app.py

import json
import os
import shutil
import requests
import spaces

import gradio as gr
from huggingface_hub import Repository
from text_generation import Client
from transformers import AutoModelForCausalLM, AutoTokenizer

from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css

checkpoint = "smallcloudai/Refact-1_6B-fim"
device = "cuda"
#device = "cpu" # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, trust_remote_code=True).to(device)

FIM_PREFIX = "<fim_prefix>"
FIM_MIDDLE = "<fim_middle>"
FIM_SUFFIX = "<fim_suffix>"

FIM_INDICATOR = "<FILL_HERE>"

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",
    ],
)

@spaces.GPU
def generate(
    prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, version="StarCoder",
):

    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}"

    inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
    outputs = model.generate(inputs, max_length=100, temperature=0.2)
    final = tokenizer.decode(outputs[0])

    return final


examples = [
    "X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1)\n\n# Train a logistic regression model, predict the labels on the test set and compute the accuracy score",
    "// Returns every other value in the array as a new array.\nfunction everyOther(arr) {",
    "Poor English: She no went to the market. Corrected English:",
    "def alternating(list1, list2):\n   results = []\n   for i in range(min(len(list1), len(list2))):\n       results.append(list1[i])\n       results.append(list2[i])\n   if len(list1) > len(list2):\n       <FILL_HERE>\n   else:\n       results.extend(list2[i+1:])\n   return results",
]


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;
}
"""


css += share_btn_css + monospace_css + ".gradio-container {color: black}"


description = """
<div style="text-align: center;">
    <h1> Refact 1.6B <span style='color: #e6b800;'>Models</span> Playground</h1>
</div>
<div style="text-align: left;">
    <p>This is a demo to generate text and code with the following model:</p>
    <ul>
        <li><a href="https://huggingface.co/smallcloudai/Refact-1_6B-fim" style='color: #e6b800;'>ReFact 1.6B</a>: An Open-Source Coding Assistant with Fine-Tuning on codebase, autocompletion, code refactoring, code analysis, integrated chat and more</li>
    </ul>
    <p><b>Please note:</b> This space is based on the Big Code Playground, and not all functionality may work. It is running on GPUZero, but can also be run on GPU/CPU.</p>
</div>
"""

with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
    with gr.Column():
        gr.Markdown(description)
        with gr.Row():
            version = gr.Dropdown(
                        ["Refact"],
                        value="Refact",
                        label="Model",
                        info="Choose a model from the list",
                        )
        with gr.Row():
            with gr.Column():
                instruction = gr.Textbox(
                    placeholder="Enter your code here",
                    lines=5,
                    label="Input",
                    elem_id="q-input",
                )
                submit = gr.Button("Generate", variant="primary")
                output = gr.Code(elem_id="q-output", lines=30, label="Output")
                with gr.Row():
                    with gr.Column():
                        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.Examples(
                    examples=examples,
                    inputs=[instruction],
                    cache_examples=False,
                    fn=process_example,
                    outputs=[output],
                )

    submit.click(
        generate,
        inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty, version],
        outputs=[output],
    )
demo.launch(debug=True)