Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import StandardScaler
+
+# Load the dataset for feature extraction
+csv_file_path = "creditcard.csv"  # Ensure this file is uploaded to Hugging Face
+df = pd.read_csv(csv_file_path)
+
+# Select important features
+selected_features = ["Amount", "V1", "V2", "V3", "Class"]
+df = df[selected_features]
+
+# Train-test split
+X = df.drop(columns=["Class"])
+y = df["Class"]
+
+# Standardize "Amount"
+scaler = StandardScaler()
+X["Amount"] = scaler.fit_transform(X[["Amount"]])
+
+# Train model
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X, y)
+
+# Save model and scaler
+joblib.dump(model, "fraud_model.pkl")
+joblib.dump(scaler, "scaler.pkl")
+
+
+# Define function for prediction
+def predict_fraud(amount, v1, v2, v3):
+    scaler = joblib.load("scaler.pkl")
+    model = joblib.load("fraud_model.pkl")
+
+    input_data = np.array([[amount, v1, v2, v3]])
+    input_data[:, 0] = scaler.transform(input_data[:, [0]])  # Scale amount
+
+    prediction = model.predict(input_data)
+    return "Fraudulent Transaction" if prediction[0] == 1 else "Non-Fraudulent Transaction"
+
+
+# Gradio UI
+iface = gr.Interface(
+    fn=predict_fraud,
+    inputs=[
+        gr.Number(label="Transaction Amount"),
+        gr.Number(label="V1"),
+        gr.Number(label="V2"),
+        gr.Number(label="V3"),
+    ],
+    outputs="text",
+    title="Credit Card Fraud Detection",
+    description="Enter transaction details to check if it's fraudulent or not.",
+)
+
+if __name__ == "__main__":
+    iface.launch()