Update app.py

app.py

@@ -1,78 +1,44 @@
 import gradio as gr
 import cv2
-import random
-import os
+from services.video_service import get_random_video_frame
 from services.detection_service import detect_objects
 from services.thermal_service import detect_thermal_anomalies
-from services.video_service import get_random_video_frame
+from services.overlay_service import overlay_boxes
 
-# Paths
 TEMP_IMAGE_PATH = "temp.jpg"
 
-# Initialize Metrics
-total_frames = 0
-total_detections = 0
-total_anomalies = 0
-
 def monitor_feed():
-    global total_frames, total_detections, total_anomalies
-
     frame = get_random_video_frame()
-
     if frame is None:
-        return None, None, None
+        return None, 0, 0  # 🛡️ If invalid frame, return defaults to keep app alive
 
-    # Save frame temporarily
     cv2.imwrite(TEMP_IMAGE_PATH, frame)
 
-    # Object detection
-    detections = detect_objects(TEMP_IMAGE_PATH)
-    detection_boxes = []
-    if detections:
-        for det in detections:
-            box = det['box']
-            label = det['label']
-            detection_boxes.append(label)
-            x, y, w, h = box
-            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-            cv2.putText(frame, label, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+    boxes = detect_objects(TEMP_IMAGE_PATH)
+    thermal_boxes = detect_thermal_anomalies(TEMP_IMAGE_PATH)
 
-    # Thermal detection
-    anomalies = detect_thermal_anomalies(TEMP_IMAGE_PATH)
-    anomaly_labels = []
-    if anomalies:
-        for anomaly in anomalies:
-            label = anomaly['label']
-            anomaly_labels.append(label)
+    all_boxes = boxes + thermal_boxes
+    image_with_boxes = overlay_boxes(TEMP_IMAGE_PATH, all_boxes)
 
-    # Update metrics
-    total_frames += 1
-    total_detections += len(detection_boxes)
-    total_anomalies += len(anomaly_labels)
-
-    # Overlay live metrics
-    metrics_text = f"Frames: {total_frames} | Detections: {total_detections} | Anomalies: {total_anomalies}"
-    cv2.putText(frame, metrics_text, (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 2)
-
-    return frame, {"Detections": detection_boxes}, {"Thermal Anomalies": anomaly_labels}
+    return image_with_boxes, len(all_boxes), len(thermal_boxes)
 
 def build_interface():
-    with gr.Blocks() as demo:
-        gr.Markdown("# Solar Surveillance System \U0001F310\U0001F50D")
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# 🚀 Solar Surveillance POC Demo")
         with gr.Row():
-            with gr.Column(scale=3):
-                image_output = gr.Image(label="Live Video Feed")
-            with gr.Column(scale=1):
-                detection_json = gr.JSON(label="Detections")
-                thermal_json = gr.JSON(label="Thermal Anomalies")
-                gr.Markdown("**Live Metrics Auto Updating** \U0001F4CA")
-
-        demo_btn = gr.Button("Start Monitoring")
-        demo_btn.click(fn=monitor_feed, outputs=[image_output, detection_json, thermal_json])
-
+            image_display = gr.Image(label="🔴 Live Surveillance Feed", interactive=False)
+            with gr.Column():
+                total_detections = gr.Number(label="Total Detections", value=0, interactive=False)
+                thermal_alerts = gr.Number(label="Thermal Alerts", value=0, interactive=False)
+
+        demo.load(
+            fn=monitor_feed,
+            inputs=[],
+            outputs=[image_display, total_detections, thermal_alerts],
+            every=1  # auto-refresh every 1 second
+        )
     return demo
 
 if __name__ == "__main__":
     demo = build_interface()
-    demo.queue()
-    demo.launch()
+    demo.launch(server_name="0.0.0.0", server_port=7860)