Update app.py

app.py

@@ -4,8 +4,8 @@ import numpy as np
 import torch
 from ultralytics import YOLO
 
-# Load YOLOv8 model (pre-trained on COCO dataset)
-model = YOLO("yolov12x.pt")  # Use "yolov8x.pt" for highest accuracy
+# Load YOLOv12x model (pre-trained on COCO dataset)
+model = YOLO("yolov12x.pt")  # Update to YOLOv12x
 
 # Class label for trucks (COCO dataset)
 TRUCK_CLASS_ID = 7  # "truck" in COCO dataset
@@ -28,7 +28,7 @@ def count_trucks(video_path):
         if frame_count % frame_skip != 0:
             continue  # Skip frames to improve efficiency
 
-        # Run YOLOv8 inference
+        # Run YOLOv12x inference
         results = model(frame, verbose=False)
 
         truck_count = 0
@@ -45,8 +45,7 @@ def count_trucks(video_path):
     cap.release()
 
     return {
-        "Total Trucks in Video": int(np.max(truck_count_per_frame)) if truck_count_per_frame else 0 #,
-        #"Avg Trucks Per Frame": round(np.mean(truck_count_per_frame), 2) if truck_count_per_frame else 0
+        "Total Trucks in Video": int(np.max(truck_count_per_frame)) if truck_count_per_frame else 0
     }
 
 # Gradio UI function
@@ -59,9 +58,9 @@ interface = gr.Interface(
     fn=analyze_video,
     inputs=gr.Video(label="Upload Video"),
     outputs=gr.Textbox(label="Truck Counting Results"),
-    title="YOLOv8-based Truck Counter",
-    description="Upload a video to detect and count trucks using YOLOv8."
+    title="YOLOv12x-based Truck Counter",
+    description="Upload a video to detect and count trucks using YOLOv12x."
 )
 
 # Launch app
-interface.launch()
+interface.launch()