Upload app.py

app.py

@@ -1,115 +1,120 @@
-import gradio as gr
-from model_utils import load_all_models, predict_with_model
-
-# Load all models
-models, model_features = load_all_models()
-
-# Mapeo de nombres amigables a nombres reales
-MODEL_MAPPING = {
-    "Death": "Death_random_forest_model",
-    "Binary diagnosis": "Binary diagnosis_random_forest_model",
-    "Necessity of transplantation": "Necessity of transplantation_random_forest_model",
-    "Progressive disease": "Progressive disease_random_forest_model"
-}
-
-# Invertir el mapeo (opcional para facilidad)
-INVERSE_MODEL_MAPPING = {v: k for k, v in MODEL_MAPPING.items()}
-
-# Feature sets for each target variable
-FEATURES = {
-    "Death": [
-        'Pedigree', 'Age at diagnosis', 'FVC (L) at diagnosis',
-        'FVC (%) at diagnosis', 'DLCO (%) at diagnosis', 'RadioWorsening2y',
-        'Severity of telomere shortening - Transform 4', 'Progressive disease'
-    ],
-    "Binary diagnosis": [
-        'Pedigree', 'Age at diagnosis', 'Antifibrotic Drug',
-        'Prednisone', 'Mycophenolate', 'FVC (L) at diagnosis',
-        'FVC (%) at diagnosis', 'DLCO (%) at diagnosis'
-    ],
-    "Necessity of transplantation": [
-        'Pedigree','Age at diagnosis','FVC (L) at diagnosis', 'FVC (%) at diagnosis', 'DLCO (%) at diagnosis',
-        'FVC (L) 1 year after diagnosis','FVC (%) 1 year after diagnosis','DLCO (%) 1 year after diagnosis', 
-        'RadioWorsening2y'
-    ],
-    "Progressive disease": [
-        'Pedigree', 'Age at diagnosis', 'FVC (L) at diagnosis','FVC (%) at diagnosis', 'DLCO (%) at diagnosis','FVC (L) 1 year after diagnosis',
-        'FVC (%) 1 year after diagnosis', 'DLCO (%) 1 year after diagnosis',
-        'RadioWorsening2y', 'Genetic mutation studied in patient'
-    ]
-    
-}
-
-FEATURE_RANGES = {
-    'Pedigree': (0, 67),
-    'Age at diagnosis': (0, 200),
-    'FVC (L) at diagnosis': (0.0, 5.0),
-    'FVC (%) at diagnosis': (0.0, 200.0),
-    'DLCO (%) at diagnosis': (0.0, 200.0),
-    'RadioWorsening2y': (0, 3),
-    'Severity of telomere shortening - Transform 4': (1, 6),
-    'Progressive disease': (0, 1),
-    'Antifibrotic Drug': (0, 1),
-    'Prednisone': (0, 1),
-    'Mycophenolate': (0, 1),
-    'FVC (L) 1 year after diagnosis': (0.0, 5.0),
-    'FVC (%) 1 year after diagnosis': (0.0, 200.0),
-    'DLCO (%) 1 year after diagnosis': (0.0, 200.0),
-    'Genetic mutation studied in patient': (0, 1),
-    'Comorbidities': (0, 1)
-}
-
-
-# Define prediction function
-def make_prediction(input_features, friendly_model_name):
-    """
-    Predict using the selected model and input features.
-    """
-    # Map the friendly model name to the real model name
-    target_model = MODEL_MAPPING.get(friendly_model_name)
-    if target_model not in models:
-        return f"Model '{friendly_model_name}' not found. Please select a valid model."
-
-    model = models[target_model]
-    features = model_features[target_model]
-
-    if len(input_features) != len(features):
-        return f"Invalid input. Expected features: {features}"
-
-    input_array = [float(x) for x in input_features]
-    prediction = predict_with_model(model, input_array)
-    return f"Prediction for {friendly_model_name}: {prediction}"
-
-# Define Gradio interface
-def gradio_interface():
-    def create_inputs_for_features(features):
-        inputs = []
-        for feature in features:
-            min_val, max_val = FEATURE_RANGES.get(feature, (None, None))
-            inputs.append(gr.Number(label=f"{feature} (Range: {min_val} - {max_val})", minimum=min_val, maximum=max_val))
-        return inputs
-
-    # Create a separate interface for each target variable
-    interfaces = []
-    for target, features in FEATURES.items():
-        inputs = create_inputs_for_features(features)
-        interface = gr.Interface(
-            fn=lambda *args, target=target: make_prediction(args, target),
-            inputs=inputs,
-            outputs=gr.Text(label="Prediction Result"),
-            title=f"Prediction for {target}",
-            description=f"Provide values for features relevant to {target}"
-        )
-        interfaces.append(interface)
-
-    # Combine all interfaces into a tabbed layout
-    tabbed_interface = gr.TabbedInterface(
-        interface_list=interfaces,
-        tab_names=list(FEATURES.keys())
-    )
-    return tabbed_interface
-
-# Launch Gradio app
-if __name__ == "__main__":
-    interface = gradio_interface()
-    interface.launch()
+import gradio as gr
+from model_utils import load_all_models, predict_with_model
+
+# Load all models
