File size: 853 Bytes
9da5b91 758c978 70a751e 758c978 70a751e 758c978 406e1b2 758c978 01d090f 70a751e 406e1b2 758c978 70a751e 758c978 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 |
import gradio as gr
from transformers import ViTFeatureExtractor, ViTForImageClassification
from hugsvision.inference.VisionClassifierInference import VisionClassifierInference
# Load the pre-trained ViT model
path = "mrm8488/vit-base-patch16-224_finetuned-kvasirv2-colonoscopy"
classifier = VisionClassifierInference(
feature_extractor=ViTFeatureExtractor.from_pretrained(path),
model=ViTForImageClassification.from_pretrained(path),
)
# Define a Gradio interface
def classify_image(img):
label = classifier.predict(img_path=img)
return f"Predicted class: {label}"
iface = gr.Interface(
fn=classify_image,
inputs=gr.inputs.Image(type="file", label="Upload an image"),
outputs="text",
title="Image Classifier",
description="Classify images using a pre-trained ViT model",
)
# Launch the Gradio app
iface.launch()
|