Update app.py

app.py

@@ -26,6 +26,83 @@ obj_qdrant = QdrantClientInitializer()
 client = obj_qdrant.initialize_db()
 obj_loader = PDFLoader()
 
+# def print_result(question, result):
+#   output_text = f"""### Question: 
+#   {question}
+#   ### Answer: 
+#   {result}
+#   """
+#   return(output_text)
+
+# def format_chat_prompt(chat_history):
+#     prompt = []
+
+#     for turn in chat_history:
+#         user_message, ai_message = turn
+#         prompt.append(HumanMessage(user_message))
+#         prompt.append(AIMessage(ai_message))
+    
+#     chat_history = InMemoryChatMessageHistory(messages=prompt)
+#     memory = ConversationBufferMemory(chat_memory=chat_history, memory_key="history", input_key="question")
+#     return memory
+
+# def chat(question, manual, history):
+#     history = history or []
+#     memory = format_chat_prompt(history)
+#     manual_list = {"Toyota_Corolla_2024_TR": -8580416610875007536, 
+#                    "Renault_Clio_2024_TR":-5514489544983735006, 
+#                    "Fiat_Egea_2024_TR":-2026113796962100812}
+    
+#     collection_list = {"Toyota_Corolla_2024_TR": "TOYOTA_MANUAL_COLLECTION_EMBED3", 
+#                    "Renault_Clio_2024_TR": "RENAULT_MANUAL_COLLECTION_EMBED3", 
+#                    "Fiat_Egea_2024_TR": "FIAT_MANUAL_COLLECTION_EMBED3"}
+    
+#     collection_name = collection_list[f"{manual}"]
+
+#     db = obj_loader.load_from_database(embeddings=embeddings, collection_name=collection_name)
+    
+#     CAR_ID = manual_list[f"{manual}"]
+#     wrapper = Wrappers(collection_name, client, embeddings, myLLM, db, CAR_ID, memory)
+    
+#     inputs = {"question": question, "iter_halucination": 0}
+#     app = wrapper.lagchain_graph()
+
+#     for output in app.stream(inputs):
+#         for key, value in output.items():
+#             pprint(f"Finished running: {key}:")
+#     # display(Markdown(print_result(question, value["generation"]['text']))) 
+#     response = value["generation"]['text']
+#     history.append((question, response))
+
+#     point_id = uuid.uuid4().hex
+#     DatabaseOperations.save_user_history_demo(client, "USER_COLLECTION_EMBED3", question, response, embeddings, point_id, manual)
+
+#     return '', history
+
+# def vote(data: gr.LikeData):
+#     if data.liked:
+#         print("You upvoted this response: ")
+#         return "OK"
+#     else:
+#         print("You downvoted this response: " )
+#         return "NOK"
+
+
+
+# manual_list = ["Toyota_Corolla_2024_TR", "Renault_Clio_2024_TR", "Fiat_Egea_2024_TR"]
+
+# with gr.Blocks() as demo:
+
+#     chatbot = gr.Chatbot(height=600)
+#     manual = gr.Dropdown(label="Kullanım Kılavuzları", value="Toyota_Corolla_2024_TR", choices=manual_list)
+#     textbox = gr.Textbox()
+#     clear = gr.ClearButton(components=[textbox, chatbot], value='Clear console')
+#     textbox.submit(chat, [textbox, manual, chatbot], [textbox, chatbot])
+#     chatbot.like(vote, None, None)  # Adding this line causes the like/dislike icons to appear in your chatbot
+
+# # gr.close_all()   
+# demo.launch(share=True)
+
 def print_result(question, result):
   output_text = f"""### Question: 
   {question}
@@ -34,9 +111,11 @@ def print_result(question, result):
   """
   return(output_text)
 
+
 def format_chat_prompt(chat_history):
     prompt = []
 
+    print(chat_history)
     for turn in chat_history:
         user_message, ai_message = turn
         prompt.append(HumanMessage(user_message))
@@ -46,7 +125,10 @@ def format_chat_prompt(chat_history):
     memory = ConversationBufferMemory(chat_memory=chat_history, memory_key="history", input_key="question")
     return memory
 
