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| import torch | |
| import gradio as gr | |
| from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer | |
| model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| max_length = 16 | |
| num_beams = 4 | |
| gen_kwargs = {"max_length": max_length, "num_beams": num_beams} | |
| def predict_step(image_paths): | |
| images = [] | |
| for image_path in image_paths: | |
| i_image = Image.open(image_path) | |
| if i_image.mode != "RGB": | |
| i_image = i_image.convert(mode="RGB") | |
| images.append(i_image) | |
| pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values | |
| pixel_values = pixel_values.to(device) | |
| output_ids = model.generate(pixel_values, **gen_kwargs) | |
| preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) | |
| preds = [pred.strip() for pred in preds] | |
| return preds | |
| #torch.hub.download_url_to_file('https://github.com/AaronCWacker/Yggdrasil/blob/main/images/35-Favorite-Games.jpg', '35-Favorite-Games.jpg') | |
| #result = predict_step(['35-Favorite-Games.jpg']) | |
| 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 | |
| description= "NLP Image Understanding" | |
| title = "NLP Image Understanding" | |
| article = "nlpconnect vit-gpt2-image-captioning" | |
| input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True) | |
| output = gr.outputs.Textbox(type="auto",label="Captions") | |
| #examples = [['35-Favorite-Games.jpg']] | |
| examples = [f"{i}.jpg" for i in range(1,20)] | |
| interface = gr.Interface( | |
| fn=predict, | |
| inputs = input, | |
| outputs=output, | |
| examples = examples, | |
| title=title, | |
| description=description, | |
| article = article, | |
| ) | |
| interface.launch() | |