Update app.py

app.py

@@ -175,6 +175,11 @@ def ask_bot(query):
     # Kombiniere den standardmäßigen Prompt mit der Benutzeranfrage
     full_query = standard_prompt + query
     return full_query
+
+# Funktion zum Kombinieren der Chat-Historie mit der aktuellen Anfrage
+def combine_history_with_query(current_query, history):
+    combined_history = " ".join([f"{chat[0]}: {chat[1]}" for chat in history])
+    return combined_history + " " + current_query
     
 
 def page1():
@@ -240,34 +245,50 @@ def page1():
                     query = "Was bedeutet die Vorhaltefinanzierung?"
 
 
-        
             if query:
                 full_query = ask_bot(query)
-                st.session_state['chat_history_page1'].append(("User", query, "new"))
+                # Kombiniere die gesamte Session-Historie mit der aktuellen Anfrage
+                combined_query = combine_history_with_query(full_query, st.session_state['chat_history_page1'])
+    
+                with st.spinner('Bot is thinking...'):
+                    chain = load_chatbot()
+                    docs = VectorStore.similarity_search(query=combined_query, k=5)
+                    with get_openai_callback() as cb:
+                        response = chain.run(input_documents=docs, question=combined_query)
+                        response = handle_no_answer(response)
+    
+                # Aktualisiere die Chat-Historie nach der Antwort des Bots
+                st.session_state['chat_history_page1'].append(("User", query))
+                st.session_state['chat_history_page1'].append(("Bot", response))
+
+            
+            #if query:
+                #full_query = ask_bot(query)
+                #st.session_state['chat_history_page1'].append(("User", query, "new"))
 
                 # Start timing
-                start_time = time.time()
+                #start_time = time.time()
 
                 
-                with st.spinner('Bot is thinking...'):
-                    chain = load_chatbot()
-                    docs = VectorStore.similarity_search(query=query, k=5)
-                    with get_openai_callback() as cb:
-                        response = chain.run(input_documents=docs, question=full_query)
-                        response = handle_no_answer(response)  # Process the response through the new function
+                #with st.spinner('Bot is thinking...'):
+                    #chain = load_chatbot()
+                    #docs = VectorStore.similarity_search(query=query, k=5)
+                    #with get_openai_callback() as cb:
+                        #response = chain.run(input_documents=docs, question=full_query)
+                        #response = handle_no_answer(response)  # Process the response through the new function
 
 
                         
                 # Stop timing
-                end_time = time.time()
+                #end_time = time.time()
                 
                 # Calculate duration
-                duration = end_time - start_time
+                #duration = end_time - start_time
 
                 # You can use Streamlit's text function to display the timing
-                st.text(f"Response time: {duration:.2f} seconds")
+                #st.text(f"Response time: {duration:.2f} seconds")
     
-                st.session_state['chat_history_page1'].append(("Bot", response, "new"))
+                #st.session_state['chat_history_page1'].append(("Bot", response, "new"))
     
     
                 # Display new messages at the bottom