Update app.py

app.py

@@ -2,10 +2,6 @@ import gradio as gr
 from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel
 import torch
 
-torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
-torch.hub.download_url_to_file('https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png', 'stop_sign.png')
-torch.hub.download_url_to_file('https://cdn.openai.com/dall-e-2/demos/text2im/astronaut/horse/photo/0.jpg', 'astronaut.jpg')
-
 git_processor_base = AutoProcessor.from_pretrained("microsoft/git-base-coco")
 git_model_base = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco")
 
@@ -57,7 +53,7 @@ def generate_captions(image):
     return caption_git_base, caption_git_large, caption_blip_base, caption_blip_large, caption_vitgpt
 
    
-examples = [["cats.jpg"], ["stop_sign.png"], ["astronaut.jpg"]]
+examples = [["test-1.jpg"], ["test-2.png"], ["test-3.jpg"]]
 outputs = [gr.outputs.Textbox(label="Caption generated by GIT-base"), gr.outputs.Textbox(label="Caption generated by GIT-large"), gr.outputs.Textbox(label="Caption generated by BLIP-base"), gr.outputs.Textbox(label="Caption generated by BLIP-large"), gr.outputs.Textbox(label="Caption generated by ViT+GPT-2")] 
 
 title = "Interactive demo: comparing image captioning models"