Update app.py

app.py

@@ -1,83 +1,61 @@
 import gradio as gr
 import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import os
-import uuid
+import numpy as np
 import joblib
+import datetime
+import os
 
-# Load the model
+# Load trained model
 model = joblib.load("anomaly_detector_rf_model.pkl")
 
-# Define the features expected by the model
-expected_features = ["amount"]  # Update this list as per your trained model
-
 def detect_anomalies(df):
-    df = df.copy()
-    df['is_anomalous'] = model.predict(df[expected_features])
-    anomalies = df[df['is_anomalous'] == 1]
-
-    # Save anomalies to temporary CSV file
-    csv_filename = f"/tmp/anomalies_{uuid.uuid4().hex}.csv"
-    anomalies.to_csv(csv_filename, index=False)
+    # Feature Engineering (must match training phase)
+    df['datetime'] = pd.to_datetime(df['timestamp'])
+    df['hour'] = df['datetime'].dt.hour
+    df['day_of_week'] = df['datetime'].dt.dayofweek
+    df['is_weekend'] = df['day_of_week'].isin([5, 6]).astype(int)
+    df['log_amount'] = np.log1p(df['amount'])
+    df['amount_zscore'] = (df['amount'] - df['amount'].mean()) / df['amount'].std()
 
-    return (
-        anomalies[["transaction_id", "merchant", "location", "amount", "is_anomalous"]],
-        csv_filename
-    )
+    expected_features = ['amount', 'log_amount', 'amount_zscore', 'hour', 'day_of_week', 'is_weekend']
 
-def generate_summary(df):
-    total_transactions = len(df)
-    total_anomalies = df['is_anomalous'].sum()
-    percent_anomalies = round((total_anomalies / total_transactions) * 100, 2)
-    return f"Total Transactions: {total_transactions}\nTotal Anomalies: {total_anomalies}\nAnomaly Rate: {percent_anomalies}%"
-
-def generate_charts(df):
-    fig, ax = plt.subplots(1, 2, figsize=(12, 5))
+    df['is_anomalous'] = model.predict(df[expected_features])
 
-    # Distribution of Amounts
-    sns.histplot(df['amount'], bins=30, ax=ax[0], kde=True)
-    ax[0].set_title('Transaction Amount Distribution')
+    anomalies = df[df['is_anomalous'] == 1]
+    anomalies_display = anomalies[['transaction_id', 'merchant', 'location', 'amount']].reset_index(drop=True)
 
-    # Anomalies by Merchant
-    anomaly_counts = df[df['is_anomalous'] == 1]['merchant'].value_counts().nlargest(10)
-    sns.barplot(x=anomaly_counts.values, y=anomaly_counts.index, ax=ax[1])
-    ax[1].set_title('Top 10 Merchants with Anomalies')
+    # Save CSV for download
+    csv_path = "/tmp/anomalies.csv"
+    anomalies_display.to_csv(csv_path, index=False)
 
-    plt.tight_layout()
-    chart_path = f"/tmp/chart_{uuid.uuid4().hex}.png"
-    plt.savefig(chart_path)
-    plt.close()
-    return chart_path
+    return anomalies_display, csv_path, {
+        "Total Transactions": len(df),
+        "Anomalies Detected": len(anomalies),
+        "Anomaly %": f"{(len(anomalies)/len(df)*100):.2f}%"
+    }
 
 def app_interface(file):
-    df = pd.read_csv(file.name)
-    anomalies, csv_path = detect_anomalies(df)
-    summary = generate_summary(df)
-    chart_path = generate_charts(df)
-    return anomalies, summary, chart_path, csv_path
-
+    try:
+        df = pd.read_csv(file.name)
+        anomalies, csv_path, summary = detect_anomalies(df)
+        return anomalies, csv_path, summary
+    except Exception as e:
+        return pd.DataFrame(), None, {"Error": str(e)}
+
+# Gradio UI
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-    # 🔍 Elder Financial Abuse Detection Tool
-    Upload a transaction dataset to identify potential financial abuse patterns in elderly individuals.
-    """)
+    gr.Markdown("# 🧠 Financial Anomaly Detector\nUpload transaction data to detect anomalies using ML.")
 
     with gr.Row():
-        file_input = gr.File(label="📂 Upload Transaction CSV")
-        analyze_btn = gr.Button("🚀 Analyze")
+        file_input = gr.File(label="📤 Upload CSV", file_types=[".csv"])
+        download_button = gr.File(label="📥 Download Anomalies CSV")
 
     with gr.Row():
-        anomalies_output = gr.Dataframe(label="⚠️ Detected Anomalies", wrap=True)
-        summary_output = gr.Textbox(label="📊 Summary")
-
-    chart_output = gr.Image(label="📈 Analysis Charts")
-    csv_download = gr.File(label="📁 Download Anomalies CSV")
+        output_table = gr.Dataframe(label="🚨 Detected Anomalies", wrap=True, height=300)
+        summary_box = gr.JSON(label="📊 Summary")
 
-    analyze_btn.click(
-        fn=app_interface,
-        inputs=[file_input],
-        outputs=[anomalies_output, summary_output, chart_output, csv_download]
-    )
+    file_input.change(fn=app_interface, inputs=file_input, outputs=[output_table, download_button, summary_box])
 
-demo.launch()
+# If running locally
+if __name__ == "__main__":
+    demo.launch()