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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.")
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