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| import json | |
| import os | |
| import shutil | |
| import requests | |
| 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 = os.environ.get("API_URL") | |
| FIM_PREFIX = "<fim_prefix>" | |
| FIM_MIDDLE = "<fim_middle>" | |
| FIM_SUFFIX = "<fim_suffix>" | |
| FIM_INDICATOR = "<FILL_HERE>" | |
| FORMATS = """## Model Formats | |
| The model is pretrained on code and is formatted with special tokens in addition to the pure code data,\ | |
| such as prefixes specifying the source of the file or tokens separating code from a commit message.\ | |
| Use these templates to explore the model's capacities: | |
| ### 1. Prefixes 🏷️ | |
| For pure code files, use any combination of the following prefixes: | |
| ``` | |
| <reponame>REPONAME<filename>FILENAME<gh_stars>STARS\ncode<|endoftext|> | |
| ``` | |
| STARS can be one of: 0, 1-10, 10-100, 100-1000, 1000+ | |
| ### 2. Commits 💾 | |
| The commits data is formatted as follows: | |
| ``` | |
| <commit_before>code<commit_msg>text<commit_after>code<|endoftext|> | |
| ``` | |
| ### 3. Jupyter Notebooks 📓 | |
| The model is trained on Jupyter notebooks as Python scripts and structured formats like: | |
| ``` | |
| <start_jupyter><jupyter_text>text<jupyter_code>code<jupyter_output>output<jupyter_text> | |
| ``` | |
| ### 4. Issues 🐛 | |
| We also trained on GitHub issues using the following formatting: | |
| ``` | |
| <issue_start><issue_comment>text<issue_comment>...<issue_closed> | |
| ``` | |
| ### 5. Fill-in-the-middle 🧩 | |
| Fill in the middle requires rearranging the model inputs. The playground handles this for you - all you need is to specify where to fill: | |
| ``` | |
| code before<FILL_HERE>code after | |
| ``` | |
| """ | |
| 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 = Client( | |
| API_URL, | |
| headers={"Authorization": f"Bearer {HF_TOKEN}"}, | |
| ) | |
| def generate( | |
| prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0 | |
| ): | |
| 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, | |
| do_sample=True, | |
| seed=42, | |
| ) | |
| if FIM_INDICATOR in prompt: | |
| fim_mode = True | |
| try: | |
| prefix, suffix = prompt.split(FIM_INDICATOR) | |
| except: | |
| raise ValueError(f"Only one {FIM_INDICATOR} allowed in prompt!") | |
| prompt = f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}" | |
| stream = client.generate_stream(prompt, **generate_kwargs) | |
| if fim_mode: | |
| output = prefix | |
| else: | |
| output = prompt | |
| previous_token = "" | |
| for response in stream: | |
| if response.token.text == "<|endoftext|>": | |
| if fim_mode: | |
| output += suffix | |
| else: | |
| return output | |
| else: | |
| output += response.token.text | |
| previous_token = response.token.text | |
| yield output | |
| return output | |
| examples = [ | |
| "def print_hello_world():", | |
| 'def fibonacci(n: int) -> int:\n """ Compute the n-th Fibonacci number. """', | |
| 'from typing import List, Tuple\n\ndef sum_and_product(numbers: List[int]) -> Tuple[int, int]:\n """ Return the sum and the product of the integers in the list as a tuple. Here is the answer of the exercise"""', | |
| "class ComplexNumbers:", | |
| ] | |
| def process_example(args): | |
| for x in generate(args): | |
| pass | |
| return x | |
| css = ".generating {visibility: hidden}" | |
| monospace_css = """ | |
| #q-input textarea { | |
| font-family: monospace, 'Consolas', Courier, monospace; | |
| } | |
| """ | |
| custom_output_css = """ | |
| #q-output textarea { | |
| min-height: 800px; | |
| } | |
| """ | |
| css += share_btn_css + monospace_css + custom_output_css + ".gradio-container {color: black}" | |
| description = """ | |
| <div style="text-align: center;"> | |
| <h1 style='color: black;'> 💫 StarCoder<span style='color: #e6b800;'> - </span>Playground 🪐</h1> | |
| <p style='color: black;'>This is a demo to generate code with <a href="https://huggingface.co/bigcode/starcoder" style='color: #e6b800;'>StarCoder</a>, a 15B parameter model for code generation in 86 programming languages.</p> | |
| </div> | |
| """ | |
| disclaimer = """⚠️ **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)""" | |
| with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo: | |
| with gr.Column(): | |
| gr.Markdown(description) | |
| with gr.Row(): | |
| with gr.Column(): | |
| instruction = gr.Textbox( | |
| placeholder="Enter your prompt here", | |
| label="Prompt", | |
| elem_id="q-input", | |
| ) | |
| submit = gr.Button("Generate", variant="primary") | |
| output = gr.Code(elem_id="q-output", lines=30) | |
| with gr.Accordion("Advanced settings", open=False): | |
| with gr.Row(): | |
| column_1, column_2 = gr.Column(), gr.Column() | |
| with column_1: | |
| temperature = gr.Slider( | |
| label="Temperature", | |
| value=0.2, | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.05, | |
| 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", | |
| ) | |
| with column_2: | |
| 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", | |
| ) | |
| gr.Markdown(disclaimer) | |
| 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 | |
| ) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[instruction], | |
| cache_examples=False, | |
| fn=process_example, | |
| outputs=[output], | |
| ) | |
| gr.Markdown(FORMATS) | |
| submit.click( | |
| generate, | |
| inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty], | |
| outputs=[output], | |
| ) | |
| share_button.click(None, [], [], _js=share_js) | |
| demo.queue(concurrency_count=16).launch(debug=True) |