app.py

import gradio as gr
import torch
from gradio.themes.utils import sizes
from transformers import AutoModelForCausalLM, AutoTokenizer

import utils
from constants import END_OF_TEXT
from settings import DEFAULT_PORT

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(
    "BEE-spoke-data/smol_llama-101M-GQA-python",
    use_fast=False,
)
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer.pad_token = END_OF_TEXT
model = AutoModelForCausalLM.from_pretrained(
    "BEE-spoke-data/smol_llama-101M-GQA-python",
    device_map="auto",
)
model = torch.compile(model, mode="reduce-overhead")

# UI things

_styles = utils.get_file_as_string("styles.css")

# Loads ./README.md file & splits it into sections
readme_file_content = utils.get_file_as_string("README.md", path="./")
(
    manifest,
    description,
    disclaimer,
    base_model_info,
    formats,
) = utils.get_sections(readme_file_content, "---", up_to=5)

theme = gr.themes.Soft(
    primary_hue="yellow",
    secondary_hue="orange",
    neutral_hue="slate",
    radius_size=sizes.radius_sm,
    font=[
        gr.themes.GoogleFont("IBM Plex Sans", [400, 600]),
        "ui-sans-serif",
        "system-ui",
        "sans-serif",
    ],
    text_size=sizes.text_lg,
)


def run_inference(prompt, temperature, max_new_tokens, top_p, repetition_penalty):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=max_new_tokens,
        min_new_tokens=8,
        renormalize_logits=True,
        no_repeat_ngram_size=6,
        repetition_penalty=repetition_penalty,
        num_beams=3,
        early_stopping=True,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
    )
    text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
    return text


# Gradio interface wrapper for inference
def gradio_interface(
    prompt: str,
    temperature: float,
    max_new_tokens: int,
    top_p: float,
    repetition_penalty: float,
):
    return run_inference(prompt, temperature, max_new_tokens, top_p, repetition_penalty)


import random

examples = [
    ["def add_numbers(a, b):\n    return", 0.2, 192, 0.9, 1.2],
    [
        "class Car:\n    def __init__(self, make, model):\n        self.make = make\n        self.model = model\n\n    def display_car(self):",
        0.2,
        192,
        0.9,
        1.2,
    ],
    [
        "import pandas as pd\ndata = {'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19]}\ndf = pd.DataFrame(data).convert_dtypes()\n# eda",
        0.2,
        192,
        0.9,
        1.2,
    ],
    [
        "def factorial(n):\n    if n == 0:\n        return 1\n    else:",
        0.2,
        192,
        0.9,
        1.2,
    ],
    [
        'def fibonacci(n):\n    if n <= 0:\n        raise ValueError("Incorrect input")\n    elif n == 1:\n        return 0\n    elif n == 2:\n        return 1\n    else:',
        0.2,
        192,
        0.9,
        1.2,
    ],
    [
        "import matplotlib.pyplot as plt\nimport numpy as np\nx = np.linspace(0, 10, 100)\n# simple plot",
        0.2,
        192,
        0.9,
        1.2,
    ],
    ["def reverse_string(s:str) -> str:\n    return", 0.2, 192, 0.9, 1.2],
    ["def is_palindrome(word:str) -> bool:\n    return", 0.2, 192, 0.9, 1.2],
    [
        "def bubble_sort(lst: list):\n    n = len(lst)\n    for i in range(n):\n        for j in range(0, n-i-1):",
        0.2,
        192,
        0.9,
        1.2,
    ],
    [
        "def binary_search(arr, low, high, x):\n    if high >= low:\n        mid = (high + low) // 2\n        if arr[mid] == x:\n            return mid\n        elif arr[mid] > x:",
        0.2,
        192,
        0.9,
        1.2,
    ],
]

# Define the Gradio Blocks interface
with gr.Blocks(theme=theme, analytics_enabled=False, css=_styles) as demo:
    with gr.Column():
        gr.Markdown(description)
        with gr.Row():
            with gr.Column():
                instruction = gr.Textbox(
                    value=random.choice([e[0] for e in examples]),
                    placeholder="Enter your code here",
                    label="Code",
                    elem_id="q-input",
                )
                submit = gr.Button("Generate", variant="primary")
                output = gr.Code(elem_id="q-output", language="python", lines=10)
                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=128,
                                        minimum=0,
                                        maximum=512,
                                        step=64,
                                        interactive=True,
                                        info="Number of tokens to generate",
                                    )
                                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=1.1,
                                        minimum=1.0,
                                        maximum=2.0,
                                        step=0.05,
                                        interactive=True,
                                        info="Penalize repeated tokens",
                                    )
                    with gr.Column():
                        version = gr.Dropdown(
                            [
                                "smol_llama-101M-GQA-python",
                            ],
                            value="smol_llama-101M-GQA-python",
                            label="Version",
                            info="",
                        )
                gr.Markdown(disclaimer)
                gr.Examples(
                    examples=examples,
                    inputs=[
                        instruction,
                        temperature,
                        max_new_tokens,
                        top_p,
                        repetition_penalty,
                        version,
                    ],
                    cache_examples=False,
                    fn=gradio_interface,
                    outputs=[output],
                )
                gr.Markdown(base_model_info)
                gr.Markdown(formats)

    submit.click(
        gradio_interface,
        inputs=[
            instruction,
            temperature,
            max_new_tokens,
            top_p,
            repetition_penalty,
        ],
        outputs=[output],
        # preprocess=False,
        batch=False,
        show_progress=True,
    )

demo.queue(max_size=10).launch(
    debug=True,
    server_port=DEFAULT_PORT,
)