Create app.py

app.py

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+import pandas as pd
+import gradio as gr
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+
+# Load dataset
+df = pd.read_csv("fraud_data.csv")
+
+# Preprocess data
+le_country = LabelEncoder()
+le_device = LabelEncoder()
+df['country'] = le_country.fit_transform(df['country'])
+df['device'] = le_device.fit_transform(df['device'])
+
+# Convert transaction_time to minutes
+df['transaction_time'] = df['transaction_time'].apply(lambda x: int(x.split(":")[0]) * 60 + int(x.split(":")[1]))
+
+# Features and target
+X = df.drop('is_fraud', axis=1)
+y = df['is_fraud']
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train model
+model = RandomForestClassifier()
+model.fit(X_train, y_train)
+
+# Prediction function
+def predict_fraud(amount, country, device, transaction_time):
+    country_encoded = le_country.transform([country])[0]
+    device_encoded = le_device.transform([device])[0]
+    time_in_minutes = int(transaction_time.split(":")[0]) * 60 + int(transaction_time.split(":")[1])
+    input_data = [[amount, country_encoded, device_encoded, time_in_minutes]]
+    prediction = model.predict(input_data)[0]
+    return "Fraudulent" if prediction == 1 else "Legitimate"
+
+# Gradio interface
+countries = df['country'].unique()
+devices = df['device'].unique()
+
+iface = gr.Interface(
+    fn=predict_fraud,
+    inputs=[
+        gr.Number(label="Amount"),
+        gr.Dropdown(choices=le_country.classes_.tolist(), label="Country"),
+        gr.Dropdown(choices=le_device.classes_.tolist(), label="Device"),
+        gr.Textbox(label="Transaction Time (HH:MM)")
+    ],
+    outputs="text",
+    title="Fraud Detection System",
+    description="Enter transaction details to predict if it's fraudulent."
+)
+
+iface.launch()