fix: revert to use numpy for rendering

app.py

@@ -10,23 +10,35 @@ model_path = hf_hub_download(repo_id='ethandavey/yoloV5-coursework-model', filen
 # Load the YOLOv5 model
 model = torch.hub.load('ultralytics/yolov5', 'custom', path=model_path, source='github')
 
-model.line_thickness = 1  # set line thickness for bounding boxes and labels
-
+# Print model class names
 print("Model class names:", model.names)
+
 print("Model loaded successfully.")
 
 def detect(image):
     # Run inference
     results = model(image)
 
-    # Set line thickness in results
-    results.render()
-
-    # Convert the image to a PIL Image
-    annotated_image = Image.fromarray(results.imgs[0])
-
-    return annotated_image
-
+    # Get detections
+    detections = results.xyxy[0]  # xyxy format for bounding boxes
+
+    # Check if any detections were made
+    if len(detections) > 0:
+        print(f"Number of detections: {len(detections)}")
+        for i, (*xyxy, conf, cls) in enumerate(detections):
+            # Extract box information
+            xyxy = [coord.item() for coord in xyxy]  # Coordinates
+            conf = conf.item()  # Confidence score
+            cls = int(cls.item())  # Class index
+            class_name = model.names[cls]  # Class name
+            print(f"Detection {i}: Class '{class_name}', Confidence {conf:.2f}, Coordinates {xyxy}")
+    else:
+        print("No detections were made.")
+
+    # Annotate image
+    annotated_image = np.squeeze(results.render())  # Render the detections on the image
+
+    return Image.fromarray(annotated_image)
 
 # Create Gradio Interface
 iface = gr.Interface(