delete

app2.py

@@ -1,50 +0,0 @@
-import torch
-import gradio as gr 
-import re 
-from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel
-
-device='cpu'
-encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-decoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-model_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
-tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
-model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
-
-def predict(image,max_length=64, num_beams=4):
-  image = image.convert('RGB')
-  image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
-  clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
-  caption_ids = model.generate(image, max_length = max_length)[0]
-  caption_text = clean_text(tokenizer.decode(caption_ids))
-  return caption_text 
-
-def set_example_image(example: list) -> dict:
-    return gr.Image.update(value=example[0])
-css = '''
-h1#title {
-  text-align: center;
-}
-h3#header {
-  text-align: center;
-}
-img#overview {
-  max-width: 800px;
-  max-height: 600px;
-}
-img#style-image {
-  max-width: 1000px;
-  max-height: 600px;
-}
-'''
-demo = gr.Blocks(css=css)
-with demo:
-  gr.Markdown('''<h1 id="title">Image Caption 🖼️</h1>''')
-  gr.Markdown('''Made by : Shreyas Dixit''')
-  with gr.Column():
-        input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)
-        output = gr.outputs.Textbox(type="auto",label="Captions")
-  btn = gr.Button("Genrate Caption")
-  btn.click(fn=predict, inputs=input, outputs=output)
-
-demo.launch()