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| from transformers import pipeline | |
| from turtle import title | |
| import gradio as gr | |
| from transformers import pipeline | |
| from PIL import Image | |
| import numpy as np | |
| checkpoint = "openai/clip-vit-large-patch14-336" | |
| classifier = pipeline("zero-shot-image-classification", | |
| model=checkpoint) | |
| def shot(image, labels_text): | |
| PIL_image = Image.fromarray(np.uint8(image)).convert('RGB') | |
| labels = labels_text.split(",") | |
| res = classifier(images=PIL_image, | |
| candidate_labels=labels, | |
| hypothesis_template= "This is a photo of a {}") | |
| return {dic["label"]: dic["score"] for dic in res} | |
| interface = gr.Interface(shot, | |
| inputs=["image", "text"], | |
| outputs="label", | |
| description="Add a picture and a list of labels separated by commas", | |
| examples=[["tundra.jpg", "Ford F-150, RAM 1500, Tundra, GMC Sierra, Silverado"], | |
| ['interior.jpeg', "Interior, Exterior"], | |
| ['carplay.webp', "Carplay, Android play"] | |
| ], | |
| title="Zero-shot Image Classification") | |
| interface.launch() |