Update app.py

app.py

@@ -256,9 +256,16 @@ def run_streamlit_app():
         azure_api_key = st.text_input("Azure API Key")
         groq_api_key = st.text_input("Groq API Key")
 
+    def update_api_based_on_model():
+        selected_model = st.session_state['selected_model']
+        if selected_model == 'gpt35':
+            st.session_state['selected_api'] = 'azure'
+        else:
+            st.session_state['selected_api'] = 'groq'
+
     with col2:
-        selected_api = st.selectbox("Select API", ["azure", "groq"])
-        selected_model = st.selectbox("Select Model", ["llama3-8b", "llama3-70b", "mixtral-8x7b", "gemma-7b", "gpt35"])
+        selected_model = st.selectbox("Select Model", ["llama3-8b", "llama3-70b", "mixtral-8x7b", "gemma-7b", "gpt35"], key='selected_model', on_change=update_api_based_on_model)
+        selected_api = st.selectbox("Select API", ["azure", "groq"], key='selected_api', disabled=True)
         embedding_model_type = "HF"
         embedding_model = st.selectbox("Select Embedding Model", ["BAAI/bge-large-en-v1.5", "other_model"])
         retriever_method = st.selectbox("Select Retriever Method", ["Vector Search", "BM25", "BM25+Vector"])
@@ -270,7 +277,7 @@ def run_streamlit_app():
         top_k = st.selectbox("Select Top K", [1, 2, 3, 5, 6], index=1)
 
     if st.button("Initialize"):
-        initialize_apis(selected_api, selected_model, pinecone_api_key, groq_api_key, azure_api_key)
+        initialize_apis(st.session_state['selected_api'], selected_model, pinecone_api_key, groq_api_key, azure_api_key)
         documents = load_pdf_data(chunk_size)
         index, nodes = create_index(documents, embedding_model_type=embedding_model_type, embedding_model=embedding_model, retriever_method=retriever_method, chunk_size=chunk_size)
         st.session_state.query_engine = setup_query_engine(index, response_mode="compact", nodes=nodes, query_engine_method=None, retriever_method=retriever_method, top_k=top_k)
@@ -325,7 +332,7 @@ def run_streamlit_app():
             st.session_state.chat_history.append({'id': question_id, 'user': question, 'response': response.response, 'contexts': response.source_nodes, 'feedback': 0, 'detailed_feedback': '', 'timestamp': timestamp})
             
             # Log initial query and response to Google Sheets without feedback
-            log_to_google_sheets([question_id, question, response.response, selected_api, selected_model, embedding_model, retriever_method, chunk_size, top_k, 0, "", timestamp])
+            log_to_google_sheets([question_id, question, response.response, st.session_state['selected_api'], selected_model, embedding_model, retriever_method, chunk_size, top_k, 0, "", timestamp])
             
             st.rerun()
         else: