Update app.py (#1)

- Update app.py (2c18242cade832c0294d96d9a62aabb7a09e96f9)


Co-authored-by: Rushil shah <rushil78@users.noreply.huggingface.co>

app.py

@@ -10,8 +10,8 @@ from model import generate2,ClipCaptionModel
 from engine import inference
 
 
-model_trained = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-model_trained.load_state_dict(torch.load('model_trained.pth',map_location=torch.device('cpu')))
+# model_trained = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+# model_trained.load_state_dict(torch.load('model_trained.pth',map_location=torch.device('cpu')))
 image_processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 tokenizer       = GPT2TokenizerFast.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 
@@ -72,10 +72,10 @@ def ui():
             st.image(uploaded_file, width = 500, channels = 'RGB')
             st.markdown("**PREDICTION:** " + out)
 
-        elif option=='VIT+GPT2': 
-            out=show_n_generate(uploaded_file, greedy = False, model = model_trained)
-            st.image(uploaded_file, width = 500, channels = 'RGB')
-            st.markdown("**PREDICTION:** " + out)
+        # elif option=='VIT+GPT2': 
+        #     out=show_n_generate(uploaded_file, greedy = False, model = model_trained)
+        #     st.image(uploaded_file, width = 500, channels = 'RGB')
+        #     st.markdown("**PREDICTION:** " + out)