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import streamlit as st
from transformers import pipeline
# Load the BioGPT model from HuggingFace or another medical GPT model
# BioGPT has been fine-tuned on medical data and should provide better responses
generator = pipeline("text-generation", model="microsoft/BioGPT")
# Streamlit app title
st.title("24/7Dr. Health Chatbot")
# Initialize session state for conversation history
if 'history' not in st.session_state:
st.session_state.history = []
# Function to generate chatbot responses using BioGPT
def generate_medical_response(user_input):
# Generate a response using BioGPT (or another medical 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']
# Input box for user symptoms
user_input = st.text_input("Describe your symptoms:")
if st.button("Ask"):
if user_input:
# Store the user's input in the conversation history
st.session_state.history.append(f"You: {user_input}")
# Generate the chatbot's response using the BioGPT model
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
user_input = ""
# Display the conversation history
if st.session_state.history:
st.subheader("Conversation History")
for message in st.session_state.history:
st.write(message)
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