Update app.py

app.py

@@ -1,50 +1,25 @@
-import gradio as gr
-import joblib
-import pandas as pd
-import os
-import traceback
-
-# --- Load model and artifacts ---
-try:
-    app_dir = os.path.dirname(os.path.abspath(__file__))
-    model = joblib.load(os.path.join(app_dir, "fall_detection_model.joblib"))
-    scaler = joblib.load(os.path.join(app_dir, "scaler.joblib"))
-    encoder = joblib.load(os.path.join(app_dir, "encoder.joblib"))
-    feature_names = joblib.load(os.path.join(app_dir, "feature_names.joblib"))  # list of all features used during training
-    print("Model, scaler, encoder, and feature names loaded successfully.")
-except FileNotFoundError as e:
-    print(f"Error: Missing file: {e}")
-    exit()
-
-# Categorical features and their categories (from encoder)
-categorical_features = ['Movement Activity', 'Location', 'day_of_week']
-categories_map = {cat: encoder.categories_[i].tolist() for i, cat in enumerate(categorical_features)}
-
-# --- Prediction function ---
-def predict_fall(movement_activity, location, day_of_week, time_since_last_event):
+def predict_fall(movement_activity, location, day_of_week, hour_of_day, minute_of_day, time_since_last_event):
     try:
-        # Initialize all features to 0hour_of_day, minute_of_day,
-        data = {f: 0 for f in feature_names}
+        data = {f: 0 for f in feature_names}  # fallback, keep for all known features
 
-        # Set one-hot encoded categorical features
+        # Set your categorical one-hot
         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 all features in correct order
-        input_df = pd.DataFrame([data])[feature_names]
+        # Build DataFrame with **all keys of data** (guarantees no missing columns)
+        input_df = pd.DataFrame([data], columns=list(data.keys()))
 
-        # Get columns scaler was trained on
         scaler_feature_names = scaler.feature_names_in_
 
-        # Scale only those columns, keep the rest unchanged
         scaled_array = scaler.transform(input_df[scaler_feature_names])
         input_df.loc[:, scaler_feature_names] = scaled_array
 
-        # Predict using the model on fully prepared DataFrame
         pred_proba = model.predict_proba(input_df)[0, 1]
         threshold = 0.4
         pred_label = "Fall Detected" if pred_proba >= threshold else "No Fall"
@@ -52,49 +27,7 @@ def predict_fall(movement_activity, location, day_of_week, time_since_last_event
         return f"Prediction: {pred_label}\nFall Probability: {pred_proba:.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."
-
-# --- Build Gradio interface ---
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# Fall Detection Model")
-    gr.Markdown("Provide sensor data to predict fall detection.")
-
-    movement_activity_input = gr.Dropdown(
-        label="Movement Activity",
-        choices=categories_map['Movement Activity'],
-        value=categories_map['Movement Activity'][0]
-    )
-    location_input = gr.Dropdown(
-        label="Location",
-        choices=categories_map['Location'],
-        value=categories_map['Location'][0]
-    )
-    day_of_week_input = gr.Dropdown(
-        label="Day of Week",
-        choices=categories_map['day_of_week'],
-        value=categories_map['day_of_week'][0]
-    )
-    hour_of_day_input = gr.Slider(0, 23, step=1, label="hour_of_day")
-    minute_of_day_input = gr.Slider(0, 59, step=1, label="minute_of_day")
-    time_since_last_event_input = gr.Number(label="time_since_last_event")
-
-    prediction_output = gr.Textbox(label="Prediction Result", lines=5)
-
-    predict_button = gr.Button("Run Prediction")
-    predict_button.click(
-        fn=predict_fall,
-        inputs=[
-            movement_activity_input,
-            location_input,
-            day_of_week_input,
-            hour_of_day_input,
-            minute_of_day_input,
-            time_since_last_event_input
-        ],
-        outputs=prediction_output
-    )
-
-if __name__ == "__main__":
-    demo.launch()