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CPU Upgrade
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Parent(s):
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update text
Browse files
app.py
CHANGED
@@ -11,6 +11,7 @@ import os
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import backoff
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from functools import lru_cache
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from huggingface_hub import list_models, ModelFilter, login
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
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@@ -127,18 +128,18 @@ def predict_subset(model_id, token):
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with gr.Blocks() as demo:
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with gr.Tab("Random
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gr.Markdown(
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This
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button = gr.Button("Refresh")
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gallery = gr.Gallery().style(grid=9, height="1400")
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button.click(return_random_sample, [], [gallery])
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with gr.Tab("
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gr.Markdown(
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You can search for images by entering a search term and clicking the search button.
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You can also change the number of images to be returned.
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This model uses the [clip-ViT-B-16](https://huggingface.co/sentence-transformers/clip-ViT-B-16) model to embed your images and search term"""
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button = gr.Button("search")
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gallery = gr.Gallery().style(grid=3)
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button.click(get_nearest_k_examples, [text, k], [gallery])
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gr.Markdown(
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# # dataset2 = dataset2.rename_column("url", "image")
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# csv = dataset2.to_csv("label_studio.csv")
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# csv_file = gr.File("label_studio.csv")
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# button.click(dataset.save_to_disk, [], [csv_file])
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with gr.Tab("predict"):
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gr.Markdown(
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You can use this to test out [image classification models](https://huggingface.co/models?pipeline_tag=image-classification) on the Hugging Face Hub
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)
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token = gr.Textbox(label="token", type="password")
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model_id = gr.Textbox(label="model_id")
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button = gr.Button("predict")
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gr.Markdown("## Results")
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plot = gr.BarPlot(x="labels", y="freqs", width=600, height=400, vertical=False)
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gallery = gr.Gallery()
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button.click(predict_subset, [model_id, token], [gallery, plot])
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demo.launch(enable_queue=True)
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import backoff
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from functools import lru_cache
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from huggingface_hub import list_models, ModelFilter, login
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import copy
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
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with gr.Blocks() as demo:
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with gr.Tab("Random Image Gallery"):
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gr.Markdown(
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"""## Random image gallery
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This tab allows you to explore images in your ARCH collection. You can refresh the images by clicking the refresh button.
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**Please note** not all images will be displayed as some images may not available via the original URLS anymore."""
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)
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button = gr.Button("Refresh")
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gallery = gr.Gallery().style(grid=9, height="1400")
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button.click(return_random_sample, [], [gallery])
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with gr.Tab("Image Search"):
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gr.Markdown(
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"""## Image search
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You can search for images by entering a search term and clicking the search button.
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You can also change the number of images to be returned.
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This model uses the [clip-ViT-B-16](https://huggingface.co/sentence-transformers/clip-ViT-B-16) model to embed your images and search term"""
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button = gr.Button("search")
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gallery = gr.Gallery().style(grid=3)
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button.click(get_nearest_k_examples, [text, k], [gallery])
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# gr.Markdown(
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# """### More info
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# ![https://raw.githubusercontent.com/UKPLab/sentence-transformers/master/docs/img/ImageSearch.png](https://raw.githubusercontent.com/UKPLab/sentence-transformers/master/docs/img/ImageSearch.png)"""
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# )
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with gr.Tab("Image Classification Model Tester"):
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gr.Markdown(
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"""## Image classification model tester
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You can use this to test out [image classification models](https://huggingface.co/models?pipeline_tag=image-classification) on the Hugging Face Hub:
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- To use this tab you will need to have a Hugging Face account and a valid token.
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- You can get a token from your [Hugging Face account page](https://huggingface.co/settings/token).
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- Input this token into the token box and then input a valid image classification model id from the Hub. For example `microsoft/resnet-50`
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This tab uses Hugging Face's [Inference API](https://huggingface.co/docs/api-inference/index) to make predictions. It will randomly select 10 images from your dataset and make predictions on them using your chosen model.
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**Please note** the predictions will take some time since the model needs to be loaded for inference first. If you make a second batch of prediction using the same model the predictions should be quicker."""
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)
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token = gr.Textbox(label="token", type="password")
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model_id = gr.Textbox(label="model_id", value="microsoft/resnet-50")
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button = gr.Button("predict")
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gr.Markdown("## Results")
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plot = gr.BarPlot(x="labels", y="freqs", width=600, height=400, vertical=False)
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gallery = gr.Gallery()
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button.click(predict_subset, [model_id, token], [gallery, plot])
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with gr.Tab("Export to Label Studio format"):
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gr.Markdown("""
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## Export to Label Studio format
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This will export the current dataset to a csv file in Label Studio format. You can then import this into Label Studio to label your images.""")
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dataset2 = copy.deepcopy(dataset)
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dataset2 = dataset2.remove_columns('image')
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dataset2 = dataset2.rename_column("url", "image")
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csv = dataset2.to_csv("label_studio.csv")
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csv_file = gr.File("label_studio.csv")
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button.click(dataset.save_to_disk, [], [csv_file])
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demo.launch(enable_queue=True)
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