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Runtime error
| import math | |
| import numpy as np | |
| import pandas as pd | |
| import gradio as gr | |
| from huggingface_hub import from_pretrained_fastai | |
| from fastai.vision.all import * | |
| def get_x(x): | |
| return pascal_source/"train"/f'{x[0]}' | |
| def get_y(x): | |
| return x[1].split(' ') | |
| pascal_source = '.' | |
| EXAMPLES_PATH = Path('./examples') | |
| repo_id = "hugginglearners/multi-object-classification" | |
| learner = from_pretrained_fastai(repo_id) | |
| labels = learner.dls.vocab | |
| def infer(img): | |
| img = PILImage.create(img) | |
| _pred, _pred_w_idx, probs = learner.predict(img) | |
| # gradio doesn't support tensors, so converting to float | |
| labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)} | |
| return labels_probs | |
| # return f"This grapevine leave is {_pred} with {100*probs[torch.argmax(probs)].item():.2f}% probability" | |
| # get the inputs | |
| inputs = gr.inputs.Image(shape=(192, 192)) | |
| # the app outputs two segmented images | |
| output = gr.outputs.Label(num_top_classes=3) | |
| # it's good practice to pass examples, description and a title to guide users | |
| title = 'Multilabel Image classification' | |
| description = 'Detect which type of object appearing in the image' | |
| article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. " | |
| examples = [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()] | |
| gr.Interface(infer, inputs, output, examples= examples, allow_flagging='never', | |
| title=title, description=description, article=article, live=False).launch(enable_queue=True, debug=False, inbrowser=False) | |