app.py

import json
import random

import gradio as gr
from difflib import SequenceMatcher

with open("qwen_gsm8k_output.jsonl", "r") as file:
    qwen_dict = [json.loads(line) for line in file]

with open("phi4_gsm8k_output.jsonl", "r") as file:
    phi4_dict = [json.loads(line) for line in file]

models_data = {
    "microsoft/phi-4" : phi4_dict,
    "Qwen/Qwen2.5-14B" : qwen_dict,
}

models_no = {
    "microsoft/phi-4" : 172,
    "Qwen/Qwen2.5-14B" : 729,
}

starting_index = 0
starting_model = [model_name for model_name in models_data.keys()][0]


description_template = """
This Space is inspired by [Luis Hunt's](https://www.linkedin.com/posts/louiswhunt_see-below-for-6882-pages-of-mmlu-and-gsm8k-activity-7281011488692047872-fWCE?utm_source=share&utm_medium=member_desktop) post.
He highlights how current top performing models from major vendors are contaminated with benchmark data that is supposed to be used to assess their performance.
This space aims to partially reproduce this work.

I chose to look at the contamination of **Qwen/Qwen2.5-14B** and **microsoft/phi-4** by **GSM8K** dataset.

For **{model_name}**, I found **{number}** GSM8K examples that had a least a 0.9 text similarity ratio between generated and original.
"""


def find_similar_chunks(original, output):
    matcher = SequenceMatcher(None, original, output)
    left = 0
    highlighted_sequence = []
    for _, j, n in matcher.get_matching_blocks():
        if left < j:
            highlighted_sequence.append((output[left:j], None))
        highlighted_sequence.append((output[j:j+n], 1))
        left = j + n
    if j+n < len(output) - 1:
        highlighted_sequence.append((output[j+n:], None))
    highlighted_sequence = highlighted_sequence[:-1]
    return highlighted_sequence

def next_example(selected_model):
    new_example = random.choice(models_data[selected_model])

    highlighted_output = find_similar_chunks(new_example["original"], new_example["output"])
    return(
        [
            new_example["prompt"],
            new_example["original"], 
            highlighted_output, 
            new_example["similarity_ratio"],
            new_example["seed"]
        ]
    )

def change_model(selected_model):
    example = models_data[selected_model][starting_index]

    highlighted_output = find_similar_chunks(example["original"], example["output"])
    return(
        [
            example["prompt"],
            example["original"], 
            highlighted_output, 
            example["similarity_ratio"],
            example["seed"],
            description_template.format(model_name=selected_model, number=models_no[selected_model])
        ]
    )

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=1):
            description_text = gr.Markdown(description_template.format(model_name=starting_model, number=models_no[starting_model]))
        with gr.Column(scale=1):
            pass
    with gr.Row():
        with gr.Column(scale=1):
            selected_model = gr.Dropdown(
                [model_name for model_name in models_data.keys()],
                value=[model_name for model_name in models_data.keys()][0],
                interactive=True,
                label="Model"
            )
        with gr.Column(scale=4):
            prompt = gr.Textbox(
                label="Prompt",
                interactive=False,
                value=models_data[starting_model][starting_index]["prompt"],
            )
    with gr.Row():
        with gr.Column(scale=4):
            original = gr.Textbox(
                label="Original",
                interactive=False,
                value=models_data[starting_model][starting_index]["original"],
            )
        with gr.Column(scale=4):
            output = gr.HighlightedText(
                label="Output",
                color_map={"1": "yellow"},
                value=find_similar_chunks(models_data[starting_model][starting_index]["original"], 
                                        models_data[starting_model][starting_index]["output"]),
            )

        with gr.Row():
            with gr.Column(scale=1):
                similarity = gr.Textbox(
                    label="Similarity ratio",
                    interactive=False,
                    value=models_data[starting_model][starting_index]["similarity_ratio"],
                )
            with gr.Column(scale=1):
                seed = gr.Textbox(
                    label="Seed",
                    interactive=False,
                    value=models_data[starting_model][starting_index]["seed"],
                )

    next_btn = gr.Button("Another")
    
    next_btn.click(fn=next_example, 
                   inputs=[selected_model],
                   outputs=[prompt, original, output, similarity, seed])

    selected_model.change(fn=change_model,
                          inputs=[selected_model],
                          outputs=[prompt, original, output, similarity, seed, description_text])

demo.launch()