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| import gradio as gr | |
| from joblib import load | |
| import numpy as np | |
| import pandas as pd | |
| # Load your saved models | |
| rf = load('best_random_forest_model.joblib') | |
| dt = load('best_decision_tree_model.joblib') | |
| mlp = load('best_MLP_classifier_model.joblib') | |
| knn = load('best_knn_model.joblib') | |
| # Class names | |
| class_names = ["High Therapeutic Dose of Warfarin Required","Low Therapeutic Dose of Warfarin Required"] | |
| # Load training data for expected feature names | |
| training_data = pd.read_csv('dataset_train.csv') | |
| # Drop the 'Unnamed: 0' column if it exists | |
| if 'Unnamed: 0' in training_data.columns: | |
| training_data = training_data.drop(columns=['Unnamed: 0']) | |
| expected_feature_names = training_data.columns.tolist() | |
| # Define the prediction function | |
| def predict_warfarin_dose(gender, race, age, height, weight, diabetes, simvastatin, amiodarone, genotype, inr, algorithm): | |
| # Decode the encoded values | |
| gender = "Male" if gender == 1 else "Female" | |
| race = race_dict_inverse[race] | |
| age = age_dict_inverse[age] | |
| genotype = genotype_dict_inverse[genotype] | |
| # Convert input data to DataFrame for one-hot encoding | |
| input_data = pd.DataFrame([[gender, race, age, height, weight, diabetes, simvastatin, amiodarone, genotype, inr]], | |
| columns=['gender', 'race', 'age', 'height', 'weight', 'diabetes', 'simvastatin', | |
| 'amiodarone', 'genotype', 'inr']) | |
| # One-hot encode categorical features | |
| input_data_encoded = pd.get_dummies(input_data, columns=['gender', 'race', 'diabetes', 'simvastatin', 'amiodarone', 'genotype']) | |
| # Reindex the DataFrame to match expected feature names | |
| input_data_encoded = input_data_encoded.reindex(columns=expected_feature_names, fill_value=0) | |
| # Predict using the selected algorithm | |
| if algorithm == 'Random Forest': | |
| model = rf | |
| elif algorithm == 'Decision Tree': | |
| model = dt | |
| elif algorithm == 'MLP': | |
| model = mlp | |
| elif algorithm == 'KNN': | |
| model = knn | |
| else: | |
| raise ValueError("Invalid algorithm selected.") | |
| y_prob = model.predict_proba(input_data_encoded) | |
| class_idx = np.argmax(y_prob) | |
| preds_dict = {class_names[i]: float(y_prob[0, i]) for i in range(len(class_names))} | |
| name = class_names[class_idx] | |
| return name, preds_dict | |
| race_dict = { | |
| "African-American":0, | |
| "Asian":1, | |
| "Black":2, | |
| "Black African":3,"Black Caribbean":4,"Black or African American":5,"Black other":6 , | |
| "Caucasian":7,"Chinese":8,"Han Chinese":9,"Hispanic":10,"Indian":11,"Intermediate":12, | |
| "Japanese":13,"Korean":14, "Malay":15, "Other":16, "Other (Black British)":17, "Other (Hungarian)":18, "Other Mixed Race":19, "White":20} | |
| age_dict = { | |
| "10-19":0, | |
| "20-29":1, | |
| "30-39":2, | |
| "40-49":3,"50-59":4,"60-69":5,"70-79":6, | |
| "80-89":7,"90+":8} | |
| genotype_dict = {"A/A":0, "A/G":1, "G/G":2} | |
| # Invert dictionaries for decoding | |
| genotype_dict_inverse = {v: k for k, v in genotype_dict.items()} | |
| race_dict_inverse = {v: k for k, v in race_dict.items()} | |
| age_dict_inverse = {v: k for k, v in age_dict.items()} | |
| # Create Gradio interface | |
| gender_choices = [("Male", 1), ("Female", 0)] | |
| gender_module = gr.Dropdown(choices=gender_choices, label="Gender") | |
| # Assuming race_choices, age_choices, genotype_choices are already defined | |
| race_module = gr.Dropdown(choices=list(race_dict.items()), label="Race") | |
| age_module = gr.Dropdown(choices=list(age_dict.items()), label="Age Group") | |
| genotype_module = gr.Dropdown(choices=list(genotype_dict.items()), label="Genotype") | |
| height_module = gr.Number(label="Height") | |
| weight_module = gr.Number(label="Weight") | |
| diabetes_module = gr.Number(label="Diabetes") | |
| simvastatin_module = gr.Radio(choices=[0, 1], label="Simvastatin") | |
| amiodarone_module = gr.Radio(choices=[0, 1], label="Amiodarone") | |
| inr_module = gr.Number(label="INR Reported") | |
| algorithm_module = gr.Dropdown(choices=["Random Forest", "Decision Tree", "MLP", "KNN"], label="Algorithm") | |
| output_module1 = gr.Textbox(label="Predicted Class") | |
| output_module2 = gr.Label(label="Predicted Probability") | |
| iface = gr.Interface(fn=predict_warfarin_dose, | |
| inputs=[gender_module, race_module, age_module, height_module, weight_module, diabetes_module, | |
| simvastatin_module, amiodarone_module, genotype_module, inr_module, algorithm_module], | |
| outputs=[output_module1, output_module2]) | |
| iface.launch(debug=True,share=True) |