-def chat(question, manual, history):
+liked_state = gr.State(None)
+last_interaction = gr.State(None)
+
+def chat(question, manual, history, liked):
     history = history or []
     memory = format_chat_prompt(history)
     manual_list = {"Toyota_Corolla_2024_TR": -8580416610875007536, 
@@ -54,14 +136,14 @@ def chat(question, manual, history):
                    "Fiat_Egea_2024_TR":-2026113796962100812}
     
     collection_list = {"Toyota_Corolla_2024_TR": "TOYOTA_MANUAL_COLLECTION_EMBED3", 
-                   "Renault_Clio_2024_TR": "RENAULT_MANUAL_COLLECTION_EMBED3", 
-                   "Fiat_Egea_2024_TR": "FIAT_MANUAL_COLLECTION_EMBED3"}
+                    "Renault_Clio_2024_TR": "RENAULT_MANUAL_COLLECTION_EMBED3", 
+                    "Fiat_Egea_2024_TR": "FIAT_MANUAL_COLLECTION_EMBED3"}
     
-    collection_name = collection_list[f"{manual}"]
+    collection_name = collection_list[manual]
 
     db = obj_loader.load_from_database(embeddings=embeddings, collection_name=collection_name)
     
-    CAR_ID = manual_list[f"{manual}"]
+    CAR_ID = manual_list[manual]
     wrapper = Wrappers(collection_name, client, embeddings, myLLM, db, CAR_ID, memory)
     
     inputs = {"question": question, "iter_halucination": 0}
@@ -70,36 +152,54 @@ def chat(question, manual, history):
     for output in app.stream(inputs):
         for key, value in output.items():
             pprint(f"Finished running: {key}:")
-    # display(Markdown(print_result(question, value["generation"]['text']))) 
+    
     response = value["generation"]['text']
     history.append((question, response))
 
-    point_id = uuid.uuid4().hex
-    DatabaseOperations.save_user_history_demo(client, "USER_COLLECTION_EMBED3", question, response, embeddings, point_id, manual)
+    # Store the last interaction without saving to the database yet
+    last_interaction.value = {
+        "question": question,
+        "response": response,
+        "manual": manual,
+        "point_id": uuid.uuid4().hex
+    }
 
     return '', history
 
-def vote(data: gr.LikeData):
-    if data.liked:
-        print("You upvoted this response: ")
-        return "OK"
-    else:
-        print("You downvoted this response: " )
-        return "NOK"
-
-
+def save_last_interaction(feedback):
+    if last_interaction.value:
+        DatabaseOperations.save_user_history_demo(
+            client,
+            "USER_COLLECTION_EMBED3",
+            last_interaction.value["question"],
+            last_interaction.value["response"],
+            embeddings,
+            last_interaction.value["point_id"],
+            last_interaction.value["manual"],
+            feedback
+        )
+        last_interaction.value = None
 
 manual_list = ["Toyota_Corolla_2024_TR", "Renault_Clio_2024_TR", "Fiat_Egea_2024_TR"]
 
 with gr.Blocks() as demo:
-
     chatbot = gr.Chatbot(height=600)
     manual = gr.Dropdown(label="Kullanım Kılavuzları", value="Toyota_Corolla_2024_TR", choices=manual_list)
     textbox = gr.Textbox()
     clear = gr.ClearButton(components=[textbox, chatbot], value='Clear console')
-    textbox.submit(chat, [textbox, manual, chatbot], [textbox, chatbot])
-    chatbot.like(vote, None, None)  # Adding this line causes the like/dislike icons to appear in your chatbot
 
-# gr.close_all()   
-demo.launch(share=True)
+    def handle_like(data: gr.LikeData):
+        liked_state.value = data.liked
+        if liked_state.value is not None:
+            feedback = "LIKE" if liked_state.value else "DISLIKE"
+        save_last_interaction(feedback)
+
+    def gradio_chat(question, manual, history):
+        save_last_interaction("N/A")  # Save previous interaction before starting a new one
+        return chat(question, manual, history, liked_state.value)
+
+    textbox.submit(gradio_chat, [textbox, manual, chatbot], [textbox, chatbot])
+    chatbot.like(handle_like, None, None)
+    
+demo.launch()