Delete app.py

app.py

@@ -1,133 +0,0 @@
-
-# import logging
-import os
-os.environ['OPENAI_API_KEY'] = "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"
-import openai
-import json
-
-
-
-from llama_index import GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext, QuestionAnswerPrompt
-from langchain import OpenAI
-
-
-
-from datasets import load_dataset
-
-dataset = load_dataset("GoChat/Gochat247_Data",use_auth_token="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")
-data = dataset['train']["text"]
-data_text = ' '.join(data)
-from llama_index import Document
-documents = Document(data_text)
-
-# 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()