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; } ''' examples = [f"example{i}.jpg" for i in range(1,7)] demo = gr.Blocks(css=css) with demo: gr.Markdown('''

Image Captioning 🖼️

''') with gr.Column(): input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True) output = gr.outputs.Textbox(type="auto",label="Captions") examples = examples btn = gr.Button("Generate Caption") btn.click(fn=predict, inputs=input, outputs=output) demo.launch()