Update app.py

app.py

@@ -1,58 +1,42 @@
-import gradio as gr
-import pandas as pd
-import joblib
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-# Load trained model & feature order
-model = joblib.load('anomaly_detector_rf_model.pkl')
-feature_order = joblib.load('feature_order.pkl')
-
-
-# Function to make predictions
-def detect_anomalies(file):
-    # Read uploaded CSV
-    df = pd.read_csv(file)
-
-    # Ensure correct feature order
-    df = df[feature_order]
-
-    # Get predictions (0 = Normal, 1 = Anomalous)
-    df['Prediction'] = model.predict(df)
-
-    # Count anomalies
-    anomaly_count = df['Prediction'].sum()
-    total = len(df)
-
-    # Visualization
-    plt.figure(figsize=(5, 3))
-    sns.countplot(x=df['Prediction'], palette=['green', 'red'])
-    plt.xticks([0, 1], ["Normal", "Anomalous"])
-    plt.title("Anomaly Distribution")
-    plt.xlabel("Transaction Type")
-    plt.ylabel("Count")
-    plt.tight_layout()
-
-    # Save the plot
-    plot_path = "anomaly_plot.png"
-    plt.savefig(plot_path)
-
-    # Return table and plot
-    return df.head(), f"Detected {anomaly_count} anomalies out of {total} transactions.", plot_path
-
-
-# Gradio Interface
-interface = gr.Interface(
-    fn=detect_anomalies,
-    inputs=gr.File(label="Upload Transaction CSV"),
-    outputs=[
-        gr.Dataframe(label="Predictions"),
-        gr.Text(label="Summary"),
-        gr.Image(label="Anomaly Chart")
-    ],
-    title="Financial Anomaly Detector",
-    description="Upload a CSV file with transactions, and the model will detect suspicious activities."
-)
-
-# Launch app
-interface.launch()
+import pandas as pd
+import joblib
+import gradio as gr
+
+# Load the trained model
+model = joblib.load("anomaly_detector_rf_model.pkl")
+
+# Define feature columns (exclude non-numeric ones if needed)
+feature_cols = ['hour', 'day_of_week', 'is_weekend', 'amount_zscore', 'log_amount',
+                'type_atm_withdrawal', 'type_credit', 'type_debit', 'merchant_encoded']
+
+def detect_anomalies(file_path):
+    # Read the dataset
+    df = pd.read_csv(file_path)
+    
+    # Ensure all features exist in the dataframe
+    if not all(col in df.columns for col in feature_cols):
+        missing_cols = [col for col in feature_cols if col not in df.columns]
+        return f"Missing columns in dataset: {missing_cols}"
+    
+    # Make predictions
+    df['is_anomalous'] = model.predict(df[feature_cols])
+    
+    # Filter anomalous transactions
+    anomalies = df[df['is_anomalous'] == 1][['transaction_id', 'merchant', 'location', 'amount']]
+    
+    # Save to a new CSV
+    anomalies.to_csv("predicted_anomalies.csv", index=False)
+    
+    return anomalies
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=detect_anomalies,
+    inputs=gr.File(label="Upload CSV File"),
+    outputs=gr.Dataframe(label="Predicted Anomalies"),
+    title="Anomaly Detection System",
+    description="Upload a transaction dataset to detect anomalies."
+)
+
+if __name__ == "__main__":
+    interface.launch(share=True)