Create app.py

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+import pickle
+import tensorflow as tf
+
+# Load the saved models
+best_models = [
+    ("Linear Regression", joblib.load('best_Linear_Regression_model.pkl')),
+    ("Random Forest", joblib.load('best_Random_Forest_model.pkl')),
+    ("Ridge Regression", joblib.load('best_Ridge_Regression_model.pkl')),
+    ("Decision Tree", joblib.load('best_Decision_Tree_model.pkl')),
+    ("MLP", tf.keras.models.load_model('best_mlp_model.h5')),
+]
+
+# Load the saved scaler, PCA, and target scaler
+scaler = joblib.load('scaler.pkl')
+pca = joblib.load('pca.pkl')
+#----------------------------------------
+#with open("target_scaler.pkl", "rb") as f:
+   # target_scaler = pickle.load(f)
+#---------------------------------------   uncomment this statement to get scaled predidcted values
+
+
+attribute_names = [
+    'Age', 'Height', 'Weight', 'Diabetes', 'Simvastatin', 'Amiodarone',
+    'INR', 'Gender', 'Race', 'VKORC1_genotype'
+]
+
+race_columns = [
+    'Race_Asian', 'Race_Black', 'Race_Black African', 'Race_Black Caribbean',
+    'Race_Black or African American', 'Race_Black other', 'Race_Caucasian',
+    'Race_Chinese', 'Race_Han Chinese', 'Race_Hispanic', 'Race_Indian',
+    'Race_Intermediate', 'Race_Japanese', 'Race_Korean', 'Race_Malay',
+    'Race_Other', 'Race_Other (Black British)', 'Race_Other (Hungarian)',
+    'Race_Other Mixed Race', 'Race_White', 'Race_other'
+]
+
+def predict(*args):
+    user_features = list(args[:-1])
+    model_name = args[-1]
+    model = dict(best_models)[model_name]
+    
+    # Mapping categorical inputs to numerical values
+    user_features[3] = 1 if user_features[3] == 'Yes' else 0  # Diabetes
+    user_features[4] = 1 if user_features[4] == 'Yes' else 0  # Simvastatin
+    user_features[5] = 1 if user_features[5] == 'Yes' else 0  # Amiodarone
+    user_features[7] = 1 if user_features[7] == 'Male' else 0  # Gender
+    
+    # Handling one-hot encoding for 'Race'
+    race = user_features.pop(8)
+    race_encoded = [1 if col == race else 0 for col in race_columns]
+    user_features = user_features[:8] + race_encoded + user_features[8:]
+    
+    # Handling one-hot encoding for 'VKORC1_genotype'
+    vkorc1_genotype = user_features.pop(-1)
+    vkorc1_genotype_encoded = [1 if vkorc1_genotype == 'A/G' else 0, 1 if vkorc1_genotype == 'G/G' else 0]
+    user_features += vkorc1_genotype_encoded
+    
+    input_data = np.array(user_features).reshape(1, -1)
+    input_data_scaled = scaler.transform(input_data)
+    input_data_pca = pca.transform(input_data_scaled)
+
+    print("Input data PCA:", input_data_pca)  # Add this print statement
+
+    
+    if model_name == "MLP":
+        prediction = model.predict(input_data_pca).reshape(-1)
+    else:
+        prediction = model.predict(input_data_pca)
+    
+    #print("Raw prediction:", prediction)  # Add this print statement just check this line to debug whether it is giving correct prediction or not
+#------------------------------------------------------------------------
+    # Inverse transform the prediction
+    #prediction_rescaled = target_scaler.inverse_transform(prediction.reshape(-1, 1))[0][0]
+
+   # return {"Predicted Value": (prediction)} 
+
+   # prediction_rescaled = target_scaler.inverse_transform(prediction.reshape(-1, 1))[0][0]
+   # print("Prediction rescaled:", prediction_rescaled)
+   # return {"Therapeutic Dosage": f"{prediction_rescaled:.2f} mg"}
+#-----------------------------------------------------------------------------------------------   the above staments used for practice on how it will work on rescaled prediction values
+    return {"Therapeutic Dosage": f"{prediction[0]:.2f} mg"}
+
+
+    #previously it return function was not float I changed it because FastAPI can't serialize the NumPy float32 object to JSON
+    #I added float(prediction[0]) to convert the NumPy float32 value to a Python float.prediction_rescaled
+
+
+# List of model names for the dropdown menu
+model_names = [name for name, _ in best_models]
+
+# Define inputs
+age = gr.inputs.Slider(minimum=10, maximum=90, label='Age')
+height = gr.inputs.Slider(minimum=124.968, maximum=202.0, label='Height')
+weight = gr.inputs.Slider(minimum=30.0, maximum=237.7, label='Weight')
+diabetes = gr.inputs.Dropdown(choices=['Yes', 'No'], label='Diabetes')
+simvastatin = gr.inputs.Dropdown(choices=['Yes', 'No'], label='Simvastatin')
+amiodarone = gr.inputs.Dropdown(choices=['Yes', 'No'], label='Amiodarone')
+inr = gr.inputs.Slider(minimum=0.8, maximum=6.1, label='INR')
+gender = gr.inputs.Dropdown(choices=['Male', 'Female'], label='Gender')
+race = gr.inputs.Dropdown(choices=[
+    'Asian', 'Black', 'Black African',
+    'Black Caribbean', 'Black or African American',
+    'Black other', 'Caucasian', 'Chinese',
+    'Han Chinese', 'Hispanic', 'Indian', 'Intermediate',
+    'Japanese', 'Korean', 'Malay', 'Other',
+    'Other (Black British)', 'Other (Hungarian)',
+    'Other Mixed Race', 'White', 'other'
+], label='Race')
+vkorc1_genotype = gr.inputs.Dropdown(choices=['A/G', 'G/G'], label='VKORC1_genotype')
+model_name = gr.inputs.Dropdown(choices=model_names, label="Model")
+
+# Define output
+output = gr.outputs.Textbox(label="Therapeutic Dosage")
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        age, height, weight, diabetes, simvastatin, amiodarone,
+        inr, gender, race, vkorc1_genotype, model_name
+    ],
+    outputs=output ,title="Machine Learning Model",
+    description="Select a model and enter user features to predict the therapeutic dose.",
+).launch(debug=True)
+
+