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| 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 = """<h1 align="center">Memory Chat Story Generator ChatGPT</h1>""" | |
| 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) | |
| gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPTwithAPI?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''') | |
| 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) | |