import gradio as gr import os import json import requests #Chatbot2 from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration import torch from datasets import load_dataset # PersistDataset ----- import os import csv from gradio import inputs, outputs import huggingface_hub #from huggingface_hub import Repository, hub_download, upload_file from datetime import datetime import fastapi from typing import List, Dict import httpx import pandas as pd import datasets as ds #Chatbot2 constants title = """

💬ChatGPT ChatBack🧠💾

""" #description = """Chatbot With persistent memory dataset allowing multiagent system AI to access a shared dataset as memory pool with stored interactions. """ UseMemory=True #ChatGPT info API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream" OPENAI_API_KEY= os.environ["HF_TOKEN"] # Add a token to this space . Then copy it to the repository secret in this spaces settings panel. os.environ reads from there. # Keys for Open AI ChatGPT API usage are created from here: https://platform.openai.com/account/api-keys description = """ Chatbot With persistent memory dataset allowing multiagent system AI to access a shared dataset as memory pool with stored interactions. ## ChatGPT Datasets 📚 - WebText - Common Crawl - BooksCorpus - English Wikipedia - Toronto Books Corpus - OpenWebText ## ChatGPT Datasets - Details 📚 - **WebText:** A dataset of web pages crawled from domains on the Alexa top 5,000 list. This dataset was used to pretrain GPT-2. - [WebText: A Large-Scale Unsupervised Text Corpus by Radford et al.](https://paperswithcode.com/dataset/webtext) - **Common Crawl:** A dataset of web pages from a variety of domains, which is updated regularly. This dataset was used to pretrain GPT-3. - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/common-crawl) by Brown et al. - **BooksCorpus:** A dataset of over 11,000 books from a variety of genres. - [Scalable Methods for 8 Billion Token Language Modeling](https://paperswithcode.com/dataset/bookcorpus) by Zhu et al. - **English Wikipedia:** A dump of the English-language Wikipedia as of 2018, with articles from 2001-2017. - [Improving Language Understanding by Generative Pre-Training](https://huggingface.co/spaces/awacke1/WikipediaUltimateAISearch?logs=build) Space for Wikipedia Search - **Toronto Books Corpus:** A dataset of over 7,000 books from a variety of genres, collected by the University of Toronto. - [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://paperswithcode.com/dataset/bookcorpus) by Schwenk and Douze. - **OpenWebText:** A dataset of web pages that were filtered to remove content that was likely to be low-quality or spammy. This dataset was used to pretrain GPT-3. - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/openwebtext) by Brown et al. """ #Chatbot2 Save Results def SaveResult(text, outputfileName): basedir = os.path.dirname(__file__) savePath = outputfileName print("Saving: " + text + " to " + savePath) from os.path import exists file_exists = exists(savePath) if file_exists: with open(outputfileName, "a") as f: #append f.write(str(text.replace("\n"," "))) f.write('\n') else: with open(outputfileName, "w") as f: #write f.write(str("time, message, text\n")) # one time only to get column headers for CSV file f.write(str(text.replace("\n"," "))) f.write('\n') return #Chatbot2 Store Message def store_message(name: str, message: str, outputfileName: str): basedir = os.path.dirname(__file__) savePath = outputfileName # if file doesnt exist, create it with labels from os.path import exists file_exists = exists(savePath) if (file_exists==False): with open(savePath, "w") as f: #write f.write(str("time, message, text\n")) # one time only to get column headers for CSV file if name and message: writer = csv.DictWriter(f, fieldnames=["time", "message", "name"]) writer.writerow( {"time": str(datetime.now()), "message": message.strip(), "name": name.strip() } ) df = pd.read_csv(savePath) df = df.sort_values(df.columns[0],ascending=False) else: if name and message: with open(savePath, "a") as csvfile: writer = csv.DictWriter(csvfile, fieldnames=[ "time", "message", "name", ]) writer.writerow( {"time": str(datetime.now()), "message": message.strip(), "name": name.strip() } ) df = pd.read_csv(savePath) df = df.sort_values(df.columns[0],ascending=False) return df #Chatbot2 get base directory of saves def get_base(filename): basedir = os.path.dirname(__file__) print(basedir) loadPath = basedir + filename print(loadPath) return loadPath #Chatbot2 - History def chat(message, history): history = history or [] if history: history_useful = [' '.join([str(a[0])+' '+str(a[1]) for a in history])] else: history_useful = [] history_useful = add_note_to_history(message, history_useful) inputs = tokenizer(history_useful, return_tensors="pt") inputs, history_useful, history = take_last_tokens(inputs, history_useful, history) reply_ids = model.generate(**inputs) response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0] history_useful = add_note_to_history(response, history_useful) list_history = history_useful[0].split(' ') history.append((list_history[-2], list_history[-1])) df=pd.DataFrame() if UseMemory: outputfileName = 'ChatbotMemory3.csv' # Test first time file create df = store_message(message, response, outputfileName) # Save to dataset basedir = get_base(outputfileName) return history, df, basedir #ChatGPT predict def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]): #repetition_penalty, top_k # 1. Set up a payload payload = { "model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": f"{inputs}"}], "temperature" : 1.0, "top_p":1.0, "n" : 1, "stream": True, "presence_penalty":0, "frequency_penalty":0, } # 2. Define your headers and add a key from https://platform.openai.com/account/api-keys headers = { "Content-Type": "application/json", "Authorization": f"Bearer {OPENAI_API_KEY}" } # 3. Create a chat counter loop that feeds [Predict next best anything based on last input and attention with memory defined by introspective attention over time] print(f"chat_counter - {chat_counter}") if chat_counter != 0 : messages=[] for data in chatbot: temp1 = {} temp1["role"] = "user" temp1["content"] = data[0] temp2 = {} temp2["role"] = "assistant" temp2["content"] = data[1] messages.append(temp1) messages.append(temp2) temp3 = {} temp3["role"] = "user" temp3["content"] = inputs messages.append(temp3) #messages payload = { "model": "gpt-3.5-turbo", "messages": messages, #[{"role": "user", "content": f"{inputs}"}], "temperature" : temperature, #1.0, "top_p": top_p, #1.0, "n" : 1, "stream": True, "presence_penalty":0, "frequency_penalty":0, } chat_counter+=1 # 4. POST it to OPENAI API history.append(inputs) print(f"payload is - {payload}") # make a POST request to the API endpoint using the requests.post method, passing in stream=True response = requests.post(API_URL, headers=headers, json=payload, stream=True) #response = requests.post(API_URL, headers=headers, json=payload, stream=True) token_counter = 0 partial_words = "" # 5. Iterate through response lines and structure readable response # TODO - make this parse out markdown so we can have similar interface counter=0 for chunk in response.iter_lines(): if counter == 0: counter+=1 continue if chunk.decode() : chunk = chunk.decode() if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']: partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"] if token_counter == 0: history.append(" " + partial_words) else: history[-1] = partial_words chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list token_counter+=1 df=pd.DataFrame() if UseMemory: outputfileName = 'ChatGPT-RLHF-Memory.csv' # Test first time file create df = store_message(chat, history, outputfileName) # Save to dataset basedir = get_base(outputfileName) #return history, df, basedir yield chat, history, chat_counter # resembles {chatbot: chat, state: history} def take_last_tokens(inputs, note_history, history): if inputs['input_ids'].shape[1] > 128: inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-128:].tolist()]) inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-128:].tolist()]) note_history = [' '.join(note_history[0].split(' ')[2:])] history = history[1:] return inputs, note_history, history def add_note_to_history(note, note_history):# good example of non async since we wait around til we know it went okay. note_history.append(note) note_history = ' '.join(note_history) return [note_history] # ChatGPT clear def reset_textbox(): return gr.update(value='') # 6. Use Gradio to pull it all together with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""") as demo: gr.HTML(title) # Chat bot memory - dataframe gr.Markdown("