+models, model_features = load_all_models()
+
+# Mapeo de nombres amigables a nombres reales
+MODEL_MAPPING = {
+    "Death": "Death_random_forest_model",
+    "Binary diagnosis": "Binary diagnosis_random_forest_model",
+    "Necessity of transplantation": "Necessity of transplantation_random_forest_model",
+    "Progressive disease": "Progressive disease_random_forest_model"
+}
+
+# Invertir el mapeo (opcional para facilidad)
+INVERSE_MODEL_MAPPING = {v: k for k, v in MODEL_MAPPING.items()}
+
+# Feature sets for each target variable
+FEATURES = {
+    "Death": [
+        'ProgressiveDisease', 'DLCO (%) at diagnosis', 'FVC (%) at diagnosis',
+        'FVC (L) at diagnosis', 'RadioWorsening2y', 'Age at diagnosis',
+        'Pedigree', 'Severity of telomere shortening - Transform 4'
+    ],
+    "Binary diagnosis": [
+        'Age at diagnosis', 'Pedigree', 'DLCO (%) at diagnosis', 'TOBACCO',
+        'FVC (%) at diagnosis', 'FVC (L) at diagnosis',
+        'Multidisciplinary committee', 'Severity of telomere shortening - Transform 4',
+        'Severity of telomere shortening', 'Biopsy'
+    ],
+    "Necessity of transplantation": [
+        'RadioWorsening2y', 'FVC (%) 1 year after diagnosis',
+        'Genetic mutation studied in patient', 'Comorbidities',
+        'DLCO (%) 1 year after diagnosis', 'FVC (L) at diagnosis',
+        'DLCO (%) at diagnosis', 'Biopsy',
+        'Severity of telomere shortening - Transform 4', 'FVC (%) at diagnosis',
+        'FVC (L) 1 year after diagnosis'
+    ],
+    "Progressive disease": [
+        'DLCO (%) at diagnosis', 'Age at diagnosis', 'Pedigree',
+        '1st degree relative', 'FVC (L) at diagnosis', 'FVC (%) at diagnosis',
+        'Biopsy', 'Genetic mutation studied in patient',
+        'Severity of telomere shortening - Transform 4', 'Severity of telomere shortening'
+    ]
+}
+
+FEATURE_RANGES = {
+    'Pedigree': (0, 67),
+    'Age at diagnosis': (0, 200),
+    'FVC (L) at diagnosis': (0.0, 5.0),
+    'FVC (%) at diagnosis': (0.0, 200.0),
+    'DLCO (%) at diagnosis': (0.0, 200.0),
+    'RadioWorsening2y': (0, 3),
+    'Severity of telomere shortening - Transform 4': (1, 6),
+    'ProgressiveDisease': (0, 1),
+    'TOBACCO': (0, 1),
+    'Multidisciplinary committee': (0, 1),
+    'Biopsy': (0, 1),
+    'Genetic mutation studied in patient': (0, 1),
+    'Comorbidities': (0, 1),
+    'FVC (L) 1 year after diagnosis': (0.0, 5.0),
+    'FVC (%) 1 year after diagnosis': (0.0, 200.0),
+    'DLCO (%) 1 year after diagnosis': (0.0, 200.0),
+    '1st degree relative': (0, 1),
+    'Severity of telomere shortening': (1, 6)
+}
+
+# Define prediction function
+def make_prediction(input_features, friendly_model_name):
+    """
+    Predict using the selected model and input features.
+    """
+    # Map the friendly model name to the real model name
+    target_model = MODEL_MAPPING.get(friendly_model_name)
+    if target_model not in models:
+        return f"Model '{friendly_model_name}' not found. Please select a valid model."
+
+    model = models[target_model]
+    features = model_features[target_model]
+
+    if len(input_features) != len(features):
+        return f"Invalid input. Expected features: {features}"
+
+    input_array = [float(x) for x in input_features]
+    prediction = predict_with_model(model, input_array)
+    return f"Prediction for {friendly_model_name}: {prediction}"
+
+# Define Gradio interface
+def gradio_interface():
+    def create_inputs_for_features(features):
+        inputs = []
+        for feature in features:
+            min_val, max_val = FEATURE_RANGES.get(feature, (None, None))
+            inputs.append(gr.Number(label=f"{feature} (Range: {min_val} - {max_val})", minimum=min_val, maximum=max_val))
+        return inputs
+
+    # Create a separate interface for each target variable
+    interfaces = []
+    for target, features in FEATURES.items():
+        inputs = create_inputs_for_features(features)
+        interface = gr.Interface(
+            fn=lambda *args, target=target: make_prediction(args, target),
+            inputs=inputs,
+            outputs=gr.Text(label="Prediction Result"),
+            title=f"Prediction for {target}",
+            description=f"Provide values for features relevant to {target}"
+        )
+        interfaces.append(interface)
+
+    # Combine all interfaces into a tabbed layout
+    tabbed_interface = gr.TabbedInterface(
+        interface_list=interfaces,
+        tab_names=list(FEATURES.keys())
+    )
+    return tabbed_interface
+
+# Launch Gradio app
+if __name__ == "__main__":
+    interface = gradio_interface()
+    interface.launch()