Updates

app.py

@@ -1,5 +1,3 @@
-# Try last 2 approaches, app was not rebuilding properly
-
 import gradio as gr
 from datasets import load_dataset
 import numpy as np
@@ -28,15 +26,6 @@ def batch_iterable(iterable, batch_size):
     for i in range(0, len(iterable), batch_size):
         yield iterable[i:i + batch_size]
 
-def compute_embeddings_with_progress(texts, batch_size, progress, desc="Computing embeddings"):
-    embeddings = []
-    total_batches = (len(texts) + batch_size - 1) // batch_size
-    for batch_texts in progress.tqdm(batch_iterable(texts, batch_size), desc=desc, total=total_batches):
-        batch_embeddings = model.encode(batch_texts, show_progressbar=False)
-        embeddings.append(batch_embeddings)
-    embedding_matrix = np.concatenate(embeddings, axis=0)
-    return embedding_matrix
-
 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(('+', '-'))])
@@ -76,7 +65,17 @@ def perform_deduplication(
             # Compute embeddings
             status = "Computing embeddings for Dataset 1..."
             yield status, ""
-            embedding_matrix = compute_embeddings_with_progress(texts, batch_size=64, progress=progress, desc="Computing embeddings for Dataset 1")
+            embeddings = []
+            batch_size = 64
+            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)
+                # Update progress
+                progress((i + 1) / total_batches, desc="Computing embeddings for Dataset 1")
+                # Yield control back to Gradio
+                yield status, ""
+            embedding_matrix = np.concatenate(embeddings, axis=0)
 
             # Deduplicate
             status = "Deduplicating embeddings..."
@@ -143,12 +142,31 @@ def perform_deduplication(
             # Compute embeddings for Dataset 1
             status = "Computing embeddings for Dataset 1..."
             yield status, ""
-            embedding_matrix1 = compute_embeddings_with_progress(texts1, batch_size=64, progress=progress, desc="Computing embeddings for Dataset 1")
+            embeddings1 = []
+            batch_size = 64
+            total_batches1 = (len(texts1) + batch_size - 1) // batch_size
+            for i, batch_texts in enumerate(batch_iterable(texts1, batch_size)):
+                batch_embeddings = model.encode(batch_texts, show_progressbar=False)
+                embeddings1.append(batch_embeddings)
+                # Update progress
+                progress((i + 1) / total_batches1, desc="Computing embeddings for Dataset 1")
+                # Yield control back to Gradio
+                yield status, ""
+            embedding_matrix1 = np.concatenate(embeddings1, axis=0)
 
             # Compute embeddings for Dataset 2
             status = "Computing embeddings for Dataset 2..."
             yield status, ""
-            embedding_matrix2 = compute_embeddings_with_progress(texts2, batch_size=64, progress=progress, desc="Computing embeddings for Dataset 2")
+            embeddings2 = []
+            total_batches2 = (len(texts2) + batch_size - 1) // batch_size
+            for i, batch_texts in enumerate(batch_iterable(texts2, batch_size)):
+                batch_embeddings = model.encode(batch_texts, show_progressbar=False)
+                embeddings2.append(batch_embeddings)
+                # Update progress
+                progress((i + 1) / total_batches2, desc="Computing embeddings for Dataset 2")
+                # Yield control back to Gradio
+                yield status, ""
+            embedding_matrix2 = np.concatenate(embeddings2, axis=0)
 
             # Deduplicate across datasets
             status = "Deduplicating embeddings across datasets..."
