Delete app.py

app.py

@@ -1,49 +0,0 @@
-# Image captioning with ViT+GPT2
-from PIL import Image
-from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, PreTrainedTokenizerFast
-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
-# encoder_outputs = model.generate(pixel_values.to('cpu'),num_beams = 5)
-# 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")
-]
-
-outputs = [
-    gr.outputs.Textbox(label = "Caption")
-]
-
-title = "Image Captioning using ViT + GPT2"
-description = "ViT and GPT2 are used to generate Image Caption for the uploaded image.COCO DataSet is used for Training"
-examples = [
-    ["Image1.png"],
-    ["Image2.png"],
-    ["Image3.png"]
-]
-
-
-
-
-gr.Interface(
-    vit2distilgpt2,
-    inputs,
-    outputs,
-    title=title,
-    description=description,
-    examples=examples,
-    theme="huggingface",
-).launch(debug=True, enable_queue=True, share=True)