Updated app with code for deduplication

app.py

@@ -22,19 +22,17 @@ default_threshold = 0.9
 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]]:
+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.
     """
-    # Update progress to indicate building the index
-    progress(0, desc="Building search index...")
+    # 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
-    progress(0, desc="Finding nearest neighbors...")
     results = reach.nearest_neighbor_threshold(
         embedding_matrix,
         threshold=threshold,
@@ -42,9 +40,8 @@ def deduplicate(embedding_matrix: np.ndarray, threshold: float, batch_size: int
         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)):
+    # Processing duplicates
+    for i, similar_items in enumerate(results):
         if i not in deduplicated_indices:
             continue
 
@@ -57,19 +54,17 @@ def deduplicate(embedding_matrix: np.ndarray, threshold: float, batch_size: int
 
     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]]:
+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.
     """
-    # Update progress to indicate building the index
-    progress(0, desc="Building search index from Dataset 1...")
+    # 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
-    progress(0, desc="Finding nearest neighbors between datasets...")
     results = reach.nearest_neighbor_threshold(
         embedding_matrix_2,
         threshold=threshold,
@@ -77,9 +72,8 @@ def deduplicate_across_datasets(embedding_matrix_1: np.ndarray, embedding_matrix
         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)):
+    # 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:
@@ -103,13 +97,12 @@ def perform_deduplication(
     threshold=default_threshold,
     progress=gr.Progress(track_tqdm=True)
 ):
-    # Monkey-patch tqdm
+    # Deep Monkey-Patching of 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
+    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
@@ -117,140 +110,121 @@ def perform_deduplication(
         
         if deduplication_type == "Single dataset":
             # Load Dataset 1
-            progress(0, desc="Loading Dataset 1...")
+            gr.print("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...")
+            gr.print("Extracting texts from Dataset 1...")
             texts = [example[dataset1_text_column] for example in ds]
             
             # Compute embeddings
-            progress(0, desc="Computing embeddings for Dataset 1...")
+            gr.print("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
+            gr.print("Deduplicating embeddings...")
+            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."
+            
             return result_text
                 
         elif deduplication_type == "Cross-dataset":
             # Load Dataset 1
-            progress(0, desc="Loading Dataset 1...")
+            gr.print("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...")
+            gr.print("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...")
+            gr.print("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...")
+            gr.print("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...")
+            gr.print("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...")
+            gr.print("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
+            gr.print("Deduplicating embeddings across datasets...")
+            duplicate_indices_in_ds2, duplicate_to_original_mapping = deduplicate_across_datasets(
+                embedding_matrix1, embedding_matrix2, threshold
             )
             
+            num_duplicates = len(duplicate_indices_in_ds2)
+            num_total_ds2 = len(texts2)
+            num_unique_ds2 = num_total_ds2 - num_duplicates
+            
+            result_text = f"**Total documents in {dataset2_name}/{dataset2_split}:** {num_total_ds2}\n"
+            result_text += f"**Number of duplicates found in {dataset2_name}/{dataset2_split}:** {num_duplicates}\n"
+            result_text += f"**Number of unique documents in {dataset2_name}/{dataset2_split} after deduplication:** {num_unique_ds2}\n\n"
+            
+            # 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
 
     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
+        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")
@@ -316,6 +290,324 @@ 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 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