import gradio as gr from datasets import load_dataset import numpy as np import model2vec from reach import Reach from difflib import ndiff import time # Load the model at startup model = model2vec.StaticModel.from_pretrained("minishlab/M2V_base_output") # Default dataset parameters default_dataset1_name = "sst2" default_dataset1_split = "train" default_dataset2_name = "sst2" default_dataset2_split = "validation" default_text_column = "sentence" default_threshold = 0.9 # Load the default datasets at startup ds_default1 = load_dataset(default_dataset1_name, split=default_dataset1_split) ds_default2 = load_dataset(default_dataset2_name, split=default_dataset2_split) def batch_iterable(iterable, batch_size): """Helper function to create batches from an iterable.""" for i in range(0, len(iterable), batch_size): yield iterable[i:i + batch_size] def log_time(message, start_time=None, logs=None): """Helper function to log the start and end times.""" current_time = time.time() if start_time is not None: elapsed = current_time - start_time log_message = f"{message} - Took {elapsed:.2f} seconds" else: log_message = f"{message} - Started" if logs is not None: logs.append(log_message) def compute_embeddings(texts, batch_size, progress, logs, desc="Computing embeddings"): embeddings = [] total_batches = (len(texts) + batch_size - 1) // batch_size for i, batch_texts in enumerate(batch_iterable(texts, batch_size)): batch_embeddings = model.encode(batch_texts, show_progressbar=False) embeddings.append(batch_embeddings) progress((i + 1) / total_batches, desc=desc) return np.concatenate(embeddings, axis=0) def deduplicate( embedding_matrix: np.ndarray, threshold: float, batch_size: int = 1024, progress=None, logs=None ) -> tuple[np.ndarray, dict[int, int]]: # Building the index log_time("Building search index", logs=logs) reach = Reach( vectors=embedding_matrix, items=[str(i) for i in range(len(embedding_matrix))] ) deduplicated_indices = set(range(len(embedding_matrix))) duplicate_to_original_mapping = {} # Finding nearest neighbors log_time("Finding nearest neighbors", logs=logs) results = reach.nearest_neighbor_threshold( embedding_matrix, threshold=threshold, batch_size=batch_size, show_progressbar=False, # Disable internal progress bar ) # Processing duplicates with a progress bar total_items = len(embedding_matrix) log_time("Processing duplicates", logs=logs) for i, similar_items in enumerate( progress.tqdm(results, desc="Processing duplicates", total=total_items) ): if i not in deduplicated_indices: continue similar_indices = [int(item[0]) for item in similar_items if int(item[0]) != i] for sim_idx in similar_indices: if sim_idx in deduplicated_indices: deduplicated_indices.remove(sim_idx) duplicate_to_original_mapping[sim_idx] = i return np.array(list(deduplicated_indices)), duplicate_to_original_mapping def display_word_differences(x: str, y: str) -> str: diff = ndiff(x.split(), y.split()) return " ".join([word for word in diff if word.startswith(("+", "-"))]) def encode_texts(texts, progress=None, logs=None): embedding_matrix = model.encode(texts, show_progressbar=False) log_time("Encoding texts completed", logs=logs) return embedding_matrix 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), ): logs = [] # To store log messages try: # Convert threshold to float threshold = float(threshold) # Initialize status message log_time("Deduplication started", logs=logs) if deduplication_type == "Single dataset": # Load Dataset 1 start_time = time.time() log_time("Loading Dataset 1", logs=logs) if ( dataset1_name == default_dataset1_name and dataset1_split == default_dataset1_split ): ds = ds_default1 else: ds = load_dataset(dataset1_name, split=dataset1_split) log_time("Loading Dataset 1 completed", start_time=start_time, logs=logs) # Extract texts start_time = time.time() log_time("Extracting texts from Dataset 1", logs=logs) texts = [example[dataset1_text_column] for example in ds] log_time("Extracting texts from Dataset 1 completed", start_time=start_time, logs=logs) # Compute embeddings start_time = time.time() log_time("Computing embeddings for