Update app.py

app.py

@@ -7,7 +7,7 @@ import seaborn as sns
 # Load trained model
 model = joblib.load("anomaly_detector_rf_model.pkl")
 
-# Define feature columns used during training
+# Features used during training
 feature_cols = [
     "amount", "hour", "day_of_week", "is_weekend", "merchant_avg_amount",
     "amount_zscore", "log_amount", "type_atm_withdrawal", "type_credit",
@@ -16,49 +16,70 @@ feature_cols = [
 
 # Function to detect anomalies
 def detect_anomalies(df):
-    # Ensure 'amount' column is present
-    if "amount" not in df.columns:
-        return "Error: 'amount' column is missing from the uploaded CSV file."
+    original_df = df.copy()
 
-    # Select only required features and maintain correct order
-    df = df.reindex(columns=feature_cols, fill_value=0)
+    for col in ["transaction_id", "merchant", "location", "amount"]:
+        if col not in original_df.columns:
+            original_df[col] = "N/A" if col != "amount" else 0.0
 
-    # Make predictions
-    df["is_anomalous"] = model.predict(df)
+    model_input = df.reindex(columns=feature_cols, fill_value=0)
+    preds = model.predict(model_input)
+    original_df["is_anomalous"] = preds
 
-    # Filter anomalies
-    anomalies = df[df["is_anomalous"] == 1]
+    anomalies = original_df[original_df["is_anomalous"] == 1]
+    return anomalies[["transaction_id", "merchant", "location", "amount", "is_anomalous"]]
 
-    # Keep only available columns
-    available_cols = [col for col in ["transaction_id", "merchant", "location", "amount"] if col in anomalies.columns]
-    return anomalies[available_cols] if available_cols else "No relevant columns found in the dataset."
-
-# Function to visualize anomalies
+# Function to generate charts
 def plot_charts(df):
     fig, axes = plt.subplots(2, 2, figsize=(12, 10))
-    sns.histplot(df["amount"], bins=30, kde=True, ax=axes[0, 0])
-    sns.boxplot(x=df["amount"], ax=axes[0, 1])
-    sns.countplot(x=df["day_of_week"], ax=axes[1, 0])
-    sns.barplot(x=df["merchant"], y=df["amount"], ax=axes[1, 1])
+
+    if "amount" in df.columns:
+        sns.histplot(df["amount"], bins=30, kde=True, ax=axes[0, 0])
+        axes[0, 0].set_title("Amount Distribution")
+        sns.boxplot(x=df["amount"], ax=axes[0, 1])
+        axes[0, 1].set_title("Amount Box Plot")
+    else:
+        axes[0, 0].text(0.5, 0.5, "No 'amount' column", ha='center')
+        axes[0, 1].text(0.5, 0.5, "No 'amount' column", ha='center')
+
+    if "day_of_week" in df.columns:
+        sns.countplot(x=df["day_of_week"], ax=axes[1, 0])
+        axes[1, 0].set_title("Transactions by Day of Week")
+    else:
+        axes[1, 0].text(0.5, 0.5, "No 'day_of_week' column", ha='center')
+
+    if "merchant" in df.columns:
+        top_merchants = df.groupby("merchant")["amount"].sum().nlargest(5).reset_index()
+        sns.barplot(data=top_merchants, x="merchant", y="amount", ax=axes[1, 1])
+        axes[1, 1].set_title("Top 5 Merchants by Amount")
+    else:
+        axes[1, 1].text(0.5, 0.5, "No 'merchant' column", ha='center')
 
     plt.tight_layout()
     return fig
 
-# Gradio Interface
+# Gradio Interface logic
 def app_interface(csv_file):
     df = pd.read_csv(csv_file)
     anomalies = detect_anomalies(df)
     fig = plot_charts(df)
-    
     return anomalies, fig
 
-interface = gr.Interface(
-    fn=app_interface,
-    inputs="file",
-    outputs=["dataframe", "plot"],
-    title="Financial Anomaly Detector",
-    description="Upload a transaction CSV file to detect fraudulent transactions."
-)
+# Launching 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.")
+
+    with gr.Row():
+        file_input = gr.File(label="📁 Upload Transaction CSV", file_types=[".csv"])
+        submit_btn = gr.Button("🔍 Detect Anomalies", variant="primary")
+
+    with gr.Tab("📋 Anomalies Detected"):
+        result_df = gr.Dataframe(label="🔴 Detected Suspicious Transactions")
+
+    with gr.Tab("📊 Transaction Insights"):
+        chart_output = gr.Plot(label="📈 Transaction Summary Charts")
+
+    submit_btn.click(fn=app_interface, inputs=file_input, outputs=[result_df, chart_output])
 
-# Launch the Gradio app with public access
 interface.launch(share=True)