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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() |