Create app.py

app.py

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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(["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)
+        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)