Create app.py

app.py

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+
+# app.py
+
+import streamlit as st
+from transformers import pipeline
+
+# 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()