Update app.py

app.py

@@ -1,28 +1,32 @@
 import gradio as gr
-import dill
 import pandas as pd
-import xgboost as xgb
 import numpy as np
-import pickle
+import joblib
 
-def decode_file(file_path):
-    with open(file_path, 'rb') as file:
-        obj = pickle.load(file)
-    return obj
+def predict(gender, age, hypertension, ever_married, work_type, 
+           heart_disease, avg_glucose_level, bmi, smoking_status, Residence_type):
 
-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]
@@ -31,47 +35,55 @@ 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']
-
-
-    input_df = pd.DataFrame([inputs], columns=input_label)
-
-    prediction = model.predict_proba(input_df)[0][1]
-
-
-    result = "The probability to have a stroke{:.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'
-]
-
-#The Interface
-iface = gr.Interface(
+    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(
     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__":
+    interface.launch()