Create app.py

app.py

@@ -1,128 +0,0 @@
-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()