Create app.py

app.py

@@ -0,0 +1,76 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import dare
+import time
+ 
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+ 
+# Load and prepare data
+data = pd.read_csv("parkinsons.data")
+data.columns = data.columns.str.replace(':', '_')
+X = data.drop(columns=["name", "status"]).values
+y = data["status"].values
+ 
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.15, stratify=y, random_state=42
+)
+ 
+# Function to train model, delete a sample, and retrain
+def run_dare_demo(delete_index=25):
+    logs = ""
+ 
+    # Train initial model
+    model = dare.Forest(n_estimators=25, max_depth=3, random_state=42)
+    start = time.perf_counter()
+    model.fit(X_train, y_train)
+    train_time = time.perf_counter() - start
+ 
+    y_pred = model.predict(X_test)
+    acc_before = accuracy_score(y_test, y_pred)
+ 
+    logs += f"✅ Initial training completed in {train_time:.4f} seconds\n"
+    logs += f"🎯 Accuracy before unlearning: {acc_before:.4f}\n"
+ 
+    # Delete a data point
+    try:
+        start_del = time.perf_counter()
+        model.delete(delete_index)
+        delete_time = time.perf_counter() - start_del
+        y_pred_after = model.predict(X_test.astype(np.float32))
+        acc_after = accuracy_score(y_test, y_pred_after)
+ 
+        logs += f"\n🧽 Deleted index {delete_index} in {delete_time:.5f} seconds\n"
+        logs += f"🎯 Accuracy after unlearning: {acc_after:.4f}\n"
+    except Exception as e:
+        logs += f"\n⚠️ Error during unlearning: {str(e)}\n"
+ 
+    # Retrain from scratch for comparison
+    try:
+        X_retrain = np.delete(X_train, delete_index, axis=0)
+        y_retrain = np.delete(y_train, delete_index, axis=0)
+ 
+        retrain_model = dare.Forest(n_estimators=25, max_depth=3, random_state=42)
+        start_retrain = time.perf_counter()
+        retrain_model.fit(X_retrain, y_retrain)
+        retrain_time = time.perf_counter() - start_retrain
+ 
+        logs += f"\n🔁 Retraining completed in {retrain_time:.5f} seconds (without index {delete_index})\n"
+    except Exception as e:
+        logs += f"\n⚠️ Error during retraining: {str(e)}\n"
+ 
+    return logs
+ 
+# Gradio Interface
+iface = gr.Interface(
+    fn=run_dare_demo,
+    inputs=gr.Slider(0, len(X_train)-1, value=25, step=1, label="Data Point Index to Unlearn"),
+    outputs="text",
+    title="DaRE: Unlearning Demo on Parkinson's Dataset",
+    description="This demo shows how to train a DaRE forest, unlearn a data point, and retrain for comparison using the Parkinson's dataset."
+)
+ 
+if __name__ == "__main__":
+    iface.launch()