app.py

import gradio as gr

_remove_color = "rgb(103,6,12)"
_addition_color = "rgb(6,103,12)"

def mark_text(text, add=True):
    if add:
        color = _addition_color
    else:
        color = _remove_color
    return f'<mark style="background-color:{color}!important;color:white!important">{text}</mark>'

def highlight(option):
    filename = option.lower().replace(' ', '_')
    with open(f"code_samples/{filename}") as f:
        output = f.read()
    lines = output.split("\n")
    for i,line in enumerate(lines):
        if line.startswith("-"):
            lines[i] = "- " + line[1:]
            lines[i] = mark_text(lines[i], False)
        elif line.startswith("+"):
            lines[i] = "+ " + line[1:]
            lines[i] = mark_text(lines[i], True)
        else:
            lines[i] = "  " + line
    return "\n".join(lines).rstrip()

with open("code_samples/initial") as f:
    template = f.read()

with open("code_samples/accelerate") as f:
    accelerated_template = f.read()

with open("code_samples/initial_with_metrics") as f:
    metrics_template = f.read()

def change(inp):
    if inp == "Basic":
        return (template, highlight(inp), "## Accelerate Code (Base Integration)")
    elif inp == "Calculating Metrics":
        return (metrics_template, highlight(inp), f"## Accelerate Code ({inp})")
    else:
        return (accelerated_template, highlight(inp), f"## Accelerate Code ({inp})")

with gr.Blocks() as demo:
    gr.Markdown(f'''# Accelerate Template Generator
Here is a very basic Python training loop.
Select how you would like to introduce an Accelerate capability to add to it.''')
    inp = gr.Radio(
        ["Basic", "Calculating Metrics", "Checkpointing", "Gradient Accumulation", ], 
        label="Select a feature"
    )
    with gr.Row():
        with gr.Column():
            gr.Markdown("## Initial Code")
            code = gr.Markdown(template)
        with gr.Column():
            feature = gr.Markdown("## Accelerate Code")
            out = gr.Markdown()
    inp.change(fn=change, inputs=inp, outputs=[code, out, feature])  
demo.launch()