Upload app.py

app.py

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+
+# import logging
+import os
+os.environ['OPENAI_API_KEY'] = "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"
+import openai
+import json
+
+
+# create a logger with a file handler
+# logger = logging.getLogger("chatbot_logger")
+# handler = logging.FileHandler("chatbot.log")
+# logger.addHandler(handler)
+# logger.setLevel(logging.INFO)
+
+from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext, QuestionAnswerPrompt
+from langchain import OpenAI
+
+
+documents = SimpleDirectoryReader('https://huggingface.co/spaces/waelabou/Gochat247Demo/tree/main/Data_Gochat').load_data()
+
+
+# Setup your LLM
+
+
+# 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(share=True)