Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+import os
+import warnings
+
+# Suppress the FutureWarning from pandas
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+# --- Gradio App Components ---
+# List of possible values for the dropdown menus
+locations = ['Bathroom', 'Bedroom', 'Kitchen', 'Living Room']
+days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
+
+# Load the trained model pipeline from the joblib file
+try:
+    # Load the complete pipeline, which includes the pre-processors and the model
+    model_pipeline = joblib.load('fall_detection_pipeline.joblib')
+    # Load the final prediction threshold
+    with open('prediction_threshold.txt', 'r') as f:
+        threshold = float(f.read())
+    print("Model and threshold loaded successfully.")
+except FileNotFoundError as e:
+    model_pipeline = None
+    threshold = 0.4 # Default threshold in case of loading error
+    print(f"Error: {e}. Model files not found. The app will run in a placeholder mode.")
+except Exception as e:
+    model_pipeline = None
+    threshold = 0.4
+    print(f"An unexpected error occurred while loading files: {e}. The app will run in a placeholder mode.")
+
+
+def predict_fall(location, day_of_week, hour_of_day, minute_of_day, time_since_last_event):
+    """
+    Makes a fall prediction based on user inputs using the pre-trained model.
+    The function expects raw inputs and the pipeline handles all transformations.
+    """
+    # Check if the model was loaded successfully. If not, return an error message.
+    if model_pipeline is None:
+        return "Prediction model not available. Please ensure model files are correctly saved."
+    
+    try:
+        # Create a dictionary to hold the feature values based on the original data columns.
+        # This is the format the pipeline expects.
+        # The 'Movement Activity' column is not used based on previous analysis, but
+        # we can include it if the model requires it. For now, let's stick to the features
+        # identified in the previous steps.
+        
+        # The feature names from the notebook cells were:
+        # ['Location', 'day_of_week', 'hour_of_day', 'minute_of_day', 'time_since_last_event']
+        
+        # Create a DataFrame from the inputs
+        input_data = pd.DataFrame([{
+            'Location': location,
+            'day_of_week': day_of_week,
+            'hour_of_day': hour_of_day,
+            'minute_of_day': minute_of_day,
+            'time_since_last_event': time_since_last_event
+        }])
+
+        # Use the trained pipeline to get the probability. The pipeline
+        # automatically handles the scaling and one-hot encoding.
+        pred_proba = model_pipeline.predict_proba(input_data)[0, 1]
+        
+        # Get the final prediction label based on the threshold.
+        pred_label = "Fall Detected" if pred_proba >= threshold else "No Fall"
+
+        return f"Prediction: {pred_label}\nFall Probability: {pred_proba:.2f}"
+
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return f"An error occurred during prediction: {str(e)}"
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Elderly Fall Prediction System")
+    gr.Markdown("This system uses a trained machine learning model to predict the likelihood of a fall based on behavioral data. **Note:** This is a demonstration and should not be used for medical diagnosis.")
+
+    with gr.Row():
+        location_input = gr.Dropdown(choices=locations, label="Location", value='Living Room')
+        day_input = gr.Dropdown(choices=days_of_week, label="Day of Week", value='Monday')
+
+    with gr.Row():
+        hour_input = gr.Slider(minimum=0, maximum=23, step=1, label="Hour of Day", value=12)
+        minute_input = gr.Slider(minimum=0, maximum=59, step=1, label="Minute of Day", value=30)
+    
+    time_since_input = gr.Number(label="Time Since Last Event (minutes)", value=60)
+
+    predict_button = gr.Button("Predict Fall")
+    output = gr.Textbox(label="Prediction Result")
+
+    predict_button.click(
+        predict_fall,
+        inputs=[location_input, day_input, hour_input, minute_input, time_since_input],
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch()