| import gradio as gr
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| from model_utils import load_all_models, predict_with_model
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|
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| models, model_features = load_all_models()
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|
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|
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| MODEL_MAPPING = {
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| "Death": "Death_random_forest_model",
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| "Binary diagnosis": "Binary diagnosis_random_forest_model",
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| "Necessity of transplantation": "Necessity of transplantation_random_forest_model",
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| "Progressive disease": "Progressive disease_random_forest_model"
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| }
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| INVERSE_MODEL_MAPPING = {v: k for k, v in MODEL_MAPPING.items()}
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|
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| FEATURES = {
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| "Death": [
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| 'Pedigree', 'Age at diagnosis', 'FVC (L) at diagnosis',
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| 'FVC (%) at diagnosis', 'DLCO (%) at diagnosis', 'RadioWorsening2y',
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| 'Severity of telomere shortening - Transform 4', 'Progressive disease'
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| ],
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| "Binary diagnosis": [
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| 'Pedigree', 'Age at diagnosis', 'Antifibrotic Drug',
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| 'Prednisone', 'Mycophenolate', 'FVC (L) at diagnosis',
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| 'FVC (%) at diagnosis', 'DLCO (%) at diagnosis'
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| ],
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| "Necessity of transplantation": [
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| 'Pedigree','Age at diagnosis','FVC (L) at diagnosis', 'FVC (%) at diagnosis', 'DLCO (%) at diagnosis',
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| 'FVC (L) 1 year after diagnosis','FVC (%) 1 year after diagnosis','DLCO (%) 1 year after diagnosis',
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| 'RadioWorsening2y'
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| ],
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| "Progressive disease": [
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| 'Pedigree', 'Age at diagnosis', 'FVC (L) at diagnosis','FVC (%) at diagnosis', 'DLCO (%) at diagnosis','FVC (L) 1 year after diagnosis',
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| 'FVC (%) 1 year after diagnosis', 'DLCO (%) 1 year after diagnosis',
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| 'RadioWorsening2y', 'Genetic mutation studied in patient'
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| ]
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|
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| }
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| FEATURE_RANGES = {
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| 'Pedigree': (0, 67),
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| 'Age at diagnosis': (0, 200),
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| 'FVC (L) at diagnosis': (0.0, 5.0),
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| 'FVC (%) at diagnosis': (0.0, 200.0),
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| 'DLCO (%) at diagnosis': (0.0, 200.0),
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| 'RadioWorsening2y': (0, 3),
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| 'Severity of telomere shortening - Transform 4': (1, 6),
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| 'Progressive disease': (0, 1),
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| 'Antifibrotic Drug': (0, 1),
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| 'Prednisone': (0, 1),
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| 'Mycophenolate': (0, 1),
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| 'FVC (L) 1 year after diagnosis': (0.0, 5.0),
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| 'FVC (%) 1 year after diagnosis': (0.0, 200.0),
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| 'DLCO (%) 1 year after diagnosis': (0.0, 200.0),
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| 'Genetic mutation studied in patient': (0, 1),
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| 'Comorbidities': (0, 1)
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| }
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| def make_prediction(input_features, friendly_model_name):
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| """
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| Predict using the selected model and input features.
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| """
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|
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| target_model = MODEL_MAPPING.get(friendly_model_name)
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| if target_model not in models:
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| return f"Model '{friendly_model_name}' not found. Please select a valid model."
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|
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| model = models[target_model]
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| features = model_features[target_model]
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|
|
| if len(input_features) != len(features):
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| return f"Invalid input. Expected features: {features}"
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|
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| input_array = [float(x) for x in input_features]
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| prediction = predict_with_model(model, input_array)
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| return f"Prediction for {friendly_model_name}: {prediction}"
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|
|
|
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| def gradio_interface():
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| def create_inputs_for_features(features):
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| inputs = []
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| for feature in features:
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| min_val, max_val = FEATURE_RANGES.get(feature, (None, None))
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| inputs.append(gr.Number(label=f"{feature} (Range: {min_val} - {max_val})", minimum=min_val, maximum=max_val))
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| return inputs
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| interfaces = []
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| for target, features in FEATURES.items():
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| inputs = create_inputs_for_features(features)
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| interface = gr.Interface(
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| fn=lambda *args, target=target: make_prediction(args, target),
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| inputs=inputs,
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| outputs=gr.Text(label="Prediction Result"),
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| title=f"Prediction for {target}",
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| description=f"Provide values for features relevant to {target}"
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| )
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| interfaces.append(interface)
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|
|
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| tabbed_interface = gr.TabbedInterface(
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| interface_list=interfaces,
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| tab_names=list(FEATURES.keys())
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| )
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| return tabbed_interface
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|
|
|
|
| if __name__ == "__main__":
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| interface = gradio_interface()
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| interface.launch()
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|
|