@@ -284,7 +302,7 @@ with gr.Blocks() as demo:
         label="Similarity Threshold"
     )
 
-    compute_button = gr.Button("Run")
+    compute_button = gr.Button("Compute")
 
     status_output = gr.Markdown()
     result_output = gr.Markdown()
@@ -318,1825 +336,3 @@ with gr.Blocks() as demo:
     )
 
 demo.launch()
-
-
-# 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
-# import tqdm
-
-# # Load the model at startup
-# model = StaticModel.from_pretrained("minishlab/M2V_base_output")
-
-# # Update default dataset to 'sst2' and set default threshold to 0.9
-# 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 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 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, ""
-#             embeddings = []
-#             batch_size = 64
-#             total_batches = (len(texts) + batch_size - 1) // batch_size
-#             for batch_texts in progress.tqdm(batch_iterable(texts, batch_size), desc="Computing embeddings", total=total_batches):
-#                 batch_embeddings = model.encode(batch_texts, show_progressbar=False)
-#                 embeddings.append(batch_embeddings)
-#             embedding_matrix = np.concatenate(embeddings, axis=0)
-
-#             # 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":
-#             # 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, ""
-#             embeddings1 = []
-#             batch_size = 64
-#             total_batches1 = (len(texts1) + batch_size - 1) // batch_size
-#             for batch_texts in progress.tqdm(batch_iterable(texts1, batch_size), desc="Computing embeddings for Dataset 1", total=total_batches1):
-#                 batch_embeddings = model.encode(batch_texts, show_progressbar=False)
-#                 embeddings1.append(batch_embeddings)
-#             embedding_matrix1 = np.concatenate(embeddings1, axis=0)
-
-#             # Compute embeddings for Dataset 2
-#             status = "Computing embeddings for Dataset 2..."
-#             yield status, ""
-#             embeddings2 = []
-#             total_batches2 = (len(texts2) + batch_size - 1) // batch_size
-#             for batch_texts in progress.tqdm(batch_iterable(texts2, batch_size), desc="Computing embeddings for Dataset 2", total=total_batches2):
-#                 batch_embeddings = model.encode(batch_texts, show_progressbar=False)
-#                 embeddings2.append(batch_embeddings)
-#             embedding_matrix2 = np.concatenate(embeddings2, axis=0)
-
-#             # 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(embedding_matrix: np.ndarray, threshold: float, batch_size: int = 1024, progress=None) -> tuple[np.ndarray, dict[int, int]]:
-#     """
-#     Deduplicate embeddings and return the deduplicated indices and a mapping of removed indices to their corresponding original indices.
-#     """
-#     # 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 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]]:
-#     """
-#     Deduplicate embeddings across two datasets and return the indices of duplicates between them.
-#     """
-#     # 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
-
-# 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=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")
-
-#     status_output = gr.Markdown()
-#     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
-# # from reach import Reach
-# # from difflib import ndiff
-# # import tqdm
-
-# # # Load the model at startup
-# # model = StaticModel.from_pretrained("minishlab/M2V_base_output")
-
-# # # Update default dataset to 'sst2' and set default threshold to 0.9
-# # 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 = []
-# #     for batch in progress.tqdm(batch_iterable(texts, batch_size), total=(len(texts) + batch_size - 1) // batch_size, desc=desc):
-# #         batch_embeddings = model.encode(batch, show_progressbar=False)
-# #         embeddings.append(batch_embeddings)
-# #     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]]:
-# #     """
-# #     Deduplicate embeddings and return the deduplicated indices and a mapping of removed indices to their corresponding original indices.
-# #     """
-# #     # 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 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]]:
-# #     """
-# #     Deduplicate embeddings across two datasets and return the indices of duplicates between them.
-# #     """
-# #     # 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
-
-# # 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 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 = 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":
-# #             # 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\n"
-# #             result_text += f"**Number of duplicates found in {dataset2_name}/{dataset2_split}:** {num_duplicates}\n\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
-
-# # 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=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")
-
-# #     status_output = gr.Markdown()
-# #     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()
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-# # # 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
-# # # import sys
-# # # import tqdm
-
-# # # # Load the model at startup
-# # # model = StaticModel.from_pretrained("minishlab/M2V_base_output")
-
-# # # # Update default dataset to 'sst2' and set default threshold to 0.9
-# # # 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 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.
-# # #     """
-# # #     # Building the 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
-# # #     results = reach.nearest_neighbor_threshold(
-# # #         embedding_matrix,
-# # #         threshold=threshold,
-# # #         batch_size=batch_size,
-# # #         show_progressbar=True  # Allow internal progress bar
-# # #     )
-
-# # #     # Processing duplicates
-# # #     for i, similar_items in enumerate(results):
-# # #         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 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.
