new try

app.py

@@ -11,24 +11,42 @@ model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5_0.65map_exp7
                        force_reload=False) 
 model.conf = 0.20  # NMS confidence threshold
 
-path  = [['img/test-image.jpg'], ['img/test-image-2']]
+path  = [['img/test-image.jpg'], ['img/test-image-2.jpg']]
+
+# def show_preds_image(image_path):
+#     image = cv2.imread(image_path)
+#     outputs = model.predict(source=image_path)
+#     results = outputs[0].cpu().numpy()
+#     for i, det in enumerate(results.boxes.xyxy):
+#         cv2.rectangle(
+#             image,
+#             (int(det[0]), int(det[1])),
+#             (int(det[2]), int(det[3])),
+#             color=(0, 0, 255),
+#             thickness=2,
+#             lineType=cv2.LINE_AA
+#         )
+#     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
 def show_preds_image(image_path):
-    image = cv2.imread(image_path)
-    outputs = model.predict(source=image_path)
-    results = outputs[0].cpu().numpy()
-    for i, det in enumerate(results.boxes.xyxy):
-        cv2.rectangle(
-            image,
-            (int(det[0]), int(det[1])),
-            (int(det[2]), int(det[3])),
-            color=(0, 0, 255),
-            thickness=2,
-            lineType=cv2.LINE_AA
-        )
-    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    # perform inference
+    image_path = path
+    results = model(image_path, size=640)
+    # Results
+    results.print()
+
+    results.xyxy[0]  # img1 predictions (tensor)
+    results.pandas().xyxy[0]  # img1 predictions (pandas)
+
+    # parse results
+    predictions = results.pred[0]
+    boxes = predictions[:, :4] # x1, y1, x2, y2
+    scores = predictions[:, 4]
+    categories = predictions[:, 5]
 
+    return results.show()
 
+    
 
 inputs_image = [
     gr.components.Image(type="filepath", label="Input Image"),