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| import json | |
| import os | |
| import shutil | |
| import requests | |
| import warnings | |
| import gradio as gr | |
| from huggingface_hub import Repository | |
| from text_generation import Client | |
| from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css | |
| HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
| API_URL_G = "https://api-inference.huggingface.co/models/ArmelR/starcoder-gradio-v0/" | |
| API_URL_S = "https://api-inference.huggingface.co/models/HuggingFaceH4/starcoderbase-finetuned-oasst1" | |
| with open("./HHH_prompt_short.txt", "r") as f: | |
| HHH_PROMPT = f.read() + "\n\n" | |
| with open("./TA_prompt_v0.txt", "r") as f: | |
| TA_PROMPT = f.read() | |
| NO_PROMPT = "" | |
| FIM_PREFIX = "<fim_prefix>" | |
| FIM_MIDDLE = "<fim_middle>" | |
| FIM_SUFFIX = "<fim_suffix>" | |
| FIM_INDICATOR = "<FILL_HERE>" | |
| FORMATS = """ | |
| # Chat mode | |
| Chat mode prepends the custom [TA prompt](https://huggingface.co/spaces/bigcode/chat-playground/blob/main/TA_prompt_v0.txt) or the [HHH prompt](https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11#file-hhh_prompt-txt) from Anthropic to the request which conditions the model to serve as an assistant. | |
| ⚠️ **Intended Use**: this app and its [supporting model](https://huggingface.co/bigcode) are provided for demonstration purposes; not to serve as replacement for human expertise. For more details on the model's limitations in terms of factuality and biases, see the [model card.](hf.co/bigcode) | |
| """ | |
| theme = gr.themes.Monochrome( | |
| primary_hue="indigo", | |
| secondary_hue="blue", | |
| neutral_hue="slate", | |
| radius_size=gr.themes.sizes.radius_sm, | |
| font=[ | |
| gr.themes.GoogleFont("Open Sans"), | |
| "ui-sans-serif", | |
| "system-ui", | |
| "sans-serif", | |
| ], | |
| ) | |
| client_g = Client( | |
| API_URL_G, headers={"Authorization": f"Bearer {HF_TOKEN}"}, | |
| ) | |
| client_starchat = Client( | |
| API_URL_S, headers={"Authorization": f"Bearer {HF_TOKEN}"}, | |
| ) | |
| def generate( | |
| prompt, | |
| temperature=0.9, | |
| max_new_tokens=256, | |
| top_p=0.95, | |
| repetition_penalty=1.0, | |
| chat_mode="TA prompt", | |
| version="StarChat-alpha", | |
| ): | |
| temperature = float(temperature) | |
| if temperature < 1e-2: | |
| temperature = 1e-2 | |
| top_p = float(top_p) | |
| fim_mode = False | |
| generate_kwargs = dict( | |
| temperature=temperature, | |
| max_new_tokens=max_new_tokens, | |
| top_p=top_p, | |
| repetition_penalty=repetition_penalty, | |
| truncate=7500, | |
| do_sample=True, | |
| seed=42, | |
| stop_sequences=["\nHuman", "\n-----", "Question:", "Answer:"], | |
| ) | |
| if chat_mode == "HHH prompt": | |
| base_prompt = HHH_PROMPT | |
| elif chat_mode == "TA prompt": | |
| base_prompt = TA_PROMPT | |
| else : | |
| base_prompt = NO_PROMPT | |
| chat_prompt = prompt + "\n\nAnswer:" | |
| prompt = base_prompt + chat_prompt | |
| if version == "StarCoder-gradio" : | |
| stream = client_g.generate_stream(prompt, **generate_kwargs) | |
| elif version == "StarChat-alpha" : | |
| stream = client_s.generate_stream(prompt, **generate_kwargs) | |
| else : | |
| pass | |
| output = "" | |
| previous_token = "" | |
| for response in stream: | |
| if ( | |
| (response.token.text in ["Human", "-----", "Question:"] | |
| and previous_token in ["\n", "-----"]) | |
| or response.token.text == "<|endoftext|>" | |
| ): | |
| return output.strip() | |
| else: | |
| output += response.token.text | |
| previous_token = response.token.text | |
| return output.strip() | |
| # chatbot mode | |
| def user(user_message, history): | |
| return "", history + [[user_message, None]] | |
| def bot( | |
| history, | |
| temperature=0.9, | |
| max_new_tokens=256, | |
| top_p=0.95, | |
| repetition_penalty=1.0, | |
| chat_mode=None, | |
| version="starchat-alpha", | |
| ): | |
| # concat history of prompts with answers expect for last empty answer only add prompt | |
| if version == "StarCoder-gradio" : | |
| prompt = "\n".join( | |
| [f"Question: {prompt}\n\nAnswer: {answer}" for prompt, answer in history[:-1]] + [f"\nQuestion: {history[-1][0]}"] | |