-# # #     """
-# # #     # Building the 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
-# # #     results = reach.nearest_neighbor_threshold(
-# # #         embedding_matrix_2,
-# # #         threshold=threshold,
-# # #         batch_size=batch_size,
-# # #         show_progressbar=True  # Allow internal progress bar
-# # #     )
-
-# # #     # Processing duplicates
-# # #     for i, similar_items in enumerate(results):
-# # #         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
-
-# # # 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 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)
-# # # ):
-# # #     # Deep Monkey-Patching of tqdm
-# # #     original_tqdm = tqdm.tqdm
-# # #     tqdm.tqdm = progress.tqdm
-# # #     for mod_name in list(sys.modules.keys()):
-# # #         if 'tqdm' in mod_name:
-# # #             sys.modules[mod_name].tqdm = progress.tqdm
-
-# # #     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 = model.encode(texts, show_progressbar=True)  # Enable internal progress bar
-            
-# # #             # Deduplicate
-# # #             status = "Deduplicating embeddings..."
-# # #             yield status, ""
-# # #             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"
-            
-# # #             # 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":
-# # #             # 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 = model.encode(texts1, show_progressbar=True)
-            
-# # #             # Compute embeddings for Dataset 2
-# # #             status = "Computing embeddings for Dataset 2..."
-# # #             yield status, ""
-# # #             embedding_matrix2 = model.encode(texts2, show_progressbar=True)
-            
-# # #             # 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
-# # #             )
-            
-# # #             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
-
-# # #     finally:
-# # #         # Restore original tqdm
-# # #         tqdm.tqdm = original_tqdm
-# # #         for mod_name in list(sys.modules.keys()):
-# # #             if 'tqdm' in mod_name:
-# # #                 sys.modules[mod_name].tqdm = original_tqdm
-
-# # # 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=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")
-
-# # #     status_output = gr.Markdown()
-# # #     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
-# # # from reach import Reach
-# # # from difflib import ndiff
-# # # import sys
-# # # import tqdm
-
-# # # # Load the model at startup
-# # # model = StaticModel.from_pretrained("minishlab/M2V_base_output")
-
-# # # # Update default dataset to 'sst2' and set default threshold to 0.9
-# # # 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 deduplicate(embedding_matrix: np.ndarray, threshold: float, batch_size: int = 1024, progress=None) -> tuple[np.ndarray, dict[int, int]]:
-# # #     """
-# # #     Deduplicate embeddings and return the deduplicated indices and a mapping of removed indices to their corresponding original indices.
-# # #     """
-# # #     # Update progress to indicate 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=True  # Allow 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 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]]:
-# # #     """
-# # #     Deduplicate embeddings across two datasets and return the indices of duplicates between them.
-# # #     """
-# # #     # Update progress to indicate building the index
-# # #     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=True  # Allow 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
-
-# # # 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 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)
-# # # ):
-# # #     # Monkey-patch tqdm
-# # #     original_tqdm = tqdm.tqdm
-# # #     original_reach_tqdm = Reach.__dict__['tqdm'] if 'tqdm' in Reach.__dict__ else None
-# # #     tqdm.tqdm = progress.tqdm
-# # #     sys.modules['tqdm'].tqdm = progress.tqdm
-# # #     sys.modules['tqdm.auto'].tqdm = progress.tqdm
-# # #     Reach.tqdm = progress.tqdm  # Monkey-patch reach's tqdm
-
-# # #     try:
-# # #         # Convert threshold to float
-# # #         threshold = float(threshold)
-        
-# # #         if deduplication_type == "Single dataset":
-# # #             # Load Dataset 1
-# # #             progress(0, desc="Loading Dataset 1...")
-# # #             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
-# # #             progress(0, desc="Extracting texts from Dataset 1...")
-# # #             texts = [example[dataset1_text_column] for example in ds]
-            
-# # #             # Compute embeddings
-# # #             progress(0, desc="Computing embeddings for Dataset 1...")
-# # #             embedding_matrix = model.encode(texts, show_progressbar=True)  # Enable internal progress bar
-            
-# # #             # Deduplicate
-# # #             result_text = deduplicate_and_prepare_results_single(
-# # #                 embedding_matrix, texts, threshold, progress
-# # #             )
-            
-# # #             return result_text
-                
-# # #         elif deduplication_type == "Cross-dataset":
-# # #             # Load Dataset 1
-# # #             progress(0, desc="Loading Dataset 1...")
