app.py

import gradio as gr
from datasets import load_dataset
import numpy as np
from model2vec import StaticModel
from reach import Reach
from difflib import ndiff

# Load the model
model = StaticModel.from_pretrained("minishlab/M2V_base_output")

# Default parameters
default_dataset_name = "sst2"
default_dataset_split = "train"
default_text_column = "sentence"
default_threshold = 0.9

def deduplicate_embeddings(
    embeddings_a: np.ndarray,
    embeddings_b: np.ndarray = None,
    threshold: float = 0.9,
    batch_size: int = 1024,
    progress=None
):
    """Deduplicate within one dataset or across two datasets."""
    if embeddings_b is None:
        reach = Reach(vectors=embeddings_a, items=[str(i) for i in range(len(embeddings_a))])
        duplicate_to_original = {}
        results = reach.nearest_neighbor_threshold(
            embeddings_a, threshold=threshold, batch_size=batch_size, show_progressbar=False
        )
        for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates", total=len(embeddings_a))):
            for sim_idx, _ in similar_items:
                sim_idx = int(sim_idx)
                if sim_idx != i and sim_idx not in duplicate_to_original:
                    duplicate_to_original[sim_idx] = i
        deduplicated_indices = set(range(len(embeddings_a))) - set(duplicate_to_original.keys())
        return deduplicated_indices, duplicate_to_original
    else:
        reach = Reach(vectors=embeddings_a, items=[str(i) for i in range(len(embeddings_a))])
        duplicate_indices_in_b = []
        duplicate_to_original = {}
        results = reach.nearest_neighbor_threshold(
            embeddings_b, threshold=threshold, batch_size=batch_size, show_progressbar=False
        )
        for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates", total=len(embeddings_b))):
            if similar_items:
                duplicate_indices_in_b.append(i)
                duplicate_to_original[i] = int(similar_items[0][0])
        return duplicate_indices_in_b, duplicate_to_original

def display_word_differences(x: str, y: str) -> str:
    """Display differences between two texts."""
    diff = ndiff(x.split(), y.split())
    return " ".join(word for word in diff if word.startswith(("+", "-")))

def load_dataset_texts(dataset_name, dataset_split, text_column):
    """Load texts from a specified dataset."""
    ds = load_dataset(dataset_name, split=dataset_split)
    return [example[text_column] for example in ds]

def perform_deduplication(
    deduplication_type,
    dataset1_name,
    dataset1_split,
    dataset1_text_column,
    dataset2_name="",
    dataset2_split="",
    dataset2_text_column="",
    threshold=default_threshold,
    progress=gr.Progress(track_tqdm=True),
):
    try:
        threshold = float(threshold)

        # Load and process Dataset 1
        yield "Loading Dataset 1...", ""
        texts1 = load_dataset_texts(dataset1_name, dataset1_split, dataset1_text_column)
        yield "Computing embeddings for Dataset 1...", ""
        #embeddings1 = compute_embeddings(texts1, batch_size=64, progress=progress, desc="Dataset 1 embeddings")
        embeddings1 = model.encode(texts1, show_progressbar=True)
        if deduplication_type == "Single dataset":
            # Deduplicate within Dataset 1
            yield "Deduplicating within Dataset 1...", ""
            deduplicated_indices, duplicate_mapping = deduplicate_embeddings(
                embeddings1, threshold=threshold, progress=progress
            )

            num_duplicates = len(duplicate_mapping)
            result_text = (
                f"**Total documents:** {len(texts1)}\n\n"
                f"**Duplicates found:** {num_duplicates}\n\n"
                f"**Unique documents after deduplication:** {len(deduplicated_indices)}\n\n"
            )

            if num_duplicates > 0:
                result_text += "**Sample duplicates:**\n\n"
                for dup_idx, orig_idx in list(duplicate_mapping.items())[:5]:
                    orig_text = texts1[orig_idx]
                    dup_text = texts1[dup_idx]
                    differences = display_word_differences(orig_text, dup_text)
                    result_text += (
                        f"**Original:**\n{orig_text}\n\n"
                        f"**Duplicate:**\n{dup_text}\n\n"
                        f"**Differences:**\n{differences}\n"
                        + "-" * 50 + "\n\n"
                    )
            else:
                result_text += "No duplicates found."

