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| import gradio as gr | |
| from pipeline_classes import CreateCombinedDataFrame, ScaleXYZData, ExtractFeatures, TrainModel, ClassifyMovementData, LowPassFilter, PCAHandler | |
| from sklearn.pipeline import Pipeline | |
| #from _config import config | |
| import pandas as pd | |
| import numpy as np | |
| import joblib | |
| import json | |
| # Define pipelines | |
| combining_dataframes_pipeline = Pipeline([ | |
| #('import_data', ImportData(use_accel=True, use_reports=True, use_combined=False, use_features=False)), | |
| ('create_combined_dataframe', CreateCombinedDataFrame(time_window=None, label_columns=None)), | |
| ]) | |
| feature_extraction_pipeline = Pipeline([ | |
| #('import_data', ImportData(use_accel=False, use_reports=False, use_combined=True, use_features=False)), | |
| ('low_pass_filter', LowPassFilter(cutoff_frequency=None, sampling_rate=None, order=None)), | |
| ('scale_xyz_data', ScaleXYZData(scaler_type=None)), | |
| ('extract_features', ExtractFeatures(window_length=None, | |
| window_step_size=None, | |
| data_frequency=None, | |
| selected_domains=None, | |
| include_magnitude=None, | |
| features_label_columns=None)), | |
| ]) | |
| training_model_pipeline = Pipeline([ | |
| #('import_data', ImportData(use_accel=False, use_reports=False, use_combined=False, use_features=True)), | |
| ('pca_handler', PCAHandler(apply_pca=None, variance=None)), | |
| ('train_model', TrainModel(classifier=None, train_label= None, target=None)), | |
| ]) | |
| analyzing_data_pipeline = Pipeline([ | |
| #('import_data', ImportData(use_accel=True, use_reports=False, use_combined=False, use_features=False)), | |
| ('low_pass_filter', LowPassFilter(cutoff_frequency=None, sampling_rate=None, order=None)), | |
| ('scale_xyz_data', ScaleXYZData(scaler_type=None)), | |
| ('extract_features', ExtractFeatures(window_length=None, | |
| window_step_size=None, | |
| data_frequency=None, | |
| selected_domains=None, | |
| include_magnitude=None, | |
| features_label_columns=None)), | |
| ('classify_movement_data', ClassifyMovementData(model_file=None)), | |
| ]) | |
| complete_training_model_pipeline = Pipeline([ | |
| #('import_data', ImportData(use_accel=True, use_reports=True, use_combined=False, use_features=False)), | |
| ('create_combined_dataframe', CreateCombinedDataFrame(time_window=None, label_columns=None)), | |
| ('low_pass_filter', LowPassFilter(cutoff_frequency=None, sampling_rate=None, order=None)), | |
| ('scale_xyz_data', ScaleXYZData(scaler_type=None)), | |
| ('extract_features', ExtractFeatures(window_length=None, | |
| window_step_size=None, | |
| data_frequency=None, | |
| selected_domains=None, | |
| include_magnitude=None, | |
| features_label_columns=None)), | |
| ('pca_handler', PCAHandler(apply_pca=None, variance=None)), | |
| ('train_model', TrainModel(classifier=None, train_label= None, target=None)), | |
| ]) | |
| def execute_combine_pipeline(accel_file, report_file, | |
| time_window=None, label_columns=None | |
| ): | |
| try: | |
| # Load data files only if paths are valid | |
| accel_data = pd.read_csv(accel_file) if accel_file else None | |
| report_data = pd.read_csv(report_file) if report_file else None | |
| # Validate inputs for the selected pipeline | |
| if accel_data is None or report_data is None: | |
| return "Error: Both accelerometer and self-report data files are required for this pipeline.", None | |
| combining_dataframes_pipeline.set_params( | |
| create_combined_dataframe__time_window=time_window, | |
| create_combined_dataframe__label_columns=label_columns.split(',')) | |
| X = report_data, accel_data | |
| result = combining_dataframes_pipeline.fit_transform(X) | |
| output_file = "combine_dataframes_output.csv" | |
| result.to_csv(output_file, index=False) | |
| return output_file | |
| except Exception as e: | |
| print(f"Error occurred: {str(e)}") | |
| return str(e), None | |
| def execute_feature_extraction_pipeline(combined_file, cutoff_frequency, order, scaler_type, window_length, window_step_size, data_frequency, include_magnitude, features_label_columns): | |
| try: | |
| combined_data = pd.read_csv(combined_file) if combined_file else None | |
| if combined_data is None: | |
