Spaces:
Running
on
Zero
Running
on
Zero
run without ssr on local
Browse files
app.py
CHANGED
@@ -1,5 +1,6 @@
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import time
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print(f"Starting up: {time.strftime('%Y-%m-%d %H:%M:%S')}")
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@@ -41,8 +42,12 @@ from sklearn.neighbors import NearestNeighbors
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def is_running_in_hf_space():
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return "SPACE_ID" in os.environ
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#if is_running_in_hf_space():
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import spaces # necessary to run on Zero.
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#print(f"Spaces version: {spaces.__version__}")
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import datamapplot
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@@ -131,12 +136,18 @@ def no_op_decorator(func):
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# decorator_to_use = spaces.GPU() if is_running_in_hf_space() else no_op_decorator
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# #duration=120
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# @decorator_to_use
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@spaces.GPU(duration=4*60)
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def create_embeddings(texts_to_embedd):
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"""Create embeddings for the input texts using the loaded model."""
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return model.encode(texts_to_embedd, show_progress_bar=True, batch_size=192)
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def predict(request: gr.Request, text_input, sample_size_slider, reduce_sample_checkbox,
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sample_reduction_method, plot_time_checkbox,
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@@ -698,7 +709,10 @@ with gr.Blocks(theme=theme, css="""
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# demo.launch(server_name="0.0.0.0", server_port=7860, share=True,allowed_paths=["/static"])
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# Mount Gradio app to FastAPI
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# Run both servers
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if __name__ == "__main__":
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import time
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print(f"Starting up: {time.strftime('%Y-%m-%d %H:%M:%S')}")
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def is_running_in_hf_space():
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return "SPACE_ID" in os.environ
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if is_running_in_hf_space():
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import spaces # necessary to run on Zero.
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from spaces.zero.client import _get_token
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#if is_running_in_hf_space():
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#import spaces # necessary to run on Zero.
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#print(f"Spaces version: {spaces.__version__}")
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import datamapplot
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# decorator_to_use = spaces.GPU() if is_running_in_hf_space() else no_op_decorator
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# #duration=120
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if is_running_in_hf_space():
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@spaces.GPU(duration=4*60)
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def create_embeddings(texts_to_embedd):
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"""Create embeddings for the input texts using the loaded model."""
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return model.encode(texts_to_embedd, show_progress_bar=True, batch_size=192)
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else:
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def create_embeddings(texts_to_embedd):
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"""Create embeddings for the input texts using the loaded model."""
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return model.encode(texts_to_embedd, show_progress_bar=True, batch_size=192)
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def predict(request: gr.Request, text_input, sample_size_slider, reduce_sample_checkbox,
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sample_reduction_method, plot_time_checkbox,
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# demo.launch(server_name="0.0.0.0", server_port=7860, share=True,allowed_paths=["/static"])
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# Mount Gradio app to FastAPI
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if is_running_in_hf_space():
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app = gr.mount_gradio_app(app, demo, path="/",ssr_mode=True)
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else:
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app = gr.mount_gradio_app(app, demo, path="/",ssr_mode=False)
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# Run both servers
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if __name__ == "__main__":
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