import os

# OPENAI_API_KEY = os.environ['Open_AI_Key']
# HF_Key = os.environ['HF_Key']

print('OPENAI_API_KEY' in os.environ)
print('HF_Key' in os.environ)

print(os.environ['OPENAI_API_KEY'])
print(os.environ['HF_Key'])

import openai
import json



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


# # handling data on space

# from huggingface_hub import HfFileSystem
# fs = HfFileSystem(token=HF_Key)

# text_list = fs.ls("datasets/GoChat/Gochat247_Data/Data", detail=False)

# data = fs.read_text(text_list[0])

# from llama_index import Document
# doc = Document(data)
# docs = []
# docs.append(doc)


# # 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(docs)

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

#     def __init__(self, index):
#         self.index = index
#         openai.api_key = OPENAI_API_KEY
#         self.chat_history = []
          
#         QA_PROMPT_TMPL = (
#         "Answer without 'Answer:' word."
#         "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(index=index)

# import webbrowser

# import gradio as gr
# import time

# with gr.Blocks(theme='SebastianBravo/simci_css') as demo:
#     with gr.Column(scale=4):
#         title = 'GoChat247 AI BOT'
#         chatbot = gr.Chatbot(label='GoChat247 AI BOT')
#         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)
#         print(webbrowser.get())

# # handling dark_theme



# # def apply_dark_theme(url):
# #     if not url.endswith('?__theme=dark'):
# #         webbrowser.open_new(url + '?__theme=dark')

# # gradioURL = 'http://localhost:7860/'
# # apply_dark_theme(gradioURL)

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