Create app.py

app.py

@@ -0,0 +1,49 @@
+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)