Update app.py

app.py

@@ -1,147 +1,141 @@
 
 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'])
+open_ai_key= os.environ['OPENAI_API_KEY']
+hf_key = os.environ['HF_KEY']
 
 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 = open_ai_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()