updated with spaces

app.py

@@ -4,7 +4,6 @@ import unicodedata
 from utils.utils import get_label
 
 
-
 def sanitize_feedback(feedback):
     """
     Convert emojis or other non-text characters in feedback to a text representation.
@@ -86,7 +85,7 @@ with gr.Blocks() as demo:
     feedback_button.click(fn=submit_feedback, inputs=feedback, outputs=None)
 
     # Thank you message section, initially hidden
-    thank_you_message = gr.Markdown("", visible=False)
+    thank_you_message = gr.Markdown("Thank you for your feedback!", visible=False)
 
     # Display thank you message when feedback is submitted
     feedback_submitted.change(
@@ -104,4 +103,4 @@ with gr.Blocks() as demo:
 
 # Launch the interface
 if __name__ == "__main__":
-    demo.launch(share=False)
+    demo.launch(share=True)

requirements.txt

@@ -11,4 +11,5 @@ opencv-python
 opencv-contrib-python
 openai-clip
 gradio==5.8.0
+spaces
 matplotlib

utils/Caption.py

@@ -1,7 +1,8 @@
 import torch
 from transformers import AutoModel, AutoTokenizer
+import spaces
 
-
+@spaces.GPU
 def get_caption(image):
     print(image)
     model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, torch_dtype=torch.float16)

utils/Emotions.py

@@ -7,8 +7,9 @@ from utils.ImageOnly import Decoder4
 from utils.CustomDataset import CustomDataset
 from utils.test import test
 from transformers import RobertaTokenizer
+import spaces
 
-
+@spaces.GPU
 def get_emotions(image, text):
     tags = ['Excitement', 'Sadness', 'Amusement', 'Disgust', 'Awe', 'Contentment', 'Fear', 'Anger']
     max_len = 128

utils/test.py

@@ -1,7 +1,8 @@
 import numpy as np
 import torch
+import spaces
 
-
+@spaces.GPU
 def test(model, model2, decoder, device, test_loader):
     model = model.to(device)
     decoder = decoder.to(device)

utils/utils.py

@@ -2,8 +2,9 @@ import matplotlib.pyplot as plt
 import numpy as np
 from utils.Caption import get_caption
 from utils.Emotions import get_emotions
+import spaces
 
-
+@spaces.GPU
 def get_label(image):
     caption = get_caption(image)