Dataset 1", logs=logs) embedding_matrix = encode_texts(texts, progress=progress, logs=logs) log_time("Computing embeddings for Dataset 1 completed", start_time=start_time, logs=logs) # Deduplicate start_time = time.time() log_time("Deduplicating embeddings", logs=logs) deduplicated_indices, duplicate_to_original_mapping = deduplicate( embedding_matrix, threshold, progress=progress, logs=logs ) log_time("Deduplication completed", start_time=start_time, logs=logs) # 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" ) # Show deduplicated examples if num_duplicates > 0: result_text += "**Examples of duplicates found:**\n\n" num_examples = min(5, num_duplicates) for duplicate_idx, original_idx in list(duplicate_to_original_mapping.items())[:num_examples]: original_text = texts[original_idx] duplicate_text = texts[duplicate_idx] differences = display_word_differences(original_text, duplicate_text) result_text += f"**Original text:**\n{original_text}\n\n" result_text += f"**Duplicate text:**\n{duplicate_text}\n\n" result_text += f"**Differences:**\n{differences}\n" result_text += "-" * 50 + "\n\n" else: result_text += "No duplicates found." log_time("Deduplication process finished", logs=logs) full_log = "\n".join(logs) # Combine all logs into one output yield full_log, result_text except Exception as e: full_log = "\n".join(logs) # Combine all logs into one output in case of an error yield f"An error occurred: {e}", "" raise e # Adjust the height of the status_output component using custom CSS 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_dataset1_name, label="Dataset 1 Name") dataset1_split = gr.Textbox(value=default_dataset1_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_dataset2_name, label="Dataset 2 Name") dataset2_split = gr.Textbox(value=default_dataset2_split, label="Dataset 2 Split") dataset2_text_column = gr.Textbox(value=default_text_column, label="Text Column Name") threshold = gr.Slider( minimum=0.0, maximum=1.0, value=default_threshold, label="Similarity Threshold" ) compute_button = gr.Button("Compute") # Use 'gr.Markdown' with 'elem_id' and custom CSS to adjust height status_output = gr.Markdown(elem_id="status_output") result_output = gr.Markdown() # Function to update the visibility of dataset2_inputs def update_visibility(deduplication_type_value): if deduplication_type_value == "Cross-dataset": return gr.update(visible=True) else: return gr.update(visible=False) 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() # import gradio as gr # from datasets import load_dataset # import numpy as np # #from model2vec import StaticModel # import model2vec # from reach import Reach # from difflib import ndiff # # Load the model at startup # model = model2vec.StaticModel.from_pretrained("minishlab/M2V_base_output") # # Default dataset parameters # default_dataset1_name = "sst2" # default_dataset1_split = "train" # default_dataset2_name = "sst2" # default_dataset2_split = "validation" # default_text_column = "sentence" # default_threshold = 0.9 # # Load the default datasets at startup # ds_default1 = load_dataset(default_dataset1_name, split=default_dataset1_split) # ds_default2 = load_dataset(default_dataset2_name, split=default_dataset2_split) # def batch_iterable(iterable, batch_size): # """Helper function to create batches from an iterable.""" # for i in range(0, len(iterable), batch_size): # yield iterable[i:i + batch_size] # def compute_embeddings(texts, batch_size, progress, desc="Computing embeddings"): # embeddings = [] # total_batches = (len(texts) + batch_size - 1) // batch_size # for i, batch_texts in enumerate(batch_iterable(texts, batch_size)): # batch_embeddings = model.encode(batch_texts, show_progressbar=False) # embeddings.append(batch_embeddings) # progress((i + 1) / total_batches, desc=desc) # return np.concatenate(embeddings, axis=0) # def deduplicate( # embedding_matrix: np.ndarray, # threshold: float, # batch_size: int = 1024, # progress=None # ) -> tuple[np.ndarray, dict[int, int]]: # # Building the index # progress(0, desc="Building search index...") # reach = Reach( # vectors=embedding_matrix, items=[str(i) for