Update app.py

app.py

@@ -1,32 +1,27 @@
 import gradio as gr
+import dill
 import pandas as pd
+import xgboost as xgb
 import numpy as np
-import joblib
+import pickle
 
-def predict(gender, age, hypertension, ever_married, work_type, 
-           heart_disease, avg_glucose_level, bmi, smoking_status, Residence_type):
+def decode_file(file_path):
+    with open(file_path, 'rb') as file:
+        obj = pickle.load(file)
+    return obj
 
+model = decode_file('model.pkl')
 
+
+def predict(gender, age, hypertension, ever_married, work_type, heart_disease, avg_glucose_level, bmi, smoking_status, Residence_type):
     gender_mapping = {'Male': 1, 'Female': 0}
     hypertension_mapping = {'Yes': 1, 'No': 0}
     ever_married_mapping = {'Yes': 1, 'No': 0}
-    work_type_mapping = {
-        'Private': 2, 
-        'Self-employed': 4, 
-        'Govt_job': 3, 
-        'children': 1, 
-        'Never_worked': 0
-    }
+    work_type_mapping = {'Private': 2, 'Self-employed': 4, 'Govt_job': 3, 'children': 1, 'Never_worked': 0}
     heart_disease_mapping = {'Yes': 1, 'No': 0}
-    smoking_status_mapping = {
-        'formerly smoked': 3, 
-        'smokes': 1, 
-        'never smoked': 2, 
-        'Unknown': 0
-    }
+    smoking_status_mapping = {'formerly smoked': 3, 'smokes': 1, 'never smoked': 2, 'Unknown': 0}
     Residence_type_mapping = {'Urban': 1, 'Rural': 0}
 
-
     gender = gender_mapping[gender]
     hypertension = hypertension_mapping[hypertension]
     ever_married = ever_married_mapping[ever_married]
@@ -35,55 +30,43 @@ def predict(gender, age, hypertension, ever_married, work_type,
     smoking_status = smoking_status_mapping[smoking_status]
     Residence_type = Residence_type_mapping[Residence_type]
 
-    input_data = {
-        'gender': [gender],
-        'age': [age],
-        'hypertension': [hypertension],
-        'ever_married': [ever_married],
-        'work_type': [work_type],
-        'heart_disease': [heart_disease],
-        'avg_glucose_level': [avg_glucose_level],
-        'bmi': [bmi],
-        'smoking_status': [smoking_status],
-        'Residence_type': [Residence_type]
-    }
-    
-    input_df = pd.DataFrame(input_data)
-
-    try:
-        model = joblib.load('model.pkl')
-        
-        prediction = model.predict_proba(input_df)[0][1]
-        
-        result = f"The probability to have a stroke: {prediction:.2%}"
-        return result
-    except Exception as e:
-        return f"Error making prediction: {str(e)}"
-
-interface = gr.Interface(
+    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']
+
+    input_df = pd.DataFrame([inputs], columns=input_labels)
+
+    prediction = model.predict_proba(input_df)[0][1]
+
+    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'
+]
+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")
+        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")
     ],
     outputs='text',
     title='Stroke Probability Predictor',
     description='Predicts the probability of having a stroke based on input features.'
 )
 
-if __name__ == "__main__":
-    interface.launch() 
+
+iface.launch()