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| import gradio as gr | |
| import json | |
| from itertools import product | |
| import subprocess # assuming you need this for calling run_on_scc.sh | |
| # Helper function to run the analysis | |
| def run_analysis(datasets, machines): | |
| selected_datasets = datasets # it's already a list | |
| selected_machines = machines # it's already a list | |
| if not selected_datasets: | |
| return "Please select at least one dataset." | |
| if not selected_machines: | |
| return "Please select at least one machine." | |
| configurations = [] | |
| for dataset, machine in product(selected_datasets, selected_machines): | |
| config = { | |
| "Dataset": dataset, "Binary": "True", "Image": "False", "TM_use": False, "value": "None", "Interpolation_value": "None", "Noise": False, "noise_type": False, "augmentation": False, "shear_factor": "None", "shear_prop": 0, "crop_scale_factor": "None", "crop_scale_prop": 0, "flip_code": "None", "flip_prop": 0, "rotation_angle": "None", "rotate_prop": 0, "color_number": "None", "color_prop": 0, "blur_param": "None", "blur_prop": 0, "features": "False", "glcm_distance": "None", "glcm_angle": "None", "glcm_prop": 0, "lbp_radius": "None", "lbp_prop": 0, "haralick_prop": 0, "Machine": machine, "lr": "None", "epochs": "None" | |
| } | |
| configurations.append(config) | |
| filepath = "results/models-parameters_list.txt" | |
| output_filepath = "results/evaluation_scores.txt" | |
| with open(filepath, 'w') as f: | |
| for config in configurations: | |
| f.write(json.dumps(config)) | |
| f.write("\n\n") | |
| # Call the SCC script and return computing status | |
| result = subprocess.run(["ssh", "-i", "~/.ssh/gradio_key", "[email protected]", "bash", "run_on_scc.sh"], | |
| capture_output=True, | |
| text=True) | |
| return "Computing..." | |
| # Function to update the summary textbox | |
| def update_summary(datasets, machines): | |
| # datasets and machines are lists, not dictionaries | |
| summary_text = f"Selected Datasets:\n{', '.join(datasets) if datasets else 'None'}\n\n" | |
| summary_text += f"Selected Machines:\n{', '.join(machines) if machines else 'None'}" | |
| return summary_text | |
| # Gradio components | |
| with gr.Blocks() as app: | |
| gr.Markdown("# Cancer Analysis Tool") | |
| # Tab 1: Dataset Selection | |
| with gr.Tab("Dataset"): | |
| datasets = gr.CheckboxGroup( | |
| ["Binary Colon", "Binary Lung", "Binary Prostate"], | |
| label="Select Datasets:" | |
| ) | |
| # Tab 2: Machine Selection | |
| with gr.Tab("Machines"): | |
| machines = gr.CheckboxGroup( | |
| ["Binary SVM", "Binary KNN", "Binary Deepnet"], | |
| label="Select Machines:" | |
| ) | |
| # Tab 3: Summary and Analysis | |
| with gr.Tab("Analysis"): | |
| summary = gr.Textbox(label="Summary of Selections", interactive=False, lines=5) | |
| run_button = gr.Button("Run Analysis") | |
| result = gr.Textbox(label="Result", interactive=False) | |
| # Trigger analysis | |
| run_button.click( | |
| run_analysis, | |
| inputs=[datasets, machines], | |
| outputs=result | |
| ) | |
| # Update summary when selections change | |
| datasets.change(update_summary, [datasets, machines], summary) | |
| machines.change(update_summary, [datasets, machines], summary) | |
| # Launch the Gradio interface | |
| app.launch(share=True) | |