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import gradio as gr
import os
import platform
from helper import CoreMLPipeline
force_tf = os.environ.get('FORCE_TF', False)
auth_key = os.environ.get('HF_TOKEN', True)
config = { "coreml_extractor_repoid":"crossprism/efficientnetv2-21k-fv-m",
"coreml_extractor_path":"efficientnetV2M21kExtractor.mlmodel",
"tf_extractor_repoid":"crossprism/efficientnetv2-21k-fv-m-tf",
"tf_extractor_path":"efficientnetv2-21k-fv-m",
"coreml_classifier_repoid":"crossprism/travel_na_landmarks",
"coreml_classifier_path":"LandmarksNAHead_quant8.mlpackage/Data/com.apple.CoreML/efficientnetV2M21kNALandmarksHead_quant8.mlmodel",
"activation":"softmax"
}
use_tf = force_tf or (platform.system() != 'Darwin')
helper = CoreMLPipeline(config, auth_key, use_tf)
def classify_image(image):
resized = image.resize((480,480))
return helper.classify(resized)
image = gr.Image(type='pil')
label = gr.Label(num_top_classes=3)
gr.Interface(fn=classify_image, inputs=image, outputs=label, examples = [["test1.jpg"],["test2.webp"],["test3.jpg"]]).launch()
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