Update app.py

app.py

@@ -5,6 +5,8 @@ import requests
 model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 vit_feature_extactor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
 tokenizer = PreTrainedTokenizerFast.from_pretrained("distilgpt2")
+
+
 #url = 'https://d2gp644kobdlm6.cloudfront.net/wp-content/uploads/2016/06/bigstock-Shocked-and-surprised-boy-on-t-113798588-300x212.jpg'
 # with Image.open(requests.get(url, stream=True).raw) as img:
 #     pixel_values = vit_feature_extactor(images=img, return_tensors="pt").pixel_values
@@ -12,12 +14,15 @@ tokenizer = PreTrainedTokenizerFast.from_pretrained("distilgpt2")
 # generated_senetences = tokenizer.batch_decode(encoder_outputs, skip_special_tokens=True,)
 # generated_senetences
 # generated_senetences[0].split(".")[0]
+
+
 def vit2distilgpt2(img):
     pixel_values = vit_feature_extactor(images=img, return_tensors="pt").pixel_values
     encoder_outputs = generated_ids = model.generate(pixel_values.to('cpu'),num_beams=5)
     generated_senetences = tokenizer.batch_decode(encoder_outputs, skip_special_tokens=True)
 
     return(generated_senetences[0].split('.')[0])
+
 import gradio as gr
 inputs = [
     gr.inputs.Image(type="pil",label="Original Images")
@@ -36,8 +41,6 @@ examples = [
 ]
 
 
-
-
 gr.Interface(
     vit2distilgpt2,
     inputs,