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| import gradio as gr | |
| import os | |
| import torch | |
| from demos.SkinCancerClass.model import predict | |
| class_names = [ 'benign_keratosis-like_lesions','basal_cell_carcinoma','actinic_keratoses','dermatofibroma','melanocytic_Nevi'] | |
| example_names = ["actinic_keratoses","basal_cell_carcinoma","melanocytic_Nevi"] | |
| title = "Skin Cancer Classifier" | |
| description = "An ViT computer vision model to classify images from HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. <br/> List: benign_keratosis-like_lesions, basal_cell_carcinoma, actinic_keratoses, dermatofibroma, melanocytic_Nevi" | |
| article = "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T" | |
| # Create examples list from "examples/" directory | |
| example_list = [["examples/" + example, example.split('_')[0]] for example in os.listdir("examples")] | |
| # print(example_list) | |
| # result , timing = predict(example_list[0]) | |
| # | |
| # Create a single dictionary | |
| # Output the combined dictionary | |
| # print(combined_dict) | |
| # Create the Gradio demo | |
| # The output of the prediction must be in a dictionary format! | |
| demo = gr.Interface(fn=predict, # mapping function from input to output | |
| inputs=gr.Image(type="pil"), # what are the inputs? | |
| outputs=[gr.Label(num_top_classes=5, label="Predictions"), # what are the outputs? | |
| gr.Number(label="Prediction time (s)")], | |
| examples=example_list, | |
| title=title, | |
| description=description, | |
| article=article, | |
| example_labels=example_names) | |
| # Launch the demo! | |
| demo.launch(debug=False, # print errors locally? | |
| share=True) # generate a publically shareable URL? |