Update app.py

app.py

@@ -5,12 +5,8 @@ from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTok
 model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
-
-
-
 max_length = 16
 num_beams = 4
 gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
@@ -43,21 +39,21 @@ def predict(image,max_length=64, num_beams=4):
 
 description= "NLP Image Understanding"
 title = "NLP Image Understanding"
-article = "nlpconnect/vit-gpt2-image-captioning"
+article = "nlpconnect vit-gpt2-image-captioning"
 
 input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)
 output = gr.outputs.Textbox(type="auto",label="Captions")
+
 #examples = [['35-Favorite-Games.jpg']]
-examples = [f"{i}.jpg" for i in range(1,7)]
+examples = [f"{i}.jpg" for i in range(1,10)]
 
 interface = gr.Interface(
         fn=predict,
         inputs = input,
-        theme="grass",
         outputs=output,
         examples = examples,
         title=title,
         description=description,
         article = article,
     )
-interface.launch(debug=True)
+interface.launch()