Update app.py

app.py

@@ -3,42 +3,34 @@ import pandas as pd
 import gradio as gr
 
 # Load model, scaler, feature names etc.
-model = joblib.load('model.joblib')
+model = joblib.load('fall_detection_model.joblib')  # updated here
 scaler = joblib.load('scaler.joblib')
 feature_names = joblib.load('feature_names.joblib')  # list of all features in correct order
 
-# For dropdown options, extract from encoder info or hardcode:
 movement_activities = ['Lying', 'No Movement', 'Sitting', 'Walking']
 locations = ['Bathroom', 'Bedroom', 'Kitchen', 'Living Room']
 days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
 
 def predict_fall(movement_activity, location, day_of_week, hour_of_day, minute_of_day, time_since_last_event):
     try:
-        # Initialize zero data dict for all features
         data = {f: 0 for f in feature_names}
 
-        # One-hot encode categorical features
         data[f'Movement Activity_{movement_activity}'] = 1
         data[f'Location_{location}'] = 1
         data[f'day_of_week_{day_of_week}'] = 1
 
-        # 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
 
-        # Create DataFrame with float dtype to avoid warnings
         input_df = pd.DataFrame([data], columns=feature_names, dtype=float)
 
-        # Scale numeric features only (assumes scaler was fit on these)
         scaler_cols = scaler.feature_names_in_
         scaled_features = scaler.transform(input_df[scaler_cols])
         input_df.loc[:, scaler_cols] = scaled_features
 
-        # Ensure columns are in model's expected order
         input_df = input_df[model.feature_names_in_]
 
-        # Predict probability
         pred_proba = model.predict_proba(input_df)[0, 1]
         threshold = 0.4
         pred_label = "Fall Detected" if pred_proba >= threshold else "No Fall"
@@ -50,7 +42,6 @@ def predict_fall(movement_activity, location, day_of_week, hour_of_day, minute_o
         traceback.print_exc()
         return f"Error: {str(e)}. Check server logs."
 
-# Build Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("## Fall Prediction")