i in range(len(embedding_matrix))] # ) # deduplicated_indices = set(range(len(embedding_matrix))) # duplicate_to_original_mapping = {} # # Finding nearest neighbors # progress(0, desc="Finding nearest neighbors...") # results = reach.nearest_neighbor_threshold( # embedding_matrix, # threshold=threshold, # batch_size=batch_size, # show_progressbar=False, # Disable internal progress bar # ) # # Processing duplicates with a progress bar # total_items = len(embedding_matrix) # for i, similar_items in enumerate( # progress.tqdm(results, desc="Processing duplicates", total=total_items) # ): # if i not in deduplicated_indices: # continue # similar_indices = [int(item[0]) for item in similar_items if int(item[0]) != i] # for sim_idx in similar_indices: # if sim_idx in deduplicated_indices: # deduplicated_indices.remove(sim_idx) # duplicate_to_original_mapping[sim_idx] = i # return np.array(list(deduplicated_indices)), duplicate_to_original_mapping # def display_word_differences(x: str, y: str) -> str: # diff = ndiff(x.split(), y.split()) # return " ".join([word for word in diff if word.startswith(("+", "-"))]) # def encode_texts(texts, progress=None): # embedding_matrix = model.encode(texts, show_progressbar=False) # return embedding_matrix # 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: # # Convert threshold to float # threshold = float(threshold) # # Initialize status message # status = "" # if deduplication_type == "Single dataset": # # Load Dataset 1 # status = "Loading Dataset 1..." # yield status, "" # if ( # dataset1_name == default_dataset1_name # and dataset1_split == default_dataset1_split # ): # ds = ds_default1 # else: # ds = load_dataset(dataset1_name, split=dataset1_split) # # Extract texts # status = "Extracting texts from Dataset 1..." # yield status, "" # texts = [example[dataset1_text_column] for example in ds] # # Compute embeddings # status = "Computing embeddings for Dataset 1..." # yield status, "" # embedding_matrix = encode_texts(texts, progress=progress) # #embedding_matrix = model.encode(texts, show_progressbar=True) # # embedding_matrix = compute_embeddings( # # texts, # # batch_size=64, # # progress=progress, # # desc="Computing embeddings for Dataset 1", # # ) # # Deduplicate # status = "Deduplicating embeddings..." # yield status, "" # deduplicated_indices, duplicate_to_original_mapping = deduplicate( # embedding_matrix, threshold, progress=progress # ) # # 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" # ) # # Show deduplicated examples # if num_duplicates > 0: # result_text += "**Examples of duplicates found:**\n\n" # num_examples = min(5, num_duplicates) # for duplicate_idx, original_idx in list(duplicate_to_original_mapping.items())[:num_examples]: # original_text = texts[original_idx] # duplicate_text = texts[duplicate_idx] # differences = display_word_differences(original_text, duplicate_text) # result_text += f"**Original text:**\n{original_text}\n\n" # result_text += f"**Duplicate text:**\n{duplicate_text}\n\n" # result_text += f"**Differences:**\n{differences}\n" # result_text += "-" * 50 + "\n\n" # else: # result_text += "No duplicates found." # # Final status # status = "Deduplication completed." # yield status, result_text # elif deduplication_type == "Cross-dataset": # # Similar code for cross-dataset deduplication # # Load Dataset 1 # status = "Loading Dataset 1..." # yield status, "" # if ( # dataset1_name == default_dataset1_name # and dataset1_split == default_dataset1_split # ): # ds1 = ds_default1 # else: # ds1 = load_dataset(dataset1_name, split=dataset1_split) # # Load Dataset 2 # status = "Loading Dataset 2..." # yield status, "" # if ( # dataset2_name == default_dataset2_name # and dataset2_split == default_dataset2_split # ): # ds2 = ds_default2 # else: # ds2 = load_dataset(dataset2_name, split=dataset2_split) # # Extract texts from Dataset 1 # status = "Extracting texts from Dataset 1..." # yield status, "" # texts1 = [example[dataset1_text_column] for example in ds1] # # Extract texts from Dataset 2 # status = "Extracting texts from Dataset 2..." # yield