| 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_sa_landmarks", | |
| "coreml_classifier_path":"LandmarksSAHead_quant8.mlpackage/Data/com.apple.CoreML/efficientnetV2M21kSALandmarksHead_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() | |