| return "Error: Combined data file is required for this pipeline.", None | |
| feature_extraction_pipeline.set_params( | |
| low_pass_filter__cutoff_frequency=cutoff_frequency, | |
| low_pass_filter__order=order, | |
| low_pass_filter__sampling_rate=data_frequency, | |
| scale_xyz_data__scaler_type=scaler_type, | |
| extract_features__window_length=window_length, | |
| extract_features__window_step_size=window_step_size, | |
| extract_features__data_frequency=data_frequency, | |
| #extract_features__selected_domains=None, | |
| extract_features__include_magnitude=include_magnitude, | |
| extract_features__features_label_columns=features_label_columns.split(',')) | |
| result = feature_extraction_pipeline.fit_transform(combined_data) | |
| output_file = "extract_features_output.csv" | |
| result.to_csv(output_file, index=False) | |
| return output_file | |
| except Exception as e: | |
| print(f"Error occurred: {str(e)}") | |
| return str(e) | |
| def execute_training_pipeline(features_file, apply_pca, pca_variance, classifier, train_label, target): | |
| try: | |
| print(f"features_file: {features_file}") | |
| features_data = pd.read_csv(features_file) if features_file else None | |
| if features_data is None: | |
| return "Error: Features data file is required for this pipeline.", None | |
| training_model_pipeline.set_params( | |
| pca_handler__apply_pca=apply_pca, | |
| pca_handler__variance=pca_variance, | |
| train_model__classifier=classifier, | |
| train_model__train_label=train_label, | |
| train_model__target=target) | |
| X = features_data | |
| training_model_pipeline.fit(X) | |
| output_file, secondary_output_file = training_model_pipeline.named_steps['train_model'].get_output_files() | |
| return output_file, secondary_output_file | |
| except Exception as e: | |
| print(f"Error occurred: {str(e)}") | |
| return str(e), None | |
| def execute_analyze_pipeline(accel_file, model_file, cutoff_frequency, order, scaler_type, window_length, data_frequency, include_magnitude, features_label_columns): | |
| try: | |
| accel_data = pd.read_csv(accel_file) if accel_file else None | |
| if accel_data is None: | |
| return "Error: Accelerometer data file is required for this pipeline.", None | |
| analyzing_data_pipeline.set_params( | |
| low_pass_filter__cutoff_frequency=cutoff_frequency, | |
| low_pass_filter__order=order, | |
| low_pass_filter__sampling_rate=data_frequency, | |
| scale_xyz_data__scaler_type=scaler_type, | |
| extract_features__window_length=window_length, | |
| extract_features__window_step_size=window_length, | |
| extract_features__data_frequency=data_frequency, | |
| #extract_features__selected_domains=None, | |
| extract_features__include_magnitude=include_magnitude, | |
| extract_features__features_label_columns=features_label_columns.split(','), | |
| classify_movement_data__model_file=model_file.name | |
| ) | |
| result = analyzing_data_pipeline.fit_transform(accel_data) | |
| output_file = "analyze_data_output.csv" | |
| result.to_csv(output_file, index=False) | |
| return output_file | |
| except Exception as e: | |
| print(f"Error occurred: {str(e)}") | |
| return str(e), None | |
| def execute_complete_training_pipeline(accel_file, report_file, time_window, label_columns, | |
| cutoff_frequency, order, scaler_type, window_length, window_step_size, data_frequency, include_magnitude, features_label_columns, | |
| apply_pca, pca_variance, classifier, train_label, target): | |
| try: | |
| accel_data = pd.read_csv(accel_file) if accel_file else None | |
| report_data = pd.read_csv(report_file) if report_file else None | |
| if accel_data is None or report_data is None: | |
| return "Error: Both accelerometer and self-report data files are required for this pipeline.", None | |
| complete_training_model_pipeline.set_params( | |
| create_combined_dataframe__time_window=time_window, | |
| create_combined_dataframe__label_columns=label_columns.split(','), | |
| low_pass_filter__cutoff_frequency=cutoff_frequency, | |
| low_pass_filter__order=order, | |
| low_pass_filter__sampling_rate=data_frequency, | |
| scale_xyz_data__scaler_type=scaler_type, | |
| extract_features__window_length=window_length, | |
| extract_features__window_step_size=window_step_size, | |
