Create app.py

app.py

@@ -0,0 +1,91 @@
+import gradio as gr
+import pandas as pd
+from catboost import CatBoostClassifier
+import joblib
+
+# Load and prepare data
+train_df = pd.read_csv('/kaggle/input/amazon-employee-access-challenge/train.csv')
+X = train_df.drop('ACTION', axis=1)
+y = train_df['ACTION']
+
+# Train and save model
+def train_and_save_model():
+    model = CatBoostClassifier(
+        iterations=100,
+        learning_rate=0.1,
+        depth=6,
+        verbose=0,
+        task_type='CPU',
+        bootstrap_type='Bernoulli',
+        subsample=0.8,
+        eval_metric='Accuracy',
+        early_stopping_rounds=20
+    )
+    model.fit(X, y)
+    joblib.dump(model, 'amazon_access_model.joblib', compress=3)
+    return model
+
+# Cache common values
+COMMON_VALUES = {
+    'ROLE_ROLLUP_1': train_df['ROLE_ROLLUP_1'].mode()[0],
+    'ROLE_ROLLUP_2': train_df['ROLE_ROLLUP_2'].mode()[0],
+    'ROLE_DEPTNAME': train_df['ROLE_DEPTNAME'].mode()[0],
+    'ROLE_FAMILY_DESC': train_df['ROLE_FAMILY_DESC'].mode()[0],
+    'ROLE_FAMILY': train_df['ROLE_FAMILY'].mode()[0],
+    'ROLE_CODE': train_df['ROLE_CODE'].mode()[0]
+}
+
+# Load or train model
+try:
+    model = joblib.load('amazon_access_model.joblib')
+except:
+    model = train_and_save_model()
+
+def predict_access(resource, mgr_id, role_title):
+    input_data = pd.DataFrame([[
+        resource,
+        mgr_id,
+        COMMON_VALUES['ROLE_ROLLUP_1'],
+        COMMON_VALUES['ROLE_ROLLUP_2'],
+        COMMON_VALUES['ROLE_DEPTNAME'],
+        role_title,
+        COMMON_VALUES['ROLE_FAMILY_DESC'],
+        COMMON_VALUES['ROLE_FAMILY'],
+        COMMON_VALUES['ROLE_CODE']
+    ]], columns=X.columns)
+    
+    prediction = model.predict(input_data)[0]
+    confidence = model.predict_proba(input_data)[0][prediction]
+    
+    result = "✅ Access Granted" if prediction == 1 else "❌ Access Denied"
+    return f"{result} (Confidence: {confidence:.2%})"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_access,
+    inputs=[
+        gr.Dropdown(
+            choices=sorted(train_df['RESOURCE'].unique().tolist())[:100],  # Limit choices
+            label="Resource"
+        ),
+        gr.Dropdown(
+            choices=sorted(train_df['MGR_ID'].unique().tolist())[:100],  # Limit choices
+            label="Manager"
+        ),
+        gr.Dropdown(
+            choices=sorted(train_df['ROLE_TITLE'].unique().tolist()),
+            label="Role Title"
+        )
+    ],
+    outputs=gr.Text(label="Access Decision"),
+    title="Amazon Access Control",
+    description="Select employee details to check access permission",
+    theme="soft",
+    examples=[
+        [train_df['RESOURCE'].iloc[0], train_df['MGR_ID'].iloc[0], train_df['ROLE_TITLE'].iloc[0]],
+        [train_df['RESOURCE'].iloc[1], train_df['MGR_ID'].iloc[1], train_df['ROLE_TITLE'].iloc[1]]
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()