# import logging
import os
os.environ['OPENAI_API_KEY'] = "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"
import openai
import json


# create a logger with a file handler
# logger = logging.getLogger("chatbot_logger")
# handler = logging.FileHandler("chatbot.log")
# logger.addHandler(handler)
# logger.setLevel(logging.INFO)

from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext, QuestionAnswerPrompt
from langchain import OpenAI


documents = SimpleDirectoryReader('https://huggingface.co/spaces/waelabou/Gochat247Demo/tree/main/Data_Gochat').load_data()


# Setup your LLM


# define LLM
llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="text-davinci-003"))

# define prompt helper
# set maximum input size
max_input_size = 4096
# set number of output tokens
num_output = 256
# set maximum chunk overlap
max_chunk_overlap = 20
prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)

service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)


index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)

       
## Define Chat BOT Class to generate Response , handle chat history, 
class Chatbot:

    def __init__(self, api_key, index):
        self.index = index
        openai.api_key = api_key
        self.chat_history = []
          
        QA_PROMPT_TMPL = (
        "Answer without 'Answer:' word please."
        "you are in a converation with Gochat247's web site visitor\n"
        "user got into this conversation to learn more about Gochat247"
        "you will act like Gochat247 Virtual AI BOT. Be friendy and welcoming\n"
        # "you will be friendy and welcoming\n"
        "The Context of the conversstion should be always limited to learing more about Gochat247 as a company providing Business Process Outosuricng and AI Customer expeeince soltuion /n"
        "The below is the previous chat with the user\n"
        "---------------------\n"
        "{context_str}"
        "\n---------------------\n"
        "Given the context information and the chat history, and not prior knowledge\n"
        "\nanswer the question : {query_str}\n"
        "\n it is ok if you don not know the answer. and ask for infomration \n"
        "Please provide a brief and concise but friendly response."
        


                           ) 
          
        self.QA_PROMPT = QuestionAnswerPrompt(QA_PROMPT_TMPL)
        

    def generate_response(self, user_input):
        
        prompt = "\n".join([f"{message['role']}: {message['content']}" for message in self.chat_history[-5:]])
        prompt += f"\nUser: {user_input}"
        self.QA_PROMPT.context_str = prompt
        response = index.query(user_input, text_qa_template=self.QA_PROMPT
                                          )

        message = {"role": "assistant", "content": response.response}
        self.chat_history.append({"role": "user", "content": user_input})
        self.chat_history.append(message)
        return message
   
    def load_chat_history(self, filename):
        try:
            with open(filename, 'r') as f:
             self.chat_history = json.load(f)
        except FileNotFoundError:
            pass

    def save_chat_history(self, filename):
        with open(filename, 'w') as f:
            json.dump(self.chat_history, f)
  

## Define Chat BOT Class to generate Response , handle chat history, 

bot = Chatbot("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", index=index)


import gradio as gr
import time


with gr.Blocks() as demo:
    chatbot = gr.Chatbot(label="GoChat247_Demo")
    msg = gr.Textbox()
    clear = gr.Button("Clear")
    
    
    def user(user_message, history):
        return "", history + [[user_message, None]]

    def agent(history):
        last_user_message = history[-1][0]
        agent_message = bot.generate_response(last_user_message)
        history[-1][1] = agent_message ["content"]
        time.sleep(1)
        return history

    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        agent, chatbot, chatbot
    )
    clear.click(lambda: None, None, chatbot, queue=False)

    


if __name__ == "__main__":
    demo.launch()