try fix

app.py

@@ -101,27 +101,27 @@ def main(
     n_samples=4,
     ):
     debug_print("main")
-    images = []
-    for url in test_images_urls:
-        import requests
-        from io import BytesIO
-        from PIL import Image
-        try:
-            response = requests.get(url)
-            if not response.ok:
-                continue
-            bytes = BytesIO(response.content)
-            image = Image.open(bytes)
-            if image.mode != 'RGB':
-                image = image.convert('RGB')
-            # width = 336
-            # aspect_ratio = float(image.height) / float(image.width)
-            # height = int(width * aspect_ratio)
-            # image = image.resize((width, height), Image.Resampling.LANCZOS)
-            images.append((image, "title"))
-        except Exception as e:
-            print(e)
-    return images    
+    # images = []
+    # for url in test_images_urls:
+    #     import requests
+    #     from io import BytesIO
+    #     from PIL import Image
+    #     try:
+    #         response = requests.get(url)
+    #         if not response.ok:
+    #             continue
+    #         bytes = BytesIO(response.content)
+    #         image = Image.open(bytes)
+    #         if image.mode != 'RGB':
+    #             image = image.convert('RGB')
+    #         # width = 336
+    #         # aspect_ratio = float(image.height) / float(image.width)
+    #         # height = int(width * aspect_ratio)
+    #         # image = image.resize((width, height), Image.Resampling.LANCZOS)
+    #         images.append((image, "title"))
+    #     except Exception as e:
+    #         print(e)
+    # return images    
 
     embeddings = base64_to_embedding(embeddings)
     # convert to python array
@@ -268,18 +268,18 @@ def on_example_image_click_set_image(input_image, image_url):
 # device = torch.device("mps" if torch.backends.mps.is_available() else "cuda:0" if torch.cuda.is_available() else "cpu")
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
-# from clip_retrieval.load_clip import load_clip, get_tokenizer 
-# from clip_retrieval.clip_client import ClipClient, Modality
-# model, preprocess = load_clip(clip_model, use_jit=True, device=device)
-# tokenizer = get_tokenizer(clip_model)
-# clip_retrieval_client = ClipClient(
-#     url=clip_retrieval_service_url, 
-#     indice_name=clip_model_id,
-#     use_safety_model = False,
-#     use_violence_detector = False,
-#     # modality = Modality.TEXT,
-#     )
-model, preprocess, tokenizer, clip_retrieval_client = None, None, None, None
+from clip_retrieval.load_clip import load_clip, get_tokenizer 
+from clip_retrieval.clip_client import ClipClient, Modality
+model, preprocess = load_clip(clip_model, use_jit=True, device=device)
+tokenizer = get_tokenizer(clip_model)
+clip_retrieval_client = ClipClient(
+    url=clip_retrieval_service_url, 
+    indice_name=clip_model_id,
+    use_safety_model = False,
+    use_violence_detector = False,
+    # modality = Modality.TEXT,
+    )
+# model, preprocess, tokenizer, clip_retrieval_client = None, None, None, None
 
 
 examples = [