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app.py

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+import pandas as pd
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.neighbors import NearestNeighbors
+import gradio as gr
+
+# --- 1. MODEL AND DATA LOADING ---
+# Load the dataset from the CSV file
+df = pd.read_csv('data.csv')
+
+# Define the columns for features and parameters
+FEATURE_COLS = ['Thickness', 'Age', 'Diameter', 'Sex', 'Valve_phenotype', 'Region']
+PARAM_COLS = ['mu', 'k1', 'k2', 'k_op', 'k_ip1', 'k_ip2', 'alpha1', 'alpha2']
+NUMERIC_FEATURES = ['Thickness', 'Age', 'Diameter']
+CATEGORICAL_FEATURES = ['Sex', 'Valve_phenotype', 'Region']
+
+# Separate features (X)
+X = df[FEATURE_COLS]
+
+# Create and fit the preprocessor
+# This learns how to scale numbers and encode categories from your full dataset
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', StandardScaler(), NUMERIC_FEATURES),
+        ('cat', OneHotEncoder(handle_unknown='ignore'), CATEGORICAL_FEATURES)
+    ])
+preprocessor.fit(X)
+
+# Transform the data using the fitted preprocessor
+X_processed = preprocessor.transform(X)
+
+# Define and train the k-Nearest Neighbors model
+knn = NearestNeighbors(n_neighbors=1, metric='euclidean')
+knn.fit(X_processed)
+
+print("Model initialized successfully!")
+
+# --- 2. THE PREDICTION FUNCTION ---
+# This function will be called by the Gradio interface
+def find_closest_patient(thickness, age, diameter, sex, valve, region):
+    """
+    Takes new patient data from the UI, finds the closest match,
+    and returns the results as formatted strings.
+    """
+    # Create a DataFrame from the user's input
+    new_patient_df = pd.DataFrame([{
+        'Thickness': thickness,
+        'Age': age,
+        'Diameter': diameter,
+        'Sex': sex,
+        'Valve_phenotype': valve,
+        'Region': region
+    }])[FEATURE_COLS]
+
+    # Preprocess the new data
+    new_patient_processed = preprocessor.transform(new_patient_df)
+
+    # Find the nearest neighbor's index
+    distances, indices = knn.kneighbors(new_patient_processed)
+    closest_index = indices[0][0]
+
+    # Get the info and parameters of the closest match
+    closest_patient_info = df.loc[closest_index, FEATURE_COLS].to_dict()
+    closest_patient_params = df.loc[closest_index, PARAM_COLS].to_dict()
+    
+    # Format the output nicely for display
+    info_str = "\n".join([f"{key}: {value}" for key, value in closest_patient_info.items()])
+    params_str = "\n".join([f"{key}: {value}" for key, value in closest_patient_params.items()])
+
+    return info_str, params_str
+
+# --- 3. THE GRADIO WEB INTERFACE ---
+# We define the user interface here
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# Constitutive Parameter Estimator for ATAA")
+    gr.Markdown("Enter the clinical information for a patient to find the constitutive parameters from the most similar case in the dataset.")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Patient Clinical Information")
+            thickness_input = gr.Slider(minimum=1.0, maximum=4.0, value=2.4, step=0.01, label="Thickness (mm)")
+            age_input = gr.Slider(minimum=20, maximum=90, value=40, step=1, label="Age (years)")
+            diameter_input = gr.Slider(minimum=40.0, maximum=65.0, value=53.0, step=0.1, label="Diameter (mm)")
+            sex_input = gr.Radio(["M", "F"], label="Sex", value="F")
+            # The choices list is dynamically created from your CSV file's unique values
+            valve_input = gr.Dropdown(choices=df['Valve_phenotype'].unique().tolist(), label="Valve Phenotype", value="BAV")
+            region_input = gr.Dropdown(choices=df['Region'].unique().tolist(), label="Aortic Region", value="Anterior")
+            
+            submit_button = gr.Button("Find Parameters", variant="primary")
+            
+        with gr.Column():
+            gr.Markdown("### Results")
+            gr.Markdown("#### Closest Match Found in Dataset:")
+            output_info = gr.Textbox(label="Clinical Info of Match", lines=6)
+            gr.Markdown("#### Constitutive Parameters:")
+            output_params = gr.Textbox(label="Parameters", lines=8)
+
+    # Define what happens when the button is clicked
+    submit_button.click(
+        fn=find_closest_patient,
+        inputs=[thickness_input, age_input, diameter_input, sex_input, valve_input, region_input],
+        outputs=[output_info, output_params]
+    )
+    
+    gr.Markdown("---")
+    gr.Markdown("### References\nFor methodology, please refer to: [Your Paper Citation Here]")
+
+
+# Launch the web application
+demo.launch()