reconnect moved files to git repo

utils/__init__.py

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+# Empty __init__.py to make utils a package

utils/file_handling.py

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+import pandas as pd
+import os
+
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in {'csv', 'xls', 'xlsx'}
+
+def read_file(filepath):
+    if filepath.endswith('.csv'):
+        return pd.read_csv(filepath)
+    else:
+        return pd.read_excel(filepath)
+
+def save_processed_file(processed_df, filepath):
+    processed_df.to_csv(os.path.join(os.path.dirname(filepath), 'processed_' + os.path.basename(filepath)))
+    return 'processed_' + os.path.basename(filepath)

utils/forecast_history.py

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+import pandas as pd
+import io
+from flask import send_file
+
+
+def update_forecast_history(session, train_percent, test_percent, forecast_periods, model_type, metrics,
+                            add_to_existing=False):
+    new_entry = {
+        'train_percent': train_percent * 100,
+        'test_percent': test_percent * 100,
+        'forecast_periods': forecast_periods,
+        'mae': metrics['MAE'] if metrics else None,
+        'mse': metrics['MSE'] if metrics else None,
+        'rmse': metrics['RMSE'] if metrics else None,
+        'model_type': model_type
+    }
+    forecast_history = session.get('forecast_history', [])
+    if not any(entry['train_percent'] == new_entry['train_percent'] and
+               entry['test_percent'] == new_entry['test_percent'] and
+               entry['forecast_periods'] == new_entry['forecast_periods'] and
+               entry['model_type'] == new_entry['model_type']
+               for entry in forecast_history):
+        forecast_history.append(new_entry)
+        session['forecast_history'] = forecast_history
+        if add_to_existing:
+            session['selected_indices'] = session.get('selected_indices', []) + [len(forecast_history) - 1]
+        else:
+            session['selected_indices'] = [len(forecast_history) - 1]
+        session.modified = True
+
+
+def download_forecast_history(session):
+    forecast_history = session.get('forecast_history', [])
+    if not forecast_history:
+        return None, 'No forecast history available'
+
+    # Create DataFrame for forecast history
+    df = pd.DataFrame(forecast_history)
+    df = df.rename(columns={
+        'train_percent': 'Train Percent (%)',
+        'test_percent': 'Test Percent (%)',
+        'forecast_periods': 'Forecast Periods',
+        'mae': 'MAE',
+        'mse': 'MSE',
+        'rmse': 'RMSE',
+        'model_type': 'Model'
+    })
+    df.insert(0, 'Run', range(1, len(df) + 1))
+
+    # Save to Excel
+    output = io.BytesIO()
+    df.to_excel(output, index=False)
+    output.seek(0)
+
+    return send_file(output,
+                     mimetype='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
+                     as_attachment=True,
+                     download_name='forecast_history.xlsx'), None