status, "" # texts2 = [example[dataset2_text_column] for example in ds2] # # Compute embeddings for Dataset 1 # status = "Computing embeddings for Dataset 1..." # yield status, "" # embedding_matrix1 = compute_embeddings( # texts1, # batch_size=64, # progress=progress, # desc="Computing embeddings for Dataset 1", # ) # # Compute embeddings for Dataset 2 # status = "Computing embeddings for Dataset 2..." # yield status, "" # embedding_matrix2 = compute_embeddings( # texts2, # batch_size=64, # progress=progress, # desc="Computing embeddings for Dataset 2", # ) # # Deduplicate across datasets # status = "Deduplicating embeddings across datasets..." # yield status, "" # duplicate_indices_in_ds2, duplicate_to_original_mapping = deduplicate_across_datasets( # embedding_matrix1, embedding_matrix2, threshold, progress=progress # ) # 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" # # Show deduplicated examples # if num_duplicates > 0: # result_text += "**Examples of duplicates found in Dataset 2:**\n\n" # num_examples = min(5, num_duplicates) # for duplicate_idx in duplicate_indices_in_ds2[:num_examples]: # original_idx = duplicate_to_original_mapping[duplicate_idx] # original_text = texts1[original_idx] # duplicate_text = texts2[duplicate_idx] # differences = display_word_differences(original_text, duplicate_text) # result_text += f"**Original text (Dataset 1):**\n{original_text}\n\n" # result_text += f"**Duplicate text (Dataset 2):**\n{duplicate_text}\n\n" # result_text += f"**Differences:**\n{differences}\n" # result_text += "-" * 50 + "\n\n" # else: # result_text += "No duplicates found." # # Final status # status = "Deduplication completed." # yield status, result_text # except Exception as e: # yield f"An error occurred: {e}", "" # raise e # def deduplicate_across_datasets( # embedding_matrix_1: np.ndarray, # embedding_matrix_2: np.ndarray, # threshold: float, # batch_size: int = 1024, # progress=None # ) -> tuple[list[int], dict[int, int]]: # # Building the index from Dataset 1 # progress(0, desc="Building search index from Dataset 1...") # reach = Reach( # vectors=embedding_matrix_1, items=[str(i) for i in range(len(embedding_matrix_1))] # ) # duplicate_indices_in_test = [] # duplicate_to_original_mapping = {} # # Finding nearest neighbors between datasets # progress(0, desc="Finding nearest neighbors between datasets...") # results = reach.nearest_neighbor_threshold( # embedding_matrix_2, # threshold=threshold, # batch_size=batch_size, # show_progressbar=False, # Disable internal progress bar # ) # total_items = len(embedding_matrix_2) # # Processing duplicates with a progress bar # for i, similar_items in enumerate( # progress.tqdm(results, desc="Processing duplicates across datasets", total=total_items) # ): # similar_indices = [int(item[0]) for item in similar_items if item[1] >= threshold] # if similar_indices: # duplicate_indices_in_test.append(i) # duplicate_to_original_mapping[i] = similar_indices[0] # return duplicate_indices_in_test, duplicate_to_original_mapping # # Adjust the height of the status_output component using custom CSS # 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_dataset1_name, label="Dataset 1 Name") # dataset1_split = gr.Textbox(value=default_dataset1_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_dataset2_name, label="Dataset 2 Name") # dataset2_split = gr.Textbox(value=default_dataset2_split, label="Dataset 2 Split") # dataset2_text_column = gr.Textbox(value=default_text_column, label="Text Column Name") # threshold = gr.Slider( # minimum=0.0, maximum=1.0, value=default_threshold, label="Similarity Threshold" # ) # compute_button = gr.Button("Compute") # # Use 'gr.Markdown' with 'elem_id' and custom CSS to adjust height # status_output = gr.Markdown(elem_id="status_output") # result_output = gr.Markdown() # # Function to update the visibility of dataset2_inputs # def update_visibility(deduplication_type_value): # if deduplication_type_value == "Cross-dataset": # return gr.update(visible=True) # else: # return gr.update(visible=False) # 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()