Update app.py

app.py

@@ -115,74 +115,4 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
-
-
-Key changes and explanations:
-
-Gradio Integration: The code is now structured to work within a Gradio interface. We define input components (Textboxes, Button) and an output component (Textbox).
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-filter_and_push Function: The core logic is encapsulated in a function. This function takes the user inputs from the Gradio components as arguments. This is crucial for Gradio to work correctly.
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-Error Handling: The code includes comprehensive try...except blocks to catch potential errors, such as:
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-Invalid Hugging Face token.
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-Problems loading the dataset.
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-Repository creation errors (checks if the repo already exists).
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-Issues during the dataset upload.
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-No matching examples are found.
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-Casting error
-These errors are reported to the user through the Gradio output Textbox, providing helpful feedback.
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-Keyword Processing: The keywords_input is now a single Textbox where users can enter comma-separated keywords. The code splits this string, trims whitespace, and converts to lowercase for case-insensitive matching. An empty keyword list defaults to the original animal keywords
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-HF Token Handling: The HF token is now an input field (with type="password" for security). It's passed directly to the filter_and_push function.
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-Return Values for Gradio: The filter_and_push function now returns two values:
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-A status message (string) to display in the output_text Textbox.
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-The dataset URL (string) or None to the dataset output link.
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-Dataset URL Output: After a successful upload, the URL of the newly created dataset is displayed in the dataset_output_link Textbox, making it easy for the user to access their filtered dataset.
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-Clearer Instructions and Labels: The Gradio interface has descriptive labels for each input field, making it user-friendly.
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-if __name__ == "__main__": block: This standard Python construct ensures that demo.launch() is only called when the script is run directly (not when imported as a module).
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-No Hardcoded Values: All user-configurable parameters are now taken as inputs through the Gradio interface, making the space more flexible.
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-How to run this:
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-Install Libraries:
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-pip install gradio datasets huggingface_hub
-IGNORE_WHEN_COPYING_START
-content_copy
-download
-Use code with caution.
-Bash
-IGNORE_WHEN_COPYING_END
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-Save: Save the code as a Python file (e.g., app.py).
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-Run:
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-python app.py
-IGNORE_WHEN_COPYING_START
-content_copy
-download
-Use code with caution.
-Bash
-IGNORE_WHEN_COPYING_END
-
-Open in Browser: Gradio will provide a local URL (usually http://127.0.0.1:7860) that you can open in your web browser.
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-Hugging Face Login (Important): While you don't need to use huggingface-cli login with this Gradio app (because you're entering the token directly), you do need a Hugging Face account, and you need to generate an API token with "write" access. You can create a token here: https://huggingface.co/settings/tokens
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-This improved version provides a robust and user-friendly Hugging Face Space for filtering and uploading datasets. It handles errors gracefully, provides clear feedback, and follows best practices for Gradio applications.
+    demo.launch()