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_eu_landmarks", "coreml_classifier_path":"LandmarksEUHead_quant8.mlpackage/Data/com.apple.CoreML/efficientnetV2M21kEULandmarksHead2_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.jpg"],["test3.jpg"]]).launch()