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Update app.py
Browse files
app.py
CHANGED
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@@ -35,8 +35,8 @@ client_side = Category(
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category_id="client_side",
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title="Client Side Libraries π€",
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description="""
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These are client side libraries to easily interact or run training with models, datasets and Spaces on Hugging Face Hub
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<br
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""",
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news=[
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News(
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@@ -57,8 +57,8 @@ documentation = Category(
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category_id="documentation",
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title="Documentation π",
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description="""
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These are our documentation efforts and blogs specifically targeted for software developers to get them started with building machine learning π¦Ύ
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<br
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""",
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news=[
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News(
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@@ -91,8 +91,8 @@ use_cases = Category(
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category_id="use_cases",
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title="Use Cases",
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description="""
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These are resources compiled to demonstrate various use cases across different niches in software development.
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<br
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""",
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news=[
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News(
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@@ -117,8 +117,8 @@ cloud = Category(
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category_id="cloud",
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title="βοΈ Cloud Deployment",
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description="""
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This category includes resources on how to deploy machine learning models to cloud using various providers βοΈ
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<br
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""",
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news=[
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News(
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@@ -167,7 +167,7 @@ def space_card(space):
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def category_tab(category):
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with gr.Tab(label=category.title, elem_id="news-tab"):
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with gr.Column():
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gr.Markdown(
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with gr.Column():
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[news_card(x) for x in category.news]
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# with gr.Tab(label="Hugging Face Projects"):
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category_id="client_side",
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title="Client Side Libraries π€",
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description="""
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## These are client side libraries to easily interact or run training with models, datasets and Spaces on Hugging Face Hub
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<br>
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""",
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news=[
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News(
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category_id="documentation",
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title="Documentation π",
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description="""
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## These are our documentation efforts and blogs specifically targeted for software developers to get them started with building machine learning π¦Ύ
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<br>
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""",
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news=[
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News(
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category_id="use_cases",
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title="Use Cases",
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description="""
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## These are resources compiled to demonstrate various use cases across different niches in software development.
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<br>
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""",
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news=[
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News(
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category_id="cloud",
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title="βοΈ Cloud Deployment",
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description="""
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## This category includes resources on how to deploy machine learning models to cloud using various providers βοΈ
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<br>
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""",
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news=[
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News(
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def category_tab(category):
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with gr.Tab(label=category.title, elem_id="news-tab"):
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with gr.Column():
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gr.Markdown(category.description, elem_id="margin-top")
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with gr.Column():
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[news_card(x) for x in category.news]
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# with gr.Tab(label="Hugging Face Projects"):
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