-# # #             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
-# # #             progress(0, desc="Loading Dataset 2...")
-# # #             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
-# # #             progress(0, desc="Extracting texts from Dataset 1...")
-# # #             texts1 = [example[dataset1_text_column] for example in ds1]
-            
-# # #             # Extract texts from Dataset 2
-# # #             progress(0, desc="Extracting texts from Dataset 2...")
-# # #             texts2 = [example[dataset2_text_column] for example in ds2]
-            
-# # #             # Compute embeddings for Dataset 1
-# # #             progress(0, desc="Computing embeddings for Dataset 1...")
-# # #             embedding_matrix1 = model.encode(texts1, show_progressbar=True)
-            
-# # #             # Compute embeddings for Dataset 2
-# # #             progress(0, desc="Computing embeddings for Dataset 2...")
-# # #             embedding_matrix2 = model.encode(texts2, show_progressbar=True)
-            
-# # #             # Deduplicate across datasets
-# # #             result_text = deduplicate_and_prepare_results_cross(
-# # #                 embedding_matrix1, embedding_matrix2, texts1, texts2, threshold, progress, dataset2_name, dataset2_split
-# # #             )
-            
-# # #             return result_text
-
-# # #     finally:
-# # #         # Restore original tqdm
-# # #         tqdm.tqdm = original_tqdm
-# # #         sys.modules['tqdm'].tqdm = original_tqdm
-# # #         sys.modules['tqdm.auto'].tqdm = original_tqdm
-
-# # #         # Restore reach's original tqdm
-# # #         if original_reach_tqdm is not None:
-# # #             Reach.tqdm = original_reach_tqdm
-# # #         else:
-# # #             del Reach.tqdm  # If it wasn't originally in Reach's __dict__
-
-# # # def deduplicate_and_prepare_results_single(embedding_matrix, texts, threshold, progress):
-# # #     # Deduplicate
-# # #     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."
-    
-# # #     return result_text
-
-# # # def deduplicate_and_prepare_results_cross(embedding_matrix1, embedding_matrix2, texts1, texts2, threshold, progress, dataset2_name, dataset2_split):
-# # #     # Deduplicate across datasets
-# # #     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."
-    
-# # #     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=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")
-
-# # #     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=output
-# # #     )
-    
-# # # demo.launch()
-
-
-
-
-# # # 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
-# # # import sys
-# # # import tqdm
-
-# # # # Load the model at startup
-# # # model = StaticModel.from_pretrained("minishlab/M2V_base_output")
-
-# # # # Load the default datasets at startup
-# # # default_dataset1_name = "ag_news"
-# # # default_dataset1_split = "train"
-# # # default_dataset2_name = "ag_news"
-# # # default_dataset2_split = "test"
-
-# # # ds_default1 = load_dataset(default_dataset1_name, split=default_dataset1_split)
-# # # ds_default2 = load_dataset(default_dataset2_name, split=default_dataset2_split)
-
-# # # def deduplicate(embedding_matrix: np.ndarray, threshold: float, batch_size: int = 1024, progress=None) -> 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))])
-
-# # #     deduplicated_indices = set(range(len(embedding_matrix)))
-# # #     duplicate_to_original_mapping = {}
-
-# # #     results = reach.nearest_neighbor_threshold(
-# # #         embedding_matrix,
-# # #         threshold=threshold,
-# # #         batch_size=batch_size,
-# # #         show_progressbar=True  # Allow internal progress bar
-# # #     )
-
-# # #     # Process duplicates
-# # #     for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates", total=len(embedding_matrix))):
-# # #         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 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]]:
-# # #     """
-# # #     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))])
-
-# # #     duplicate_indices_in_test = []
-# # #     duplicate_to_original_mapping = {}
-
-# # #     results = reach.nearest_neighbor_threshold(
-# # #         embedding_matrix_2,
-# # #         threshold=threshold,
-# # #         batch_size=batch_size,
-# # #         show_progressbar=True  # Allow internal progress bar
-# # #     )
-
-# # #     for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates across datasets", total=len(embedding_matrix_2))):
-# # #         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
-
-# # # 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 perform_deduplication(
