Upload 2 files

app.py

@@ -0,0 +1,81 @@
+import gradio as gr
+import joblib
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVC
+from sklearn.ensemble import RandomForestClassifier, StackingClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
+import numpy as np
+
+# --- 1. Load the Pre-trained Model and Label Encoder ---
+model_filename = 'stacking_classifier_model.joblib'
+try:
+    loaded_model = joblib.load(model_filename)
+except FileNotFoundError:
+    print(f"Error: The model file '{model_filename}' was not found.")
+    print("Please make sure you have run the training script and saved the model.")
+    exit()
+
+# Re-create the label encoder to decode predictions
+labels = ['No', 'Yes']
+label_encoder = LabelEncoder()
+label_encoder.fit(labels)
+
+# --- 2. Define the Prediction Function for Gradio ---
+def predict_fall(impact_force, body_orientation, heart_rate, temperature, movement_activity, location):
+    """
+    This function takes user inputs, preprocesses them, and uses the
+    trained Stacking Classifier to predict if a fall has occurred.
+    """
+    input_data = pd.DataFrame([{
+        'Impact Force Level': impact_force,
+        'Body Orientation (degrees)': body_orientation,
+        'Heart Rate (bpm)': heart_rate,
+        'Temperature (C)': temperature,
+        'Movement Activity': movement_activity,
+        'Location': location
+    }])
+
+    # Preprocessing to match the training data format
+    input_data['Location_Bathroom'] = 1 if location == 'Bathroom' else 0
+    input_data['Location_Bedroom'] = 1 if location == 'Bedroom' else 0
+    input_data['Location_Kitchen'] = 1 if location == 'Kitchen' else 0
+    input_data['Location_Living Room'] = 1 if location == 'Living Room' else 0
+    input_data.drop(['Location'], axis=1, inplace=True)
+    
+    input_data['Movement Activity_Falling'] = 1 if movement_activity == 'Falling' else 0
+    input_data['Movement Activity_Standing'] = 1 if movement_activity == 'Standing' else 0
+    input_data['Movement Activity_Walking'] = 1 if movement_activity == 'Walking' else 0
+    input_data.drop(['Movement Activity'], axis=1, inplace=True)
+
+    training_columns = loaded_model.feature_names_in_
+    for col in training_columns:
+        if col not in input_data.columns:
+            input_data[col] = 0
+            
+    input_data = input_data[training_columns]
+
+    # Make prediction
+    prediction = loaded_model.predict(input_data)[0]
+
+    # Decode the prediction
+    decoded_prediction = label_encoder.inverse_transform([prediction])[0]
+    return decoded_prediction
+
+# --- 3. Create and Launch the Gradio Interface ---
+inputs = [
+    gr.Slider(1, 100, label="Impact Force Level"),
+    gr.Slider(0, 180, label="Body Orientation (degrees)"),
+    gr.Slider(30, 200, label="Heart Rate (bpm)"),
+    gr.Slider(35, 42, label="Temperature (C)"),
+    gr.Dropdown(["Walking", "Standing", "Falling"], label="Movement Activity"),
+    gr.Dropdown(["Living Room", "Bedroom", "Kitchen", "Bathroom"], label="Location")
+]
+
+outputs = gr.Textbox(label="Fall Detected")
+
+demo = gr.Interface(fn=predict_fall, inputs=inputs, outputs=outputs, title="Fall Detection System")
+demo.launch()

stacking_classifier_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb844397f24d39d96698f1ca80cd6ff34aa62675e75714caae39c1524b5bd82b
+size 15289447