Update app.py

app.py

@@ -2,36 +2,64 @@ import joblib
 import pandas as pd
 import gradio as gr
 
-# Load model
-model = joblib.load("stroke_rf_model.pkl")
+# Load model and preprocessor
+try:
+    model = joblib.load("stroke_rf_model.pkl")
+    preprocessor = joblib.load("preprocessor.pkl")
+    print("✅ Model and preprocessor loaded successfully.")
+except Exception as e:
+    print(f"❌ Error loading model/preprocessor: {str(e)}")
+    model = None
+    preprocessor = None
 
 # Define prediction function
-def predict_stroke(age, hypertension, heart_disease, glucose_level, bmi):
-    data = {
-        'age': [age],
-        'hypertension': [hypertension],
-        'heart_disease': [heart_disease],
-        'avg_glucose_level': [glucose_level],
-        'bmi': [bmi]
-    }
-    df = pd.DataFrame(data)
-    prediction = model.predict(df)
-    return "Stroke Risk" if prediction[0] == 1 else "No Stroke Risk"
+def predict_stroke(gender, age, hypertension, heart_disease, ever_married,
+                   work_type, residence_type, avg_glucose_level, smoking_status, bmi):
+    if model is None or preprocessor is None:
+        return "Error: Model or preprocessor not loaded."
 
-# Create Gradio Interface
+    data = pd.DataFrame([{
+        'gender': gender,
+        'age': age,
+        'hypertension': hypertension,
+        'heart_disease': heart_disease,
+        'ever_married': ever_married,
+        'work_type': work_type,
+        'Residence_type': residence_type,
+        'avg_glucose_level': avg_glucose_level,
+        'smoking_status': smoking_status,
+        'bmi': bmi
+    }])
+
+    try:
+        processed_data = preprocessor.transform(data)
+        prediction = model.predict(processed_data)
+        return "⚠️ Stroke Risk" if prediction[0] == 1 else "✅ No Stroke Risk"
+    except Exception as e:
+        print(f"❌ Prediction error: {str(e)}")
+        return f"Error: {str(e)}"
+
+# Gradio Interface
 iface = gr.Interface(
     fn=predict_stroke,
     inputs=[
+        gr.Textbox(label="Gender (Male/Female)"),
         gr.Number(label="Age"),
         gr.Radio(choices=[0, 1], label="Hypertension (0=No, 1=Yes)"),
         gr.Radio(choices=[0, 1], label="Heart Disease (0=No, 1=Yes)"),
+        gr.Textbox(label="Ever Married (Yes/No)"),
+        gr.Textbox(label="Work Type"),
+        gr.Textbox(label="Residence Type (Urban/Rural)"),
         gr.Number(label="Average Glucose Level"),
+        gr.Textbox(label="Smoking Status"),
         gr.Number(label="BMI")
     ],
     outputs="text",
-    title="Stroke Prediction Model",
-    description="Predict stroke risk based on health metrics."
+    title="🩺 Stroke Risk Prediction App",
+    description="Predict the likelihood of stroke based on health metrics.",
+    allow_flagging="never"
 )
 
-# Launch the app
-iface.launch()
+# Launch Gradio app
+if __name__ == "__main__":
+    iface.launch()