Update app.py

app.py

@@ -52,7 +52,7 @@ days = st.multiselect("What days are you free?",
                options = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat"],
                default = None,
                placeholder = "Any day"
-                )
+        )
 
 assistant = st.chat_message("assistant")
 initial_message = "Hello, I am your GPT-powered USC Class Helper! How can I assist you today?"
@@ -96,7 +96,9 @@ def get_rag_results(prompt):
     )
 
     response, additional_metadata = clean_pinecone(response)
+    print("Here is the pinecone response:", response)
     response = reranker(query, response) # BERT cross encoder for ranking
+    print("Here is the response from the reranker", response)
 
     return response, additional_metadata
 
@@ -122,22 +124,17 @@ if prompt := st.chat_input("What kind of class are you looking for?"):
                 for m in st.session_state.messages]
         message_history = " ".join([message["content"] for message in messages])
 
-        route = routing_agent(prompt, OPENAI_API, message_history)
-
-        if route == "1":
-        ## Option for accessing Vector DB
-            rag_response, additional_metadata = get_rag_results(prompt)
-            result_query = 'Original Query:' + prompt + 'Query Results:' + str(rag_response) + '\n Additional Class Times:' + str(additional_metadata)
-            assistant_response = results_agent(result_query, OPENAI_API)
-        else:    
-        ## Option if not accessing Database
-            assistant_response = openai.ChatCompletion.create(
-                model = "gpt-4",
-                messages = [
-                    {"role": m["role"], "content": m["content"]}
-                    for m in st.session_state.messages
-                ]
-            )["choices"][0]["message"]["content"]
+        rag_response, additional_metadata = get_rag_results(prompt)
+        result_query = 'Original Query:' + prompt + '\nQuery Results:' + str(rag_response) + '\nMessage History:' + str(message_history)
+        # '\n Additional Class Times:' + str(additional_metadata)
+        assistant_response = results_agent(result_query, OPENAI_API)
+            # assistant_response = openai.ChatCompletion.create(
+            #     model = "gpt-4",
+            #     messages = [
+            #         {"role": m["role"], "content": m["content"]}
+            #         for m in st.session_state.messages
+            #     ]
+            # )["choices"][0]["message"]["content"]
 
         ## Display response regardless of route
         for chunk in assistant_response.split():