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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import pickle | |
| from sklearn.preprocessing import StandardScaler | |
| import sklearn | |
| import os | |
| print(f"Prediction environment scikit-learn version: {sklearn.__version__}") | |
| def decode_file(file_path): | |
| with open(file_path, 'rb') as file: | |
| obj = pickle.load(file) | |
| return obj | |
| # Load the model without try/except | |
| model = decode_file('./model.pkl') | |
| def preprocess_input(data_dict): | |
| """Preprocess input data to match the training format""" | |
| # First create DataFrame with original columns | |
| df = pd.DataFrame([data_dict]) | |
| # Store original features before transformation | |
| original_df = df.copy() | |
| # Numeric features | |
| numeric_features = ['age', 'avg_glucose_level', 'bmi'] | |
| # Scale numeric features and add prefix | |
| scaler = StandardScaler() | |
| scaled_features = scaler.fit_transform(df[numeric_features]) | |
| for i, feat in enumerate(numeric_features): | |
| df[f'num__{feat}'] = scaled_features[:, i] | |
| # Create categorical features | |
| # Gender | |
| df['cat__gender_Male'] = (df['gender'] == 'Male').astype(float) | |
| df['cat__gender_Other'] = 0.0 | |
| # Hypertension | |
| df['cat__hypertension_1'] = df['hypertension'].astype(float) | |
| # Heart Disease | |
| df['cat__heart_disease_1'] = df['heart_disease'].astype(float) | |
| # Ever Married | |
| df['cat__ever_married_Yes'] = (df['ever_married'] == 'Yes').astype(float) | |
| # Work Type | |
| df['cat__work_type_Never_worked'] = (df['work_type'] == 'Never_worked').astype(float) | |
| df['cat__work_type_Private'] = (df['work_type'] == 'Private').astype(float) | |
| df['cat__work_type_Self-employed'] = (df['work_type'] == 'Self-employed').astype(float) | |
| df['cat__work_type_children'] = (df['work_type'] == 'children').astype(float) | |
| # Residence Type | |
| df['cat__Residence_type_Urban'] = (df['Residence_type'] == 'Urban').astype(float) | |
| # Smoking Status | |
| df['cat__smoking_status_formerly smoked'] = (df['smoking_status'] == 'formerly smoked').astype(float) | |
| df['cat__smoking_status_never smoked'] = (df['smoking_status'] == 'never smoked').astype(float) | |
| df['cat__smoking_status_smokes'] = (df['smoking_status'] == 'smokes').astype(float) | |
| # Select only the transformed columns in the correct order | |
| feature_columns = [ | |
| 'num__age', 'num__avg_glucose_level', 'num__bmi', | |
| 'cat__gender_Male', 'cat__gender_Other', 'cat__hypertension_1', | |
| 'cat__heart_disease_1', 'cat__ever_married_Yes', | |
| 'cat__work_type_Never_worked', 'cat__work_type_Private', | |
| 'cat__work_type_Self-employed', 'cat__work_type_children', | |
| 'cat__Residence_type_Urban', 'cat__smoking_status_formerly smoked', | |
| 'cat__smoking_status_never smoked', 'cat__smoking_status_smokes' | |
| ] | |
| return df[feature_columns] | |
| def predict(gender, age, hypertension, ever_married, work_type, heart_disease, | |
| avg_glucose_level, bmi, smoking_status, Residence_type): | |
| """Make prediction using the loaded model""" | |
| if model is None: | |
| return "Error: Model not loaded" | |
| # Create input dictionary | |
| input_data = { | |
| 'gender': gender, | |
| 'age': float(age), # Ensure numeric | |
| 'hypertension': 1 if hypertension == 'Yes' else 0, | |
| 'heart_disease': 1 if heart_disease == 'Yes' else 0, | |
| 'ever_married': ever_married, | |
| 'work_type': work_type, | |
| 'Residence_type': Residence_type, | |
| 'avg_glucose_level': float(avg_glucose_level), # Ensure numeric | |
| 'bmi': float(bmi), # Ensure numeric | |
| 'smoking_status': smoking_status | |
| } | |
| # Preprocess the input | |
| processed_input = preprocess_input(input_data) | |
| # Use the loaded model | |
| try: | |
| prediction = model.predict_proba(processed_input)[0][1] | |
| return f"The probability of stroke is {prediction:.2%}" | |
| except Exception as e: | |
| return f"Error making prediction: {str(e)}, model is not valid" # updated here | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.Radio(choices=['Female', 'Male'], label="Gender"), | |
| gr.Slider(minimum=0, maximum=100, label="Age"), | |
| gr.Radio(choices=['Yes', 'No'], label="Hypertension"), | |
| gr.Radio(choices=['Yes', 'No'], label="Ever Married"), | |
| gr.Radio(choices=['Private', 'Self-employed', 'Govt_job', 'children', 'Never_worked'], label="Work Type"), | |
| gr.Radio(choices=['Yes', 'No'], label="Heart Disease"), | |
| gr.Number(label="Average Glucose Level"), | |
| gr.Slider(minimum=10, maximum=50, label="BMI"), | |
| gr.Radio(choices=['formerly smoked', 'never smoked', 'smokes', 'Unknown'], label="Smoking Status"), | |
| gr.Radio(choices=['Urban', 'Rural'], label="Residence Type") | |
| ], | |
| outputs='text', | |
| title='Stroke Probability Predictor', | |
| description='Predicts the probability of having a stroke based on input features.' | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |