Update app.py

app.py

@@ -1,56 +1,56 @@
-import gradio as gr
-from model_architecture import ImageCaptionGenerationWithAttention
-from transformers import BartForConditionalGeneration, BartTokenizer, ViTModel, ViTImageProcessor
-import torch
-from PIL import Image
-from dotenv import load_dotenv
-import os
-import traceback
-
-load_dotenv()
-HF_TOKEN = os.getenv('hf_token')
-
-
-class GenerateCaptions:
-    def __init__(self):
-        self.device = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu")
-        vit_model = ViTModel.from_pretrained(
-            "google/vit-base-patch16-224", token=HF_TOKEN).to(self.device)
-        bart_model = BartForConditionalGeneration.from_pretrained(
-            "facebook/bart-base").to(self.device)
-        self.processor = ViTImageProcessor.from_pretrained(
-            "google/vit-base-patch16-224")
-        self.tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-        self.model = ImageCaptionGenerationWithAttention(
-            vit_model, bart_model, self.tokenizer)
-        self.model.load_state_dict(torch.load(
-            'image_captioning_model_state_dict.pt', map_location=self.device))
-        self.model.eval()
-
-    def generate_caption(self, frame, max_length=50, num_beams=5):
-        try:
-            image_pixel_values = self.processor(
-                frame, return_tensors="pt").pixel_values
-            generated_caption_ids = self.model.generate(
-                image_pixel_values, max_length, num_beams)
-            return self.tokenizer.decode(generated_caption_ids[0], skip_special_tokens=True)
-        except Exception as e:
-            print(e)
-            print(traceback.format_exc())
-
-
-gc = GenerateCaptions()
-
-demo = gr.Interface(
-    fn=gc.generate_caption,
-    inputs=gr.Image(type='pil'),
-    outputs="text",
-    title="Image Caption with Attention",
-    examples=['Image.jpg', 'Image 2.jpg'],
-    submit_btn='Generate Caption',
-    flagging_mode='never'
-)
-
-
-demo.launch()
+import gradio as gr
+from model_architecture import ImageCaptionGenerationWithAttention
+from transformers import BartForConditionalGeneration, BartTokenizer, ViTModel, ViTImageProcessor
+import torch
+from PIL import Image
+from dotenv import load_dotenv
+import os
+import traceback
+
+load_dotenv()
+HF_TOKEN = os.getenv('hf_token')
+
+
+class GenerateCaptions:
+    def __init__(self):
+        self.device = torch.device(
+            "cuda" if torch.cuda.is_available() else "cpu")
+        vit_model = ViTModel.from_pretrained(
+            "google/vit-base-patch16-224", token=HF_TOKEN).to(self.device)
+        bart_model = BartForConditionalGeneration.from_pretrained(
+            "facebook/bart-base").to(self.device)
+        self.processor = ViTImageProcessor.from_pretrained(
+            "google/vit-base-patch16-224")
+        self.tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
+        self.model = ImageCaptionGenerationWithAttention(
+            vit_model, bart_model, self.tokenizer)
+        self.model.load_state_dict(torch.load(
+            'image_captioning_model_state_dict.pt', map_location=self.device))
+        self.model.eval()
+
+    def generate_caption(self, frame, max_length=50, num_beams=5):
+        try:
+            image_pixel_values = self.processor(
+                frame, return_tensors="pt").pixel_values
+            generated_caption_ids = self.model.generate(
+                image_pixel_values, max_length, num_beams)
+            return self.tokenizer.decode(generated_caption_ids[0], skip_special_tokens=True)
+        except Exception as e:
+            print(e)
+            print(traceback.format_exc())
+
+
+gc = GenerateCaptions()
+
+demo = gr.Interface(
+    fn=gc.generate_caption,
+    inputs=gr.Image(type='pil'),
+    outputs="text",
+    title="Image Caption Generation",
+    examples=['Image.jpg', 'Image 2.jpg'],
+    submit_btn='Generate Caption',
+    flagging_mode='never'
+)
+
+
+demo.launch()