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| import gradio as gr | |
| from PIL import Image | |
| from transformers import BridgeTowerForImageAndTextRetrieval, BridgeTowerProcessor | |
| model_id = "BridgeTower/bridgetower-large-itm-mlm-gaudi" | |
| processor = BridgeTowerProcessor.from_pretrained(model_id) | |
| model = BridgeTowerForImageAndTextRetrieval.from_pretrained(model_id) | |
| # Process an image | |
| def process(image, texts): | |
| scores = {} | |
| texts = texts.split(",") | |
| for text in texts: | |
| encoding = processor(image, text, return_tensors="pt") | |
| outputs = model(**encoding) | |
| scores[text] = "{:.2f}".format(outputs.logits[0, 1].item()) | |
| # sort scores in descending order | |
| scores = dict(sorted(scores.items(), key=lambda item: item[1], reverse=True)) | |
| return scores | |
| # Inputs | |
| image = gr.Image(label="Image") | |
| texts = gr.Text(label="List of comma-separated texts") | |
| # Output | |
| scores = gr.JSON(label="Scores") | |
| description = "This Space lets you score a list of texts on an image.\ | |
| This can be used to find the most relevant text for an image, or for semantic search on images." | |
| iface = gr.Interface( | |
| theme="huggingface", | |
| description=description, | |
| fn=process, | |
| inputs=[image, texts], | |
| outputs=scores, | |
| examples=[ | |
| [ | |
| "example1.jpg", | |
| "a metal band on stage, a chamber orchestra on stage, a giant rubber duck, a machine learning meetup", | |
| ], | |
| [ | |
| "example2.jpg", | |
| "medieval art, religious art, a group of angels, a movie poster", | |
| ], | |
| ], | |
| allow_flagging="never", | |
| ) | |
| iface.launch() | |