🍰Gradio chatbot backed by dataframe CSV memory🎨

") with gr.Row(): t1 = gr.Textbox(lines=1, default="", label="Chat Text:") b1 = gr.Button("🍰 Respond and Retrieve Messages") with gr.Row(): # inputs and buttons s1 = gr.State([]) df1 = gr.Dataframe(wrap=True, max_rows=1000, overflow_row_behaviour= "paginate") with gr.Row(): # inputs and buttons file = gr.File(label="File") s2 = gr.Markdown() #b1.click(fn=chat, inputs=[t1, s1], outputs=[s1, df1, file]) with gr.Column(elem_id = "col_container"): chatbot = gr.Chatbot(elem_id='chatbot') inputs = gr.Textbox(placeholder= "There is only one real true reward in life and this is existence versus nonexistence. Everything else is a corollary.", label= "Type an input and press Enter") #t state = gr.State([]) gpt = gr.Button() with gr.Accordion("Parameters", open=False): top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",) temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",) chat_counter = gr.Number(value=0, visible=False, precision=0) inputs.submit( predict, [inputs, top_p, temperature,chat_counter, chatbot, state], [chatbot, state, chat_counter],) gpt.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter],) gpt.click(reset_textbox, [], [inputs]) inputs.submit(reset_textbox, [], [inputs]) # Show ChatGPT Datasets information gr.Markdown(description) # Kickoff demo.queue().launch(debug=True)