import gradio as gr import os import json import requests #Streaming endpoint 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 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(): #Skipping first chunk if counter == 0: counter+=1 continue #counter+=1 # check whether each line is non-empty if chunk.decode() : chunk = chunk.decode() # decode each line as response data is in bytes if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']: #if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0: # break 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 yield chat, history, chat_counter # resembles {chatbot: chat, state: history} def reset_textbox(): return gr.update(value='') title = """

Memory Chat Story Generator ChatGPT

""" description = """ ## 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. """ # 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) with gr.Column(elem_id = "col_container"): chatbot = gr.Chatbot(elem_id='chatbot') #c inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t state = gr.State([]) #s b1 = 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],) b1.click( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter],) b1.click(reset_textbox, [], [inputs]) inputs.submit(reset_textbox, [], [inputs]) gr.Markdown(description) demo.queue().launch(debug=True)