updated app2

app.py

@@ -48,22 +48,8 @@ def predict_heart_failure(age_category, alcohol_drinkers, chest_scan, covid_posi
     print(f"Prediction: {prediction}")
     return "High likelihood of heart failure" if prediction == 1 else "Low likelihood of heart failure"
 
-def predict_heart_failure(age_category, alcohol_drinkers, chest_scan, covid_positive, physical_health_days, mental_health_days, sleep_hours, bmi):
-    # Prepare input data
-    input_data = {feature: 0 for feature in feature_names}
-    input_data[f"AgeCategory_{age_category}"] = 1
-    input_data[f"AlcoholDrinkers_{alcohol_drinkers}"] = 1
-    input_data[f"ChestScan_{chest_scan}"] = 1
-    input_data[f"CovidPos_{covid_positive}"] = 1
-    input_data["PhysicalHealthDays"] = physical_health_days
-    input_data["MentalHealthDays"] = mental_health_days
-    input_data["SleepHours"] = sleep_hours
-    input_data["BMI"] = bmi
-
-    # Convert to DataFrame
-    input_df = pd.DataFrame([input_data])
 
-    # Debugging: Print the input DataFrame
+def debug_model_input(input_df, model, threshold=0.5):
     print("Input DataFrame passed to the model:")
     print(input_df)
 
@@ -73,14 +59,11 @@ def predict_heart_failure(age_category, alcohol_drinkers, chest_scan, covid_posi
     print(probs)
 
     # Adjust threshold and make predictions
-    threshold = 0.4
     predictions = (probs[:, 1] >= threshold).astype(int)
     print("Predictions with adjusted threshold:")
     print(predictions)
 
-    # Return prediction
-    return "High likelihood of heart failure" if predictions[0] == 1 else "Low likelihood of heart failure"
-
+debug_model_input(test_df, model, threshold=0.4)
 
 # Define Gradio inputs
 gradio_inputs = [