Update app.py

app.py

@@ -2,14 +2,30 @@ import gradio as gr
 import pandas as pd
 import numpy as np
 import pickle
-import joblib
-
-
-
-model = joblib.load('model.pkl')
-
-
-def predict(gender, age, hypertension, ever_married, work_type, heart_disease, avg_glucose_level, bmi, smoking_status, Residence_type):
+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 once when starting the app
+try:
+    model = decode_file('model.pkl')
+    print("Model loaded successfully")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    model = None
+
+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"
+        
     # Mapping for categorical variables
     gender_mapping = {'Male': 1, 'Female': 0}
     hypertension_mapping = {'Yes': 1, 'No': 0}
@@ -28,47 +44,41 @@ def predict(gender, age, hypertension, ever_married, work_type, heart_disease, a
     smoking_status = smoking_status_mapping[smoking_status]
     Residence_type = Residence_type_mapping[Residence_type]
 
-    inputs = [gender, age, hypertension, ever_married, work_type, heart_disease, avg_glucose_level, bmi, smoking_status, Residence_type]
-    input_labels = ['gender', 'age', 'hypertension', 'ever_married', 'work_type', 'heart_disease', 'avg_glucose_level', 'bmi', 'smoking_status', 'Residence_type']
+    # Create input data
+    inputs = [gender, age, hypertension, ever_married, work_type, heart_disease, 
+              avg_glucose_level, bmi, smoking_status, Residence_type]
+    input_labels = ['gender', 'age', 'hypertension', 'ever_married', 'work_type', 
+                   'heart_disease', 'avg_glucose_level', 'bmi', 'smoking_status', 'Residence_type']
 
-    # Convert the input into a pandas DataFrame
+    # Convert to DataFrame
     input_df = pd.DataFrame([inputs], columns=input_labels)
+    
+    try:
+        # Make prediction
+        prediction = model.predict_proba(input_df)[0][1]
+        return f"The probability of stroke is {prediction:.2%}"
+    except Exception as e:
+        return f"Error making prediction: {str(e)}"
 
-    # Predict the stroke probability
-    prediction = model.predict_proba(input_df)[0][1]
-
-    # Return the prediction
-    result = "The probability of stroke is {:.2f}%".format(prediction * 100)  # to give a percentage
-    return result
-
-
-
-
-
-
-input_labels = [
-    'gender', 'age', 'hypertension', 'ever_married', 'work_type', 
-    'heart_disease', 'avg_glucose_level', 'bmi', 'smoking_status', 'Residence_type'
-]
 # Create the Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=[
-        gr.components.Radio(choices=['Female', 'Male'], label="Gender"),
-        gr.components.Slider(label="Age"),
-        gr.components.Radio(choices=['Yes', 'No'], label="Hypertension"),
-        gr.components.Radio(choices=['Yes', 'No'], label="Ever Married"),
-        gr.components.Radio(choices=['Private', 'Self-employed', 'Govt_job', 'children', 'Never_worked'], label="Work Type"),
-        gr.components.Radio(choices=['Yes', 'No'], label="Heart Disease"),
-        gr.components.Number(label="Average Glucose Level"),
-        gr.components.Slider(label="BMI"),
-        gr.components.Radio(choices=['formerly smoked', 'never smoked', 'smokes', 'Unknown'], label="Smoking Status"),
-        gr.components.Radio(choices=['Urban', 'Rural'], label="Residence Type")
+        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.'
 )
 
-
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()