.

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-import pycaret
 from pycaret.classification import *
 
 
@@ -8,6 +7,18 @@ modelvalue= setup(data = healthcare_stroke_data, target = 'stroke', normalize =
 best = compare_models(sort = 'AUC', n_select = 15)
 compare_model_results = pull()
 
+
+def predict(model, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status):
+  
+    df = pd.DataFrame.from_dict({'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], 'bmi':[bmi],'smoking_status':[smoking_status]})
+    model_index = list(compare_model_results['Model']).index(model)
+    model = best[model_index]
+    pred = predict_model(model, df, raw_score=True)
+    return {'Yes': pred['Score_Yes'][0].astype('float64'), 
+            'No': pred['Score_No'][0].astype('float64' )}
+            
+            
 model = gr.inputs.Dropdown(list(compare_model_results['Model']),label="Model")
 gender = gr.inputs.Dropdown(choices=["Male", "Female"],label = 'gender')
 age = gr.inputs.Slider(minimum=1, maximum=100, default=data['age'].mean(), label = 'age')