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2d1776b
1
Parent(s):
91a155f
add markdown descriptions
Browse files
app.py
CHANGED
@@ -118,17 +118,31 @@ def predict_subset(model_id, token):
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from toolz import frequencies
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df = pd.DataFrame(
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{
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)
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return gallery, df
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with gr.Blocks() as demo:
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with gr.Tab("Random image gallery"):
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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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text = gr.Textbox(label="Search for images")
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k = gr.Slider(minimum=3, maximum=18, step=1)
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button = gr.Button("search")
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@@ -143,9 +157,14 @@ with gr.Blocks() as demo:
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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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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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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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from toolz import frequencies
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df = pd.DataFrame(
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{
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"labels": frequencies(labels).keys(),
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"freqs": frequencies(labels).values(),
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}
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)
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return gallery, df
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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 is a random image gallery.
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You can refresh the images by clicking the refresh button."""
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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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This is an 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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)
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text = gr.Textbox(label="Search for images")
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k = gr.Slider(minimum=3, maximum=18, step=1)
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button = gr.Button("search")
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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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"""## 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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)
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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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