| import torch |
| import re |
| import gradio as gr |
| 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 |
|
|
|
|
|
|
| input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True) |
| output = gr.outputs.Textbox(type="auto",label="Captions") |
| examples = [f"example{i}.jpg" for i in range(1,7)] |
|
|
| title = "Image Captioning " |
|
|
| interface = gr.Interface( |
| fn=predict, |
| inputs = input, |
| css="footer {visibility: hidden}", |
| theme="grass", |
| outputs=output, |
| examples = examples, |
| title=title, |
| ) |
| interface.launch(debug=True) |