| extract_features__data_frequency=data_frequency, | |
| #extract_features__selected_domains=None, | |
| extract_features__include_magnitude=include_magnitude, | |
| extract_features__features_label_columns=label_columns.split(','), | |
| pca_handler__apply_pca=apply_pca, | |
| pca_handler__variance=pca_variance, | |
| train_model__classifier=classifier, | |
| train_model__train_label=label_columns, | |
| train_model__target=target | |
| ) | |
| X = report_data, accel_data | |
| complete_training_model_pipeline.fit(X) | |
| output_file, secondary_output_file = complete_training_model_pipeline.named_steps['train_model'].get_output_files() | |
| return output_file, secondary_output_file | |
| except Exception as e: | |
| print(f"Error occurred: {str(e)}") | |
| return str(e), None | |
| # Gradio Blocks Interface | |
| with gr.Blocks() as demo: | |
| with gr.Tabs(): | |
| with gr.TabItem("Combine DataFrames"): | |
| accel_file = gr.File(label="Upload Accelerometer Data") | |
| report_file = gr.File(label="Upload Self-Report Data") | |
| time_window = gr.Number(label="Time Window (minutes)", value=2) | |
| label_columns = gr.Textbox(label="Label Columns (comma-separated)", value="valence,arousal") | |
| combine_button = gr.Button("Combine DataFrames") | |
| combine_output = gr.File(label="Download Combined DataFrame") | |
| def combine_dataframes(accel_file, report_file, time_window, label_columns): | |
| output_file = execute_combine_pipeline(accel_file, report_file, time_window, label_columns) | |
| return output_file | |
| combine_button.click(combine_dataframes, inputs=[accel_file, report_file, time_window, label_columns], outputs=combine_output) | |
| with gr.TabItem("Extract Features"): | |
| combined_file = gr.File(label="Upload Combined Data") | |
| cutoff_frequency = gr.Number(label="Cutoff Frequency (Hz)", value=10) | |
| order = gr.Number(label="Order", value=4) | |
| scaler_type = gr.Radio(label="Scaler Type", choices=["standard", "minmax"]) | |
| window_length = gr.Number(label="Window Length (seconds)", value=60) | |
| window_step_size = gr.Number(label="Window Step Size (seconds)", value=20) | |
| data_frequency = gr.Number(label="Data Frequency (Hz)", value=25) | |
| #selected_domains= gr.Textbox(label="Only these domains (Comma-Seperated) / If you want all then leave out", value=None) | |
| include_magnitude= gr.Checkbox(label="Include Magnitude", value=True) | |
| features_label_columns= gr.Textbox(label="Label Columns (comma-separated)", value="valence,arousal") | |
| extract_button = gr.Button("Extract Features") | |
| extract_output = gr.File(label="Download Extracted Features") | |
| def extract_features(combined_file, cutoff_frequency, order, scaler_type, window_length, window_step_size, data_frequency, include_magnitude, features_label_columns): | |
| output_file = execute_feature_extraction_pipeline(combined_file, | |
| cutoff_frequency, order, scaler_type, window_length, window_step_size, data_frequency, | |
| include_magnitude, features_label_columns | |
| ) | |
| return output_file | |
| extract_button.click(extract_features, inputs=[combined_file, cutoff_frequency, order, scaler_type, window_length, window_step_size, | |
| data_frequency, include_magnitude, features_label_columns], outputs=extract_output) | |
| with gr.TabItem("Train Model"): | |
| features_file = gr.File(label="Upload Features Data") | |
| apply_pca = gr.Checkbox(label="Apply PCA", value=False) | |
| pca_variance = gr.Number(label="PCA Variance", value=0.95) | |
| classifier = gr.Dropdown(label="Classifier", choices=["xgboost", "svm", "randomforest"], value="xgboost") | |
| train_label = gr.Textbox(label="Label Columns (comma-separated)", value="valence,arousal") | |
| target = gr.Textbox(label="Target Label", value="arousal") | |
| train_button = gr.Button("Train Model") | |
| train_output_json = gr.File(label="Download Model PKL") | |
| train_output_pkl = gr.File(label="Download Model JSON") | |
| def train_model(features_file, apply_pca, pca_variance, classifier, train_label, target): | |
| output_file, secondary_output_file = execute_training_pipeline(features_file, apply_pca, pca_variance, classifier, train_label, target) | |
| return output_file, secondary_output_file | |
