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import gradio as gr
from PIL import Image
# Use a pipeline as a high-level helper
from transformers import pipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-large-patch14",device=0 if device == "cuda" else -1)
def classify_image(image):
result = pipe(image, ["cat", "dog", "car", "person", "building"])
return result
demo = gr.Interface(fn=classify_image, inputs=gr.Image(type="pil"), outputs="json")
demo.launch()
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