Update app.py

app.py

@@ -1,139 +1,144 @@
 
 import os
-OPENAI_API_KEY = os.environ['Open_AI_Key']
-HF_Key = os.environ['HF_Key']
+
+# 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)
+
 import openai
 import json
 
 
 
-from llama_index import GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext, QuestionAnswerPrompt
-from langchain import OpenAI
+# from llama_index import GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext, QuestionAnswerPrompt
+# from langchain import OpenAI
 
 
-# handling data on space
+# # handling data on space
 
-from huggingface_hub import HfFileSystem
-fs = HfFileSystem(token=HF_Key)
+# from huggingface_hub import HfFileSystem
+# fs = HfFileSystem(token=HF_Key)
 
-text_list = fs.ls("datasets/GoChat/Gochat247_Data/Data", detail=False)
+# text_list = fs.ls("datasets/GoChat/Gochat247_Data/Data", detail=False)
 
-data = fs.read_text(text_list[0])
+# data = fs.read_text(text_list[0])
 
-from llama_index import Document
-doc = Document(data)
-docs = []
-docs.append(doc)
+# 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 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)
+# # 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)
+# service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
 
-index = GPTSimpleVectorIndex.from_documents(docs)
+# index = GPTSimpleVectorIndex.from_documents(docs)
 
    
-## Define Chat BOT Class to generate Response , handle chat history, 
-class Chatbot:
+# ## 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 = []
+#     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.") 
+#         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)
+#         self.QA_PROMPT = QuestionAnswerPrompt(QA_PROMPT_TMPL)
         
 
-    def generate_response(self, user_input):
+#     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
+#         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)
+#     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, 
+# ## Define Chat BOT Class to generate Response , handle chat history, 
 
-bot = Chatbot(index=index)
+# bot = Chatbot(index=index)
 
-import webbrowser
+# import webbrowser
 
-import gradio as gr
-import time
+# 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")
+# 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 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
+#         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())
+#         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
+# # handling dark_theme
 
 
 
-# def apply_dark_theme(url):
-#     if not url.endswith('?__theme=dark'):
-#         webbrowser.open_new(url + '?__theme=dark')
+# # 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)
+# # gradioURL = 'http://localhost:7860/'
+# # apply_dark_theme(gradioURL)
 
-if __name__ == "__main__":
-    demo.launch()
+# if __name__ == "__main__":
+#     demo.launch()