Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+import pickle
+
+# Load the trained model
+with open('knn_model.pkl', 'rb') as file:
+    kn_class = pickle.load(file)
+
+# Load the fitted MinMaxScaler
+with open('scaler.pkl', 'rb') as file:
+    scaler = pickle.load(file)
+
+def predict_fraud(cc_num, gender, lat, long, city_pop, unix_time, amount):
+    # Handle categorical feature 'Gender'
+    gender = 1 if gender == 'M' else 0
+    
+    # Scale the amount feature
+    amount_scaled = scaler.transform([[amount]])[0][0]
+    
+    # Create input dataframe for the model
+    input_data = pd.DataFrame({
+        'cc_num': [cc_num],
+        'Gender': [gender],
+        'lat': [lat],
+        'long': [long],
+        'city_pop': [city_pop],
+        'unix_time': [unix_time],
+        'Amount_Scaled': [amount_scaled]
+    })
+    
+    # Predict using the loaded model
+    prediction = kn_class.predict(input_data)
+    
+    # Return the result
+    return 'Fraudulent Transaction' if prediction[0] == 1 else 'Legitimate Transaction'
+
+# Define examples, including one example of fraud
+examples = [
+    [1234567890123456, 'M', 40.712776, -74.005974, 8398748, 1614575732, 100.0],  # Legitimate transaction
+    [2345678901234567, 'F', 34.052235, -118.243683, 3990456, 1614575832, 200.0],  # Legitimate transaction
+    [3456789012345678, 'M', 37.774929, -122.419416, 883305, 1614575932, 5000.0]  # Fraudulent transaction
+]
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=predict_fraud,
+    inputs=[
+        gr.Number(label="Credit Card Number"),
+        gr.Radio(['M', 'F'], label="Gender"),
+        gr.Number(label="Latitude"),
+        gr.Number(label="Longitude"),
+        gr.Number(label="City Population"),
+        gr.Number(label="Unix Time"),
+        gr.Number(label="Transaction Amount")
+    ],
+    outputs="text",
+    title="Fraud Detection Application",
+    description="Enter the transaction details to predict if it is fraudulent or legitimate.",
+    examples=examples
+)
+
+# Launch the interface
+interface.launch()