Update app.py

app.py

@@ -3,6 +3,7 @@ import pandas as pd
 import joblib
 import matplotlib.pyplot as plt
 import seaborn as sns
+import io
 
 # Load trained model
 model = joblib.load("anomaly_detector_rf_model.pkl")
@@ -27,9 +28,9 @@ def detect_anomalies(df):
     original_df["is_anomalous"] = preds
 
     anomalies = original_df[original_df["is_anomalous"] == 1]
-    return anomalies[["transaction_id", "merchant", "location", "amount", "is_anomalous"]]
+    return original_df, anomalies[["transaction_id", "merchant", "location", "amount", "is_anomalous"]]
 
-# Function to generate charts
+# Function to plot charts
 def plot_charts(df):
     fig, axes = plt.subplots(2, 2, figsize=(12, 10))
 
@@ -58,28 +59,49 @@ def plot_charts(df):
     plt.tight_layout()
     return fig
 
-# Gradio Interface logic
+# Function to generate summary + charts + file
 def app_interface(csv_file):
     df = pd.read_csv(csv_file)
-    anomalies = detect_anomalies(df)
-    fig = plot_charts(df)
-    return anomalies, fig
+    full_df, anomalies = detect_anomalies(df)
 
-# Launching with UI
+    total = len(full_df)
+    anom_count = len(anomalies)
+    percent = (anom_count / total) * 100 if total > 0 else 0
+
+    summary = (
+        f"🔢 **Total Transactions**: {total}\n"
+        f"⚠️ **Anomalies Detected**: {anom_count}\n"
+        f"📊 **Anomaly Percentage**: {percent:.2f}%"
+    )
+
+    # Convert anomalies to CSV bytes for download
+    csv_bytes = anomalies.to_csv(index=False).encode()
+    download = io.BytesIO(csv_bytes)
+
+    fig = plot_charts(full_df)
+
+    return summary, anomalies, fig, download
+
+# Gradio App with UI
 with gr.Blocks(theme=gr.themes.Soft()) as interface:
     gr.Markdown("# 🛡️ Financial Abuse & Anomaly Detection App")
-    gr.Markdown("Upload your **financial transaction CSV** to detect suspicious activity and view insightful visualizations.")
+    gr.Markdown("Upload your **transaction CSV** to detect anomalies and view insights.")
+
+    with gr.Row():
+        file_input = gr.File(label="📁 Upload CSV File", file_types=[".csv"])
+        detect_button = gr.Button("🚨 Run Detection", variant="primary")
 
     with gr.Row():
-        file_input = gr.File(label="📁 Upload Transaction CSV", file_types=[".csv"])
-        submit_btn = gr.Button("🔍 Detect Anomalies", variant="primary")
+        summary_box = gr.Markdown("")
 
     with gr.Tab("📋 Anomalies Detected"):
-        result_df = gr.Dataframe(label="🔴 Detected Suspicious Transactions")
+        result_table = gr.Dataframe(label="🔴 Anomalies")
+        download_btn = gr.File(label="⬇️ Download Detected Anomalies")
 
-    with gr.Tab("📊 Transaction Insights"):
-        chart_output = gr.Plot(label="📈 Transaction Summary Charts")
+    with gr.Tab("📊 Transaction Charts"):
+        chart_output = gr.Plot()
 
-    submit_btn.click(fn=app_interface, inputs=file_input, outputs=[result_df, chart_output])
+    detect_button.click(fn=app_interface, inputs=file_input,
+                        outputs=[summary_box, result_table, chart_output, download_btn])
 
 interface.launch(share=True)