Update app.py

app.py

@@ -1,3 +1,4 @@
+
 import torch
 import pickle
 import cv2
@@ -8,11 +9,11 @@ from transformers import ViTForImageClassification, AutoImageProcessor, AdamW, V
 from torch.utils.data import DataLoader, TensorDataset
 import gradio as gr
 
-model_path = '/home/user/app'
+model_path = '/home/usr/app'
 train_pickle_path = 'train_data.pickle'
 valid_pickle_path = 'valid_data.pickle'
 image_directory = 'images' 
-test_image_path = '/home/user/app/test.jpg'
+test_image_path = '/home/usr/app/test.jpg'
 num_epochs = 5    # Fine-tune the model
 label_list = ["小白", "巧巧", "冏媽", "乖狗", "花捲", "超人", "黑胖", "橘子"]
 label_dictionary = {"小白": 0, "巧巧": 1, "冏媽": 2, "乖狗": 3, "花捲": 4, "超人": 5, "黑胖": 6, "橘子": 7}
@@ -163,14 +164,21 @@ def predict(upload_image):
     # Load the test data
     # Load the image
 
-    # img = cv2.imread(test_image_path)
+    # img2 = cv2.imread(test_image_path)
+    # print("cv2: ", img2)
+    # print("cv2 shape: ", img2.shape)
     # img = upload_image
-    # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    # img = cv2.cvtColor((upload_image * 255).astype(np.uint8), cv2.COLOR_RGB2BGR)
     pil_image = upload_image.convert('RGB')
     open_cv_image = np.array(pil_image)
     # Convert RGB to BGR
     img = open_cv_image[:, :, ::-1].copy()
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    # print("gradio: ", img)
+    # print("gradio shape: ", img.shape)
+
+
+    # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
 
     # Resize the image to 224x224 pixels
     img = Image.fromarray(img)