 
        max_length=max_input_length
    ).to(device)

    # Generate the response
    output = model.generate(
        inputs['input_ids'],
        attention_mask=inputs['attention_mask'],
        max_length=512,
        temperature=temperature,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    ) 

    # 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.")