-# # #     deduplication_type,
-# # #     dataset1_name,
-# # #     dataset1_split,
-# # #     dataset1_text_column,
-# # #     dataset2_name="",
-# # #     dataset2_split="",
-# # #     dataset2_text_column="",
-# # #     threshold=0.8,
-# # #     progress=gr.Progress(track_tqdm=True)
-# # # ):
-# # #     # Monkey-patch tqdm
-# # #     original_tqdm = tqdm.tqdm
-# # #     original_reach_tqdm = Reach.__dict__['tqdm'] if 'tqdm' in Reach.__dict__ else None
-# # #     tqdm.tqdm = progress.tqdm
-# # #     sys.modules['tqdm'].tqdm = progress.tqdm
-# # #     sys.modules['tqdm.auto'].tqdm = progress.tqdm
-# # #     Reach.tqdm = progress.tqdm  # Monkey-patch reach's tqdm
-
-# # #     try:
-# # #         # Convert threshold to float
-# # #         threshold = float(threshold)
-        
-# # #         if deduplication_type == "Single dataset":
-# # #             # Check if the dataset is the default one
-# # #             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
-# # #             texts = [example[dataset1_text_column] for example in ds]
-            
-# # #             # Compute embeddings
-# # #             embedding_matrix = model.encode(texts, show_progressbar=True)  # Enable internal progress bar
-            
-# # #             # Deduplicate
-# # #             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
-# # #             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"
-            
-# # #             return result_text
-                
-# # #         elif deduplication_type == "Cross-dataset":
-# # #             # Dataset 1
-# # #             if dataset1_name == default_dataset1_name and dataset1_split == default_dataset1_split:
-# # #                 ds1 = ds_default1
-# # #             else:
-# # #                 ds1 = load_dataset(dataset1_name, split=dataset1_split)
-            
-# # #             # Dataset 2
-# # #             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
-# # #             texts1 = [example[dataset1_text_column] for example in ds1]
-# # #             texts2 = [example[dataset2_text_column] for example in ds2]
-            
-# # #             # Compute embeddings
-# # #             embedding_matrix1 = model.encode(texts1, show_progressbar=True)  # Enable internal progress bar
-# # #             embedding_matrix2 = model.encode(texts2, show_progressbar=True)  # Enable internal progress bar
-            
-# # #             # Deduplicate across datasets
-# # #             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
-# # #             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"
-            
-# # #             return result_text
-
-# # #     finally:
-# # #         # Restore original tqdm
-# # #         tqdm.tqdm = original_tqdm
-# # #         sys.modules['tqdm'].tqdm = original_tqdm
-# # #         sys.modules['tqdm.auto'].tqdm = original_tqdm
-
-# # #         # Restore reach's original tqdm
-# # #         if original_reach_tqdm is not None:
-# # #             Reach.tqdm = original_reach_tqdm
-# # #         else:
-# # #             del Reach.tqdm  # If it wasn't originally in Reach's __dict__
-
-# # # 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")
-# # #         dataset1_text_column = gr.Textbox(value="text", 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="ag_news", label="Dataset 2 Name")
-# # #             dataset2_split = gr.Textbox(value="test", label="Dataset 2 Split")
-# # #             dataset2_text_column = gr.Textbox(value="text", label="Text Column Name")
-    
-# # #     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_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=output
-# # #     )
-    
-# # # demo.launch()
-
-
-# # # # 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
-# # # # import sys
-# # # # import tqdm
-
-# # # # # Load the model at startup
-# # # # model = StaticModel.from_pretrained("minishlab/M2V_base_output")
-
-# # # # # Load the default datasets at startup
-# # # # default_dataset1_name = "ag_news"
-# # # # default_dataset1_split = "train"
-# # # # default_dataset2_name = "ag_news"
-# # # # default_dataset2_split = "test"
-
-# # # # ds_default1 = load_dataset(default_dataset1_name, split=default_dataset1_split)
-# # # # ds_default2 = load_dataset(default_dataset2_name, split=default_dataset2_split)
-
-# # # # def deduplicate(embedding_matrix: np.ndarray, threshold: float, batch_size: int = 1024, progress=None) -> 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))])
-
-# # # #     deduplicated_indices = set(range(len(embedding_matrix)))
-# # # #     duplicate_to_original_mapping = {}
-