| ) | |
| else : | |
| prompt = "\n".join( | |
| [f"Human: {prompt}\n\nAssistant: {answer}" for prompt, answer in history[:-1]] + [f"\nHuman: {history[-1][0]}"] | |
| ) | |
| bot_message = generate( | |
| prompt, | |
| temperature=temperature, | |
| max_new_tokens=max_new_tokens, | |
| top_p=top_p, | |
| repetition_penalty=repetition_penalty, | |
| chat_mode=chat_mode, | |
| ) | |
| history[-1][1] = bot_message | |
| return history | |
| examples = [ | |
| "def print_hello_world():", | |
| "def fibonacci(n):", | |
| "class TransformerDecoder(nn.Module):", | |
| "class ComplexNumbers:", | |
| "How to install gradio" | |
| ] | |
| def process_example(args): | |
| for x in generate(args): | |
| pass | |
| return x | |
| css = ".generating {visibility: hidden}" + share_btn_css | |
| with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo: | |
| with gr.Column(): | |
| gr.Markdown( | |
| """\ | |
| # Tech Assistant powered by 💫 StarCoder | |
| _Note:_ this is an internal chat playground - **please do not share**. The deployment can also change and thus the space not work as we continue development.\ | |
| """ | |
| ) | |
| with gr.Row(): | |
| column_1, column_2 = gr.Column(scale=3), gr.Column(scale=1) | |
| with column_2: | |
| chat_mode = gr.Dropdown( | |
| ["NO prompt","TA prompt", "HHH prompt"], | |
| value="NO prompt", | |
| label="Chat mode", | |
| info="Use Anthropic's HHH prompt or our custom tech prompt to turn the model into an assistant.", | |
| ) | |
| temperature = gr.Slider( | |
| label="Temperature", | |
| value=0.2, | |
| minimum=0.0, | |
| maximum=2.0, | |
| step=0.1, | |
| interactive=True, | |
| info="Higher values produce more diverse outputs", | |
| ) | |
| max_new_tokens = gr.Slider( | |
| label="Max new tokens", | |
| value=256, | |
| minimum=0, | |
| maximum=8192, | |
| step=64, | |
| interactive=True, | |
| info="The maximum numbers of new tokens", | |
| ) | |
| top_p = gr.Slider( | |
| label="Top-p (nucleus sampling)", | |
| value=0.90, | |
| minimum=0.0, | |
| maximum=1, | |
| step=0.05, | |
| interactive=True, | |
| info="Higher values sample more low-probability tokens", | |
| ) | |
| repetition_penalty = gr.Slider( | |
| label="Repetition penalty", | |
| value=1.2, | |
| minimum=1.0, | |
| maximum=2.0, | |
| step=0.05, | |
| interactive=True, | |
| info="Penalize repeated tokens", | |
| ) | |
| version = gr.Dropdown( | |
| ["StarCoder-gradio", "StarChat-alpha"], | |
| value="StarChat-alpha", | |
| label="Version", | |
| info="", | |
| ) | |
| with column_1: | |
| # output = gr.Code(elem_id="q-output") | |
| # add visibl=False and update if chat_mode True | |
| chatbot = gr.Chatbot() | |
| instruction = gr.Textbox( | |
| placeholder="Enter your prompt here", | |
| label="Prompt", | |
| elem_id="q-input", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| clear = gr.Button("Clear Chat") | |
| with gr.Column(): | |
| submit = gr.Button("Generate", variant="primary") | |
| with gr.Group(elem_id="share-btn-container"): | |
| community_icon = gr.HTML(community_icon_html, visible=True) | |
| loading_icon = gr.HTML(loading_icon_html, visible=True) | |
| share_button = gr.Button( | |
| "Share to community", elem_id="share-btn", visible=True | |
| ) | |
| # examples of non-chat mode | |
| #gr.Examples( | |
| # examples=examples, | |
| # inputs=[instruction], | |
| # cache_examples=False, | |
| # fn=process_example, | |
| # outputs=[output], | |
| # ) | |
| gr.Markdown(FORMATS) | |
| instruction.submit( | |
| user, [instruction, chatbot], [instruction, chatbot], queue=False | |
| ).then( | |
| bot, | |
| [chatbot, temperature, max_new_tokens, top_p, repetition_penalty, chat_mode, version], | |
| chatbot, | |
| ) | |
| submit.click( | |
| user, [instruction, chatbot], [instruction, chatbot], queue=False | |
| ).then( | |
| bot, | |
| [chatbot, temperature, max_new_tokens, top_p, repetition_penalty, chat_mode, version], | |
| chatbot, | |
| ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| share_button.click(None, [], [], _js=share_js) | |
| demo.queue(concurrency_count=16).launch(debug=True) | |