            yield "Deduplication completed.", result_text

        else:
            # Load and process Dataset 2
            yield "Loading Dataset 2...", ""
            texts2 = load_dataset_texts(dataset2_name, dataset2_split, dataset2_text_column)
            yield "Computing embeddings for Dataset 2...", ""
            #embeddings2 = compute_embeddings(texts2, batch_size=64, progress=progress, desc="Dataset 2 embeddings")
            embeddings2 = model.encode(texts2, show_progressbar=True)
            # Deduplicate Dataset 2 against Dataset 1
            yield "Deduplicating Dataset 2 against Dataset 1...", ""
            duplicate_indices, duplicate_mapping = deduplicate_embeddings(
                embeddings1, embeddings_b=embeddings2, threshold=threshold, progress=progress
            )

            num_duplicates = len(duplicate_indices)
            result_text = (
                f"**Total documents in {dataset2_name}/{dataset2_split}:** {len(texts2)}\n\n"
                f"**Duplicates found in Dataset 2:** {num_duplicates}\n\n"
                f"**Unique documents after deduplication:** {len(texts2) - num_duplicates}\n\n"
            )

            if num_duplicates > 0:
                result_text += "**Sample duplicates from Dataset 2:**\n\n"
                for idx in duplicate_indices[:5]:
                    orig_text = texts1[duplicate_mapping[idx]]
                    dup_text = texts2[idx]
                    differences = display_word_differences(orig_text, dup_text)
                    result_text += (
                        f"**Original (Dataset 1):**\n{orig_text}\n\n"
                        f"**Duplicate (Dataset 2):**\n{dup_text}\n\n"
                        f"**Differences:**\n{differences}\n"
                        + "-" * 50 + "\n\n"
                    )
            else:
                result_text += "No duplicates found."

            yield "Deduplication completed.", result_text

    except Exception as e:
        yield f"An error occurred: {e}", ""
        raise e

with gr.Blocks(css="#status_output { height: 150px; overflow: auto; }") as demo:
    gr.Markdown("# Semantic Deduplication")

    deduplication_type = gr.Radio(
        choices=["Single dataset", "Cross-dataset"],
        label="Deduplication Type",
        value="Single dataset",
    )

    with gr.Row():
        dataset1_name = gr.Textbox(value=default_dataset_name, label="Dataset 1 Name")
        dataset1_split = gr.Textbox(value=default_dataset_split, label="Dataset 1 Split")
        dataset1_text_column = gr.Textbox(value=default_text_column, label="Text Column Name")

    dataset2_inputs = gr.Column(visible=False)
    with dataset2_inputs:
        gr.Markdown("### Dataset 2")
        with gr.Row():
            dataset2_name = gr.Textbox(value=default_dataset_name, label="Dataset 2 Name")
            dataset2_split = gr.Textbox(value=default_dataset_split, label="Dataset 2 Split")
            dataset2_text_column = gr.Textbox(value=default_text_column, label="Text Column Name")

    threshold = gr.Slider(0.0, 1.0, value=default_threshold, label="Similarity Threshold")
    compute_button = gr.Button("Compute")
    status_output = gr.Markdown(elem_id="status_output")
    result_output = gr.Markdown()

    def update_visibility(choice):
        return gr.update(visible=choice == "Cross-dataset")

    deduplication_type.change(update_visibility, inputs=deduplication_type, outputs=dataset2_inputs)

    compute_button.click(
        fn=perform_deduplication,
        inputs=[
            deduplication_type,
            dataset1_name,
            dataset1_split,
            dataset1_text_column,
            dataset2_name,
            dataset2_split,
            dataset2_text_column,
            threshold,
        ],
        outputs=[status_output, result_output],
    )

demo.launch()