-# # # #     results = reach.nearest_neighbor_threshold(
-# # # #         embedding_matrix,
-# # # #         threshold=threshold,
-# # # #         batch_size=batch_size,
-# # # #         show_progressbar=True  # Allow internal progress bar
-# # # #     )
-
-# # # #     # Process duplicates
-# # # #     for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates")):
-# # # #         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 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]]:
-# # # #     """
-# # # #     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))])
-
-# # # #     duplicate_indices_in_test = []
-# # # #     duplicate_to_original_mapping = {}
-
-# # # #     results = reach.nearest_neighbor_threshold(
-# # # #         embedding_matrix_2,
-# # # #         threshold=threshold,
-# # # #         batch_size=batch_size,
-# # # #         show_progressbar=True  # Allow internal progress bar
-# # # #     )
-
-# # # #     # Process duplicates
-# # # #     for i, similar_items in enumerate(progress.tqdm(results, desc="Processing duplicates across datasets")):
-# # # #         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
-
-# # # # 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 perform_deduplication(
-# # # #     deduplication_type,
-# # # #     dataset1_name,
-# # # #     dataset1_split,
-# # # #     dataset1_text_column,
-# # # #     dataset2_name="",
-# # # #     dataset2_split="",
-# # # #     dataset2_text_column="",
-# # # #     threshold=0.8,
-# # # #     progress=gr.Progress(track_tqdm=True)
-# # # # ):
-# # # #     # Monkey-patch tqdm
-# # # #     original_tqdm = tqdm.tqdm
-# # # #     tqdm.tqdm = progress.tqdm
-# # # #     sys.modules['tqdm'].tqdm = progress.tqdm
-# # # #     sys.modules['tqdm.auto'].tqdm = progress.tqdm
-
-# # # #     try:
-# # # #         # Convert threshold to float
-# # # #         threshold = float(threshold)
-        
-# # # #         if deduplication_type == "Single dataset":
-# # # #             # Check if the dataset is the default one
-# # # #             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
-# # # #             texts = [example[dataset1_text_column] for example in ds]
-            
-# # # #             # Compute embeddings
-# # # #             embedding_matrix = model.encode(texts, show_progressbar=True)  # Enable internal progress bar
-            
-# # # #             # Deduplicate
-# # # #             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
-# # # #             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"
-            
-# # # #             return result_text
-                
-# # # #         elif deduplication_type == "Cross-dataset":
-# # # #             # Dataset 1
-# # # #             if dataset1_name == default_dataset1_name and dataset1_split == default_dataset1_split:
-# # # #                 ds1 = ds_default1
-# # # #             else:
-# # # #                 ds1 = load_dataset(dataset1_name, split=dataset1_split)
-            
-# # # #             # Dataset 2
-# # # #             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
-# # # #             texts1 = [example[dataset1_text_column] for example in ds1]
-# # # #             texts2 = [example[dataset2_text_column] for example in ds2]
-            
-# # # #             # Compute embeddings
-# # # #             embedding_matrix1 = model.encode(texts1, show_progressbar=True)  # Enable internal progress bar
-# # # #             embedding_matrix2 = model.encode(texts2, show_progressbar=True)  # Enable internal progress bar
-            
-# # # #             # Deduplicate across datasets
-# # # #             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
-# # # #             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"
-            
-# # # #             return result_text
-
-# # # #     finally:
-# # # #         # Restore original tqdm
-# # # #         tqdm.tqdm = original_tqdm
-# # # #         sys.modules['tqdm'].tqdm = original_tqdm
-# # # #         sys.modules['tqdm.auto'].tqdm = original_tqdm
-
-# # # # 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")
-# # # #         dataset1_text_column = gr.Textbox(value="text", 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="ag_news", label="Dataset 2 Name")
-# # # #             dataset2_split = gr.Textbox(value="test", label="Dataset 2 Split")
-# # # #             dataset2_text_column = gr.Textbox(value="text", label="Text Column Name")
-    
-# # # #     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_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=output
-# # # #     )
-    
-# # # # demo.launch()