gistfile1.txt

>>> from transformers.tools import HfAgent
>>> a = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")

>>> text = """Ukraine says Friday's missile strike on the headquarters of Russia's Black Sea fleet in Crimea was timed to coincide with a meeting of naval officials.
The fleet, based in the port city of Sevastopol, is seen as the best of Russia's navy.
A Ukrainian military source told the BBC that Friday's attack was carried out using Storm Shadow missiles, which are supplied by Britain and France."""

>>> a.run("Can you summarize `text` for me", text=text)

gistfile10.txt

https://huggingface.co/docs/transformers/tasks/zero_shot_object_detection
https://huggingface.co/docs/transformers/tasks/object_detection

gistfile11.txt

https://huggingface.co/docs/transformers/tasks/zero_shot_object_detection
https://huggingface.co/docs/transformers/tasks/object_detection

gistfile2.txt

>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> image = client.text_to_image("A penguin with chainsaw. klimt style")
>>> image.save("astronaut.png")

>>> image = client.text_to_image("A penguin with chainsaw. monet style")
>>> image.save("astronaut2.png")

image = client.text_to_image("A penguin with chainsaw. monet style")
image.save("astronaut2.png")

gistfile3.txt

pip install huggingface_hub

gistfile4.txt

>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> client.image_to_text("https://assets.webshop.nl/products/Shopping/e46e0ed50ca6a061a4fbb790cf9c742f.300x300.jpeg")

'a set of four small cups with different colors'

gistfile5.txt

client.zero_shot_image_classification('https://assets.shops.ae/products/Shopping/9259fb77287d4925f3d3763b85e1bf48.300x300.jpeg', labels=b[0:10])

gistfile6.txt

from transformers import pipeline

pipe = pipeline("image-classification", model="rizvandwiki/gender-classification")

pipe('https://assets.webshop.nl/products/Shopping/f3f97e5bfcc0d8d80229e2dff6dddb90.300x300.jpeg')
pipe(["https://assets.webshop.nl/products/Shopping/134aed2f59a9eff753a935738d550b63.300x300.jpeg", "https://assets.webshop.nl/products/Shopping/d3bd90e34b45cecc189a9954ac9dc308.300x300.jpeg"])

gistfile7.txt

KeyError: "Unknown task text-to-image, available tasks are ['audio-classification', 'automatic-speech-recognition', 'conversational', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-segmentation', 'image-to-text', 'mask-generation', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'summarization', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'text2text-generation', 'token-classification', 'translation', 'video-classification', 'visual-question-answering', 'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection', 'translation_XX_to_YY']"

gistfile8.txt

>>> from transformers import pipeline
>>> model_name = "openai/clip-vit-large-patch14-336"
>>> classifier = pipeline("zero-shot-image-classification", model = model_name)
>>> classifier('https://assets.webshop.nl/products/Shopping/7da005230f6d740348af4d923c1805c6.300x300.jpeg', candidate_labels=['male', 'female', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10'])
[{'score': 0.7134156823158264, 'label': 'male'}, {'score': 0.12635408341884613, 'label': 'female'}, {'score': 0.02425290271639824, 'label': '4'}, {'score': 0.021945219486951828, 'label': '2'}, {'score': 0.020914090797305107, 'label': '3'}, {'score': 0.01855575293302536, 'label': '5'}, {'score': 0.017798833549022675, 'label': '6'}, {'score': 0.014678528532385826, 'label': '1'}, {'score': 0.012363165616989136, 'label': '7'}, {'score': 0.01090542133897543, 'label': '8'}, {'score': 0.009844750165939331, 'label': '9'}, {'score': 0.00897147785872221, 'label': '10'}]

gistfile9.txt

https://huggingface.co/docs/transformers/training
https://huggingface.co/EZlee/e-commerce-bert-base-multilingual-cased