| train_button.click(train_model, inputs=[features_file, apply_pca, pca_variance, classifier, train_label, target], outputs=[train_output_json, train_output_pkl]) | |
| with gr.TabItem("Analyze Data"): | |
| accel_file = gr.File(label="Upload Accelerometer Data") | |
| model_file = gr.File(label="Upload Model") | |
| cutoff_frequency = gr.Number(label="Cutoff Frequency (Hz)", value=10) | |
| order = gr.Number(label="Order", value=4) | |
| scaler_type = gr.Radio(label="Scaler Type", choices=["standard", "minmax"]) | |
| window_length = gr.Number(label="Window Length (seconds)", value=60) | |
| data_frequency = gr.Number(label="Data Frequency (Hz)", value=25) | |
| #selected_domains= gr.Textbox(label="Only these domains (Comma-Seperated) / If you want all then leave out", value=None) | |
| include_magnitude= gr.Checkbox(label="Include Magnitude", value=True) | |
| features_label_columns= gr.Textbox(label="Label Columns (comma-separated)", value="valence,arousal") | |
| analyze_button = gr.Button("Analyze Data") | |
| analyze_output = gr.File(label="Download Analyzed Data") | |
| def analyze_data(accel_file, model_file, cutoff_frequency, order, scaler_type, window_length, data_frequency, include_magnitude, features_label_columns): | |
| output_file = execute_analyze_pipeline(accel_file, model_file, cutoff_frequency, order, scaler_type, window_length, | |
| data_frequency, include_magnitude, features_label_columns) | |
| return output_file | |
| analyze_button.click(analyze_data, inputs=[accel_file, model_file, cutoff_frequency, order, scaler_type, window_length, | |
| data_frequency, include_magnitude, features_label_columns ], outputs=analyze_output) | |
| with gr.TabItem("Complete Train Model"): | |
| accel_file = gr.File(label="Upload Accelerometer Data") | |
| report_file = gr.File(label="Upload Self-Report Data") | |
| time_window = gr.Number(label="Time Window (minutes)", value=2) | |
| label_columns = gr.Textbox(label="Label Columns (comma-separated)", value="valence,arousal") | |
| cutoff_frequency = gr.Number(label="Cutoff Frequency (Hz)", value=10) | |
| order = gr.Number(label="Order", value=4) | |
| scaler_type = gr.Radio(label="Scaler Type", choices=["standard", "minmax"]) | |
| window_length = gr.Number(label="Window Length (seconds)", value=60) | |
| window_step_size = gr.Number(label="Window Step Size (seconds)", value=20) | |
| data_frequency = gr.Number(label="Data Frequency (Hz)", value=25) | |
| include_magnitude= gr.Checkbox(label="Include Magnitude", value=True) | |
| #features_label_columns= gr.Textbox(label="Label Columns (comma-separated)", value="valence,arousal") | |
| apply_pca = gr.Checkbox(label="Apply PCA", value=False) | |
| pca_variance = gr.Number(label="PCA Variance", value=0.95) | |
| classifier = gr.Dropdown(label="Classifier", choices=["xgboost", "svm", "randomforest"], value="xgboost") | |
| #train_label = gr.Textbox(label="Label Columns (comma-separated)", value="valence,arousal") | |
| target = gr.Textbox(label="Target Label", value="arousal") | |
| complete_train_button = gr.Button("Complete Train Model") | |
| complete_train_output_pkl = gr.File(label="Download Model PKL") | |
| complete_train_output_json = gr.File(label="Download Model JSON") | |
| def complete_train_model(accel_file, report_file, time_window, label_columns, | |
| cutoff_frequency, order, scaler_type, window_length, window_step_size, data_frequency, include_magnitude, features_label_columns, | |
| apply_pca, pca_variance, classifier, train_label, target): | |
| output_file, secondary_output_file = execute_complete_training_pipeline(accel_file, report_file, time_window, label_columns, | |
| cutoff_frequency, order, scaler_type, window_length, window_step_size, data_frequency, include_magnitude, features_label_columns, | |
| apply_pca, pca_variance, classifier, train_label, target) | |
| return output_file, secondary_output_file | |
| complete_train_button.click(complete_train_model, inputs=[accel_file, report_file, time_window, label_columns, | |
| cutoff_frequency, order, scaler_type, window_length, window_step_size, data_frequency, include_magnitude, features_label_columns, | |
| apply_pca, pca_variance, classifier, train_label, target], outputs=[complete_train_output_pkl, complete_train_output_json]) | |
| demo.launch() |