Update app.py

app.py

@@ -2,7 +2,6 @@ import os
 import requests
 import tellurium as te
 import tempfile
-import ollama
 import streamlit as st
 import chromadb
 from langchain_text_splitters import RecursiveCharacterTextSplitter
@@ -158,7 +157,7 @@ def create_vector_db(final_items):
     for item in final_items:
         item2 = str(item)
         item_id = f"id_{item2[:45].replace(' ', '_')}"
-    
+
         item_id_already_created = db.get(item_id) #referenced db here, but it is already initialized?
     
         if item_id_already_created is None:  # If the ID does not exist
@@ -249,21 +248,17 @@ def generate_response(db, query_text, previous_context):
     # Use Streamlit to stream the response in real-time
     full_response = ""
     
-    response_placeholder = st.empty()  # Create a placeholder for streaming output
+    response_placeholder = st.empty()
     
-    # Stream the response token by token
     for token in output_stream:
-        token_text = token["choices"][0]["text"]
-        full_response += token_text
-        
-        # Continuously update the placeholder in real-time with the new token
-        response_placeholder.write(full_response)  
-    
+        full_response += token
+        response_placeholder.text(full_response)
+
     return full_response
 
+
 def streamlit_app():
-    global db 
-    
+    global db
     st.title("BioModelsRAG")
     
     search_str = st.text_input("Enter search query:")
@@ -294,18 +289,18 @@ def streamlit_app():
                     
                     final_items = split_biomodels(antimony_file_path)
                 
-                db = create_vector_db(final_items)  # Create or update the database with final items
+                db = create_vector_db(final_items)
                 
                 st.write("Models have been processed and added to the database.")
     
-    # Cache the chat messages
+    # Cache the chat messages without arguments
     @st.cache_resource
     def get_messages():
         if "messages" not in st.session_state:
             st.session_state.messages = []
         return st.session_state.messages
 
-    st.session_state.messages = get_messages(db)
+    st.session_state.messages = get_messages()
 
     # Display chat history
     for message in st.session_state.messages:
@@ -319,7 +314,7 @@ def streamlit_app():
         st.session_state.messages.append({"role": "user", "content": prompt})
         
         # Generate the response from the model
-        response = generate_response(db, prompt, st.session_state.messages)  # Pass the prompt to generate_response
+        response = generate_response(db, prompt, st.session_state.messages)
         
         # Display assistant response
         with st.chat_message("assistant"):
@@ -327,6 +322,6 @@ def streamlit_app():
 
         # Add the assistant response to the chat history
         st.session_state.messages.append({"role": "assistant", "content": response})
-        
+
 if __name__ == "__main__":
     streamlit_app()