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| # 4/29/2024 | |
| # This version added saving chat history to a log file (need persist data from a space to a dataset) | |
| # Updated the GPT model to gpt-4 | |
| # Add timestamp and ip address | |
| # 2/23/2024 | |
| # This version uses different method in llama index to define llm model | |
| # Removed deprecated classes and replaced with newest dependencies | |
| # Start by setting token and debug mode before starting schedulers | |
| import os | |
| from huggingface_hub import logging, login | |
| login(token=os.environ.get("HF_TOKEN"), write_permission=True) | |
| #logging.set_verbosity_debug() | |
| import openai | |
| import json | |
| import gradio as gr | |
| from openai import OpenAI | |
| # rebuild storage context and load knowledge index | |
| # from llama_index import StorageContext, load_index_from_storage, LLMPredictor, ServiceContext | |
| from llama_index.core import StorageContext | |
| from llama_index.core import load_index_from_storage | |
| from llama_index.llms.openai import OpenAI | |
| #from llama_index.core import Settings | |
| # add datetime and ip to the log file | |
| from datetime import datetime; | |
| import socket; | |
| # access data folder of persistent storage | |
| from pathlib import Path | |
| from huggingface_hub import CommitScheduler | |
| from uuid import uuid4 | |
| # generate an unique identifier for the session | |
| session_id = uuid4() | |
| # deprecated | |
| storage_context = StorageContext.from_defaults(persist_dir='./') | |
| # gpt-3.5_turbo is the current default model | |
| llm_predictor = OpenAI(temperature=0.5, model="gpt-4") | |
| #service_context = ServiceContext.from_defaults(llm=llm_predictor) | |
| #index = load_index_from_storage(storage_context, service_context=service_context) | |
| index = load_index_from_storage(storage_context) | |
| class Chatbot: | |
| def __init__(self, api_key, index): | |
| self.index = index | |
| openai.api_key = api_key | |
| self.chat_history = [] | |
| # set chat history data path in data folder (persistent storage) | |
| dataset_dir = Path("logs") | |
| dataset_dir.mkdir(parents=True, exist_ok=True) | |
| #self.dataset_path = dataset_dir / f"chat_log_{uuid4()}.json" | |
| self.dataset_path = dataset_dir / f"chat_log_{session_id}.json" | |
| self.scheduler = CommitScheduler( | |
| repo_id="history_data", | |
| repo_type="dataset", | |
| folder_path=dataset_dir, | |
| path_in_repo="data", | |
| ) | |
| def generate_response(self, user_input): | |
| query_engine = index.as_query_engine(llm=llm_predictor) | |
| response = query_engine.query(user_input) | |
| # generate response | |
| message = {"role": "assistant", "content": response.response} | |
| return message | |
| # do not need this function if use append mode when dump data in file | |
| #def load_chat_history(self): | |
| # try: | |
| # with open(self.dataset_path, 'r') as f: | |
| # self.chat_history = json.load(f) | |
| # except FileNotFoundError: | |
| # pass | |
| def append_chat_history(self, user_input, output): | |
| # create a dictionary for the chat history | |
| #self.chat_history = [] | |
| dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S") | |
| #print(dt) | |
| hostname = socket.gethostname() | |
| ip = socket.gethostbyname(hostname) | |
| #print(ip) | |
| #self.chat_history.append({"role": "datetime", "content": dt}) | |
| #self.chat_history.append({"role": "IP", "content": ip}) | |
| #self.chat_history.append({"role": "user", "content": user_input}) | |
| #self.chat_history.append({"role": "assistant", "content": output}) | |
| # save the data in dictionary format | |
| dictionary = { | |
| "datetime": dt, | |
| "ip": ip, | |
| "user": user_input, | |
| "assistant": output | |
| } | |
| self.chat_history.append(dictionary) | |
| def save_chat_history(self): | |
| with self.scheduler.lock: | |
| with self.dataset_path.open("a") as f: | |
| json.dump(self.chat_history, f) | |
| f.write("\n") | |
| def create_bot(user_input): | |
| bot = Chatbot(os.getenv("OPENAI_API_KEY"), index=index) | |
| #bot.load_chat_history(); | |
| if user_input: | |
| # use moderations endpoint to check input | |
| client = openai.OpenAI() | |
| response_mod = client.moderations.create(input=user_input) | |
| response_dict = response_mod.model_dump() | |
| flagged = response_dict['results'][0]['flagged'] | |
| #print("Flagged:", flagged) | |
| if not flagged: | |
| response_bot = bot.generate_response(user_input) | |
| output = response_bot['content'] | |
| else: | |
| output = "Invalid request." | |
| bot.append_chat_history(user_input, output) | |
| bot.save_chat_history() | |
| return output | |
| inputs = gr.components.Textbox(lines=7, label="Ask questions related to the course. For example, when is the due date for Excel Module 9, what is the assignment late policy, how to use NPV function in Excel, etc.") | |
| outputs = gr.components.Textbox(label="Response") | |
| gr.Interface(fn=create_bot, inputs=inputs, outputs=outputs, title="Virtual TA", | |
| description="This is a prototype of learning assistant designed for MIS 320 online section (Version 2.0). Powered by ChatGPT-4.\nNote: ChatGPT can make mistakes. Consider checking important information.", | |
| theme="compact").launch(share=True) | |