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# app.py
import streamlit as st
from transformers import pipeline
import torch
# Initialize the BioGPT model using the Hugging Face pipeline
generator = pipeline("text-generation", model="microsoft/BioGPT")
# Streamlit app title and description
st.title("24/7Dr. Health Chatbot")
st.markdown("""
This is a health chatbot that can provide responses based on the symptoms you describe.
It uses a medical GPT model to generate responses and help guide your understanding.
""")
# Initialize session state for conversation history if it does not exist
if 'history' not in st.session_state:
st.session_state.history = []
# Function to generate chatbot responses using BioGPT
def generate_medical_response(user_input):
"""
Generates a response using BioGPT model based on user input (symptoms).
Args:
user_input (str): The symptoms or health-related query from the user.
Returns:
str: The generated response from the BioGPT model.
"""
response = generator(user_input,
max_length=150,
num_return_sequences=1,
pad_token_id=50256,
truncation=True,
temperature=0.7,
top_k=50,
top_p=0.95)
return response[0]['generated_text']
def display_conversation_history():
"""Display the conversation history in the app."""
if st.session_state.history:
st.subheader("Conversation History")
for message in st.session_state.history:
st.write(message)
def main():
"""Main function to run the Streamlit app."""
# Input box for user to describe symptoms
user_input = st.text_input("Describe your symptoms:")
# When the 'Ask' button is pressed
if st.button("Ask"):
if user_input: # Check if user input is not empty
# Store the user's input in the conversation history
st.session_state.history.append(f"You: {user_input}")
# Generate the chatbot's response using BioGPT
bot_response = generate_medical_response(user_input)
# Store the chatbot's response in the conversation history
st.session_state.history.append(f"Bot: {bot_response}")
# Clear the input box after submission (optional for improved UX)
st.text_input("Describe your symptoms:", "", key="clear_input")
# Display the conversation history on the Streamlit app
display_conversation_history()
if __name__ == "__main__":
main()
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