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from turtle import title
from transformers import pipeline
import numpy as np
from PIL import Image
import gradio as gr
#model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
#tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
#tokenizer.src_lang = "en"
#encodedText = tokenizer(labels_text, return_tensors="pt")
#generatedTokens = model.generate(**encodedText, forced_bos_token_id=tokenizer.get_lang_id("ru"))
#return tokenizer.batch_decode(generatedTokens, skip_special_tokens=True)[0]
pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32")
images="dog.jpg"
def shot(image, labels_text):
PIL_image = Image.fromarray(np.uint8(image)).convert('RGB')
labels = labels_text.split(",")
# Translate
res = pipe(images=PIL_image,
candidate_labels=labels,
hypothesis_template= "This is a photo of a {}")
return {dic["label"]: dic["score"] for dic in res}
iface = gr.Interface(shot,
["image", "text"],
"label",
examples=[["dog.jpg", "dog,cat,bird"]],
description="Add a picture and a list of labels separated by commas",
title="Zero-shot Image Classification")
iface.launch() |