app.py

# import gradio as gr

# def greet(name):
#     return "Hello " + name + "!!"

# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
# demo.launch()


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

def deduplicate(embedding_matrix: np.ndarray, threshold: float, batch_size: int = 1024) -> tuple[np.ndarray, dict[int, int]]:
    """
    Deduplicate embeddings and return the deduplicated indices and a mapping of removed indices to their corresponding original indices.
    """
    reach = Reach(vectors=embedding_matrix, items=[str(i) for i in range(len(embedding_matrix))])

    # Use a set for deduplicated indices and keep track of duplicates
    deduplicated_indices = set(range(len(embedding_matrix)))  # Start with all indices as deduplicated
    duplicate_to_original_mapping = {}

    results = reach.nearest_neighbor_threshold(
        embedding_matrix, 
        threshold=threshold, 
        batch_size=batch_size, 
        show_progressbar=True
    )

    # Process duplicates
    for i, similar_items in enumerate(tqdm(results)):
        if i not in deduplicated_indices:
            continue  # Skip already marked duplicates

        # Similar items are returned as (index, score), we are only interested in the index
        similar_indices = [int(item[0]) for item in similar_items if int(item[0]) != i]

        # Mark similar documents as duplicates and map them to the original
        for sim_idx in similar_indices:
            if sim_idx in deduplicated_indices:
                deduplicated_indices.remove(sim_idx)
                duplicate_to_original_mapping[sim_idx] = i  # Map duplicate to original

    return np.array(list(deduplicated_indices)), duplicate_to_original_mapping

def deduplicate_across_datasets(embedding_matrix_1: np.ndarray, embedding_matrix_2: np.ndarray, threshold: float, batch_size: int = 1024) -> tuple[list[int], dict[int, int]]:
    """
    Deduplicate embeddings across two datasets and return the indices of duplicates between them.
    """
    reach = Reach(vectors=embedding_matrix_1, items=[str(i) for i in range(len(embedding_matrix_1))])

    # Keep track of duplicates in the second dataset
    duplicate_indices_in_test = []
    duplicate_to_original_mapping = {}

    # Find nearest neighbors from the test set in the train set
    results = reach.nearest_neighbor_threshold(
        embedding_matrix_2, 
        threshold=threshold, 
        batch_size=batch_size, 
        show_progressbar=True
    )

    # Process duplicates
    for i, similar_items in enumerate(tqdm(results)):
        # Similar items are returned as (index, score), we are only interested in the index
        similar_indices = [int(item[0]) for item in similar_items if item[1] >= threshold]  # Keep those above the threshold

        # If we find a similar item in the train set, mark it as a duplicate
        if similar_indices:
            duplicate_indices_in_test.append(i)
            duplicate_to_original_mapping[i] = similar_indices[0]  # Map duplicate in test to original in train

    return duplicate_indices_in_test, duplicate_to_original_mapping

def perform_deduplication(
    deduplication_type,
    dataset1_name,
    dataset1_split,
    dataset2_name,
    dataset2_split,
    threshold
):
    # Convert threshold to float
    threshold = float(threshold)
    
    if deduplication_type == "Single dataset":
        # Load the dataset
        ds = load_dataset(dataset1_name, split=dataset1_split)
        
        # Extract texts
        texts = [example['text'] for example in ds]
        
        # Compute embeddings
        model = StaticModel.from_pretrained("minishlab/M2V_base_output")
        embedding_matrix = model.encode(texts, show_progressbar=True)
        
        # Deduplicate
        deduplicated_indices, duplicate_to_original_mapping = deduplicate(embedding_matrix, threshold)
        
        # Prepare the results
        num_duplicates = len(duplicate_to_original_mapping)
        num_total = len(texts)
        num_deduplicated = len(deduplicated_indices)
        
        result_text = f"**Total documents:** {num_total}\n"
        result_text += f"**Number of duplicates found:** {num_duplicates}\n"
        result_text += f"**Number of unique documents after deduplication:** {num_deduplicated}\n\n"
        result_text += f"**Deduplicated indices:** {deduplicated_indices.tolist()}\n\n"
        result_text += f"**Duplicate to original mapping:** {duplicate_to_original_mapping}\n"
        
        return result_text
        
    elif deduplication_type == "Cross-dataset":
        # Load datasets
        ds1 = load_dataset(dataset1_name, split=dataset1_split)
        ds2 = load_dataset(dataset2_name, split=dataset2_split)
        
        # Extract texts
        texts1 = [example['text'] for example in ds1]
        texts2 = [example['text'] for example in ds2]
        
        # Compute embeddings
        model = StaticModel.from_pretrained("minishlab/M2V_base_output")
        embedding_matrix1 = model.encode(texts1, show_progressbar=True)
        embedding_matrix2 = model.encode(texts2, show_progressbar=True)
        
        # Deduplicate across datasets
        duplicate_indices_in_ds2, duplicate_to_original_mapping = deduplicate_across_datasets(embedding_matrix1, embedding_matrix2, threshold)
        
        num_duplicates = len(duplicate_indices_in_ds2)
        num_total_ds2 = len(texts2)
        num_unique_ds2 = num_total_ds2 - num_duplicates
        
        result_text = f"**Total documents in {dataset2_name}/{dataset2_split}:** {num_total_ds2}\n"
        result_text += f"**Number of duplicates found in {dataset2_name}/{dataset2_split}:** {num_duplicates}\n"
        result_text += f"**Number of unique documents in {dataset2_name}/{dataset2_split} after deduplication:** {num_unique_ds2}\n\n"
        result_text += f"**Duplicate indices in {dataset2_name}/{dataset2_split}:** {duplicate_indices_in_ds2}\n\n"
        result_text += f"**Duplicate to original mapping:** {duplicate_to_original_mapping}\n"
        
        return result_text

with gr.Blocks() 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="ag_news", label="Dataset 1 Name")
        dataset1_split = gr.Textbox(value="train", label="Dataset 1 Split")
    
    dataset2_row = gr.Row(visible=False)
    with dataset2_row:
        dataset2_name = gr.Textbox(value="ag_news", label="Dataset 2 Name")
        dataset2_split = gr.Textbox(value="test", label="Dataset 2 Split")
    
    threshold = gr.Slider(minimum=0.0, maximum=1.0, value=0.8, label="Similarity Threshold")
    
    compute_button = gr.Button("Compute")
    
    output = gr.Markdown()
    
    # Function to update the visibility of dataset2_row
    def update_visibility(deduplication_type):
        if deduplication_type == "Cross-dataset":
            return {dataset2_row: gr.update(visible=True)}
        else:
            return {dataset2_row: gr.update(visible=False)}
    
    deduplication_type.change(update_visibility, inputs=deduplication_type, outputs=[dataset2_row])
    
    compute_button.click(
        fn=perform_deduplication,
        inputs=[deduplication_type, dataset1_name, dataset1_split, dataset2_name, dataset2_split, threshold],
        outputs=output
    )
    
demo.launch()