Update app.py

app.py

@@ -1,93 +1,93 @@
-import gradio as gr
-import pandas as pd
-from clean import clean_data
-from report import create_full_report, REPORT_DIR
-import os
-import tempfile
-
-def clean_and_visualize(file, progress=gr.Progress()):
-    # Load the data
-    df = pd.read_csv(file.name)
-
-    # Clean the data
-    cleaned_df = None
-    nonconforming_cells_before = None
-    process_times = None
-    removed_columns = None
-    removed_rows = None
-
-    for progress_value, status_text in clean_data(df):
-        if isinstance(status_text, tuple):
-            cleaned_df, nonconforming_cells_before, process_times, removed_columns, removed_rows = status_text
-            progress(progress_value, desc="Cleaning completed")
-        else:
-            progress(progress_value, desc=status_text)
-
-    # Generate full visualization report
-    create_full_report(
-        df,
-        cleaned_df,
-        nonconforming_cells_before,
-        process_times,
-        removed_columns,
-        removed_rows
-    )
-
-    # Save cleaned DataFrame to a temporary CSV file
-    with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as tmp_file:
-        cleaned_df.to_csv(tmp_file.name, index=False)
-        cleaned_csv_path = tmp_file.name
-
-    # Collect all generated images
-    image_files = [os.path.join(REPORT_DIR, f) for f in os.listdir(REPORT_DIR) if f.endswith('.png')]
-
-    return cleaned_csv_path, image_files
-
-def launch_app():
-    with gr.Blocks() as app:
-        gr.Markdown("# Data Cleaning and Visualization App")
-
-        with gr.Row():
-            file_input = gr.File(label="Upload CSV File")
-
-        with gr.Row():
-            clean_button = gr.Button("Start Cleaning")
-
-        with gr.Row():
-            progress_bar = gr.Progress()
-
-        with gr.Row():
-            download_button = gr.Button("Download Cleaned CSV", visible=False)
-            cleaned_file_output = gr.File(label="Cleaned CSV", visible=False)
-
-        with gr.Row():
-            output_gallery = gr.Gallery(label="Visualization Results", show_label=True, elem_id="gallery", columns=[2],
-                                        rows=[2], object_fit="contain", height="auto")
-
-        def process_and_show_download(file):
-            cleaned_csv_path, image_files = clean_and_visualize(file, progress=progress_bar)
-            return (
-                gr.Button.update(visible=True),
-                gr.File.update(value=cleaned_csv_path, visible=True),
-                image_files
-            )
-
-        clean_button.click(
-            fn=process_and_show_download,
-            inputs=file_input,
-            outputs=[download_button, cleaned_file_output, output_gallery]
-        )
-
-        def trigger_download():
-            return gr.File.update(visible=True)
-
-        download_button.click(
-            fn=trigger_download,
-            inputs=[],
-            outputs=[cleaned_file_output]
-        )
-
-    app.launch()
-
-if __name__ == "__main__":
+import gradio as gr
+import pandas as pd
+from clean import clean_data
+from report import create_full_report, REPORT_DIR
+import os
+import tempfile
+
+def clean_and_visualize(file, progress=gr.Progress()):
+    # Load the data
+    df = pd.read_csv(file.name)
+
+    # Clean the data
+    cleaned_df = None
+    nonconforming_cells_before = None
+    process_times = None
+    removed_columns = None
+    removed_rows = None
+
+    for progress_value, status_text in clean_data(df):
+        if isinstance(status_text, tuple):
+            cleaned_df, nonconforming_cells_before, process_times, removed_columns, removed_rows = status_text
+            progress(progress_value, desc="Cleaning completed")
+        else:
+            progress(progress_value, desc=status_text)
+
+    # Generate full visualization report
+    create_full_report(
+        df,
+        cleaned_df,
+        nonconforming_cells_before,
+        process_times,
+        removed_columns,
+        removed_rows
+    )
+
+    # Save cleaned DataFrame to a temporary CSV file
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as tmp_file:
+        cleaned_df.to_csv(tmp_file.name, index=False)
+        cleaned_csv_path = tmp_file.name
+
+    # Collect all generated images
+    image_files = [os.path.join(REPORT_DIR, f) for f in os.listdir(REPORT_DIR) if f.endswith('.png')]
+
+    return cleaned_csv_path, image_files
+
+def launch_app():
+    with gr.Blocks() as app:
+        gr.Markdown("# Data Cleaning and Visualization App")
+
+        with gr.Row():
+            file_input = gr.File(label="Upload CSV File")
+
+        with gr.Row():
+            clean_button = gr.Button("Start Cleaning")
+
+        with gr.Row():
+            progress_bar = gr.Progress()
+
+        with gr.Row():
+            download_button = gr.Button("Download Cleaned CSV", visible=False)
+            cleaned_file_output = gr.File(label="Cleaned CSV", visible=False)
+
+        with gr.Row():
+            output_gallery = gr.Gallery(label="Visualization Results", show_label=True, elem_id="gallery", columns=[2],
+                                        rows=[2], object_fit="contain", height="auto")
+
+        def process_and_show_download(file):
+            cleaned_csv_path, image_files = clean_and_visualize(file, progress=progress_bar)
+            return (
+                gr.Button.update(visible=True),
+                gr.File.update(value=cleaned_csv_path, visible=True),
+                image_files
+            )
+
+        clean_button.click(
+            fn=process_and_show_download,
+            inputs=file_input,
+            outputs=[download_button, cleaned_file_output, output_gallery]
+        )
+
+        def trigger_download():
+            return gr.File.update(visible=True)
+
+        download_button.click(
+            fn=trigger_download,
+            inputs=[],
+            outputs=[cleaned_file_output]
+        )
+
+    app.launch()
+
+if __name__ == "__main__":
     launch_app()