Update app.py

app.py

@@ -57,8 +57,8 @@ def detect_anomalies(data_input, sensitivity=3.0):
         # Reshape to 3D format (batch, sequence, features)
         values_3d = values.reshape(1, -1, 1)
         
-        # Get reconstruction
-        reconstructed = model.reconstruct(X=values_3d)  # Explicit parameter name
+        # Get reconstruction - using explicit parameter name
+        reconstructed = model.reconstruct(X=values_3d)
         
         # Calculate reconstruction error (MAE)
         errors = np.abs(values - reconstructed[0,:,0])
@@ -83,26 +83,25 @@ def detect_anomalies(data_input, sensitivity=3.0):
         ax.grid(True)
         plt.tight_layout()
         
-        # Prepare outputs
-        stats = {
-            "data_points": len(df),
-            "anomalies_detected": int(df['is_anomaly'].sum()),
-            "detection_threshold": float(threshold),
-            "max_anomaly_score": float(np.max(errors)),
-            "average_value": float(np.mean(values))
-        }
+        # Limit DataFrame display size
+        display_df = df[['timestamp', 'value', 'anomaly_score', 'is_anomaly']].head(20)
         
         return (
             fig, 
-            gr.JSON(value=stats), 
-            gr.DataFrame(value=df[['timestamp', 'value', 'anomaly_score', 'is_anomaly']])
+            {"statistics": {
+                "data_points": len(df),
+                "anomalies_detected": int(df['is_anomaly'].sum()),
+                "detection_threshold": float(threshold),
+                "max_anomaly_score": float(np.max(errors))
+            }},
+            display_df.to_dict('records')
         )
     
     except Exception as e:
         logger.error(f"Detection error: {str(e)}")
         return (
             None,
-            gr.JSON(value={"error": str(e)}),
+            {"error": str(e)},
             None
         )
 
@@ -124,8 +123,10 @@ DEFAULT_DATA = """timestamp,value
 
 # Gradio Interface
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""📈 **Equipment Anomaly Detection**  
-    Detect unusual patterns in sensor data using MOMENT-1-large model""")
+    gr.Markdown("""
+    # 📈 Equipment Anomaly Detection
+    Detect unusual patterns in sensor data using MOMENT-1-large model
+    """)
     
     with gr.Row():
         with gr.Column():
@@ -133,28 +134,23 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                 label="Paste CSV Data",
                 value=DEFAULT_DATA,
                 lines=10,
-                max_lines=15,
-                placeholder="timestamp,value\n2025-..."
+                placeholder="timestamp,value\n2025-01-01, 100\n2025-01-02, 105..."
             )
             sensitivity = gr.Slider(
                 1.0, 5.0, value=3.0, step=0.1,
                 label="Detection Sensitivity (z-score)"
             )
-            btn = gr.Button("Analyze", variant="primary")
+            analyze_btn = gr.Button("Analyze", variant="primary")
             
         with gr.Column():
             plot = gr.Plot(label="Results")
             stats = gr.JSON(label="Detection Statistics")
-            results_df = gr.DataFrame(
-                label="Processed Results",
-                headers=["timestamp", "value", "anomaly_score", "is_anomaly"],
-                max_rows=10
-            )
+            results = gr.JSON(label="Top 20 Records")
     
-    btn.click(
+    analyze_btn.click(
         detect_anomalies,
         inputs=[data_input, sensitivity],
-        outputs=[plot, stats, results_df]
+        outputs=[plot, stats, results]
     )
 
 if __name__ == "__main__":