app.py

import uuid
from wrappers import *
from embedding_loader import *
from initialize_db import QdrantClientInitializer
from pdf_loader import PDFLoader
from IPython.display import display, Markdown
import gradio as gr
from langchain_core.messages import HumanMessage, AIMessage
from langchain.memory import ConversationBufferMemory
from langchain_core.chat_history import InMemoryChatMessageHistory

embeddings = import_embedding()
AZURE_OPENAI_KEY = os.getenv('azure_api')
os.environ['AZURE_OPENAI_KEY'] = AZURE_OPENAI_KEY
openai.api_version = "2024-02-15-preview" # change it with your own version
openai.azure_endpoint = os.getenv('azure_endpoint')
model = "gpt35turbo" # deployment name on Azure OPENAI Studio
myLLM = AzureChatOpenAI(azure_endpoint = openai.azure_endpoint, 
                            api_key=AZURE_OPENAI_KEY,  
                            api_version=openai.api_version,
                            temperature=0,
                            streaming=True, 
                            model = model,)

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}
  ### Answer: 
  {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))
        prompt.append(AIMessage(ai_message))
    
    chat_history = InMemoryChatMessageHistory(messages=prompt)
    memory = ConversationBufferMemory(chat_memory=chat_history, memory_key="history", input_key="question")
    return memory

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, 
                   "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[manual]

    db = obj_loader.load_from_database(embeddings=embeddings, collection_name=collection_name)
    
    CAR_ID = manual_list[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}:")
    
    response = value["generation"]['text']
    history.append((question, response))

    # 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 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')

    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()