Update app.py

app.py

@@ -2,66 +2,66 @@ import gradio as gr
 import joblib
 import pandas as pd
 
-# Load saved objects
+# Load model artifacts
 model = joblib.load('fall_detection_model.joblib')
 scaler = joblib.load('scaler.joblib')
 encoder = joblib.load('encoder.joblib')
-feature_names = joblib.load('feature_names.joblib')  # make sure this has hour_of_day and minute_of_day
+feature_names = joblib.load('feature_names.joblib')  # your full feature list
+
+# Extract categories for dropdown menus
+movement_choices = encoder.categories_[0].tolist()
+location_choices = encoder.categories_[1].tolist()
+day_choices = [col.replace('day_of_week_', '') for col in feature_names if col.startswith('day_of_week_')]
 
 def predict_fall(movement_activity, location, day_of_week, hour_of_day, minute_of_day, time_since_last_event):
     try:
-        # Initialize all features with zero
-        data = {f: 0 for f in feature_names}
+        # Initialize all features to 0
+        data = {feat: 0 for feat in feature_names}
 
         # One-hot encode categoricals
         data[f'Movement Activity_{movement_activity}'] = 1
         data[f'Location_{location}'] = 1
         data[f'day_of_week_{day_of_week}'] = 1
 
-        # Set numerical features
+        # Set numeric features
         data['hour_of_day'] = hour_of_day
         data['minute_of_day'] = minute_of_day
         data['time_since_last_event'] = time_since_last_event
 
-        # Build DataFrame with columns ordered as feature_names
+        # Build DataFrame with columns in correct order
         input_df = pd.DataFrame([data], columns=feature_names)
 
-        # Scale numeric features
+        # Scale all features (must match scaler's expected input columns)
         scaled_array = scaler.transform(input_df)
         input_df.loc[:, feature_names] = scaled_array
 
-        # Predict probability
-        pred_proba = model.predict_proba(input_df)[0, 1]
+        # Predict probability of fall
+        prob_fall = model.predict_proba(input_df)[:, 1][0]
+
         threshold = 0.4
-        pred_label = "Fall Detected" if pred_proba >= threshold else "No Fall"
+        prediction = "Fall Detected" if prob_fall >= threshold else "No Fall"
 
-        return f"Prediction: {pred_label}\nFall Probability: {pred_proba:.2f}"
+        return f"Prediction: {prediction}\nFall Probability: {prob_fall:.2f}"
 
     except Exception as e:
         import traceback
-        tb = traceback.format_exc()
-        print("Error in prediction:\n", tb)
-        return f"Error: {str(e)}\nCheck server logs for details."
-
-# Prepare dropdown choices from encoder categories
-movement_choices = encoder.categories_[0].tolist()
-location_choices = encoder.categories_[1].tolist()
-day_choices = [col.replace('day_of_week_', '') for col in feature_names if col.startswith('day_of_week_')]
+        traceback.print_exc()
+        return f"Error: {str(e)}. Check logs for details."
 
-# Gradio Interface
+# Gradio interface setup
 iface = gr.Interface(
     fn=predict_fall,
     inputs=[
-        gr.Dropdown(choices=movement_choices, label="Movement Activity"),
-        gr.Dropdown(choices=location_choices, label="Location"),
-        gr.Dropdown(choices=day_choices, label="Day of Week"),
+        gr.Dropdown(movement_choices, label="Movement Activity"),
+        gr.Dropdown(location_choices, label="Location"),
+        gr.Dropdown(day_choices, label="Day of Week"),
         gr.Slider(0, 23, step=1, label="Hour of Day"),
         gr.Slider(0, 59, step=1, label="Minute of Day"),
-        gr.Number(label="Time Since Last Event (seconds)", value=60)
+        gr.Number(label="Time Since Last Event (seconds)", value=60),
     ],
     outputs="text",
     title="Fall Detection Model",
-    description="Predicts fall based on sensor data."
+    description="Predicts if a fall is detected based on sensor input."
 )
 
 if __name__ == "__main__":