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| import json | |
| import os | |
| from io import BytesIO | |
| import gradio as gr | |
| from huggingface_hub import upload_file | |
| default_question = """ | |
| We're going to use the <a href="https://huggingface.co/datasets/wikitext"><code>wikitext (link)</a></code> dataset with the <code><a href="https://huggingface.co/bert-base-cased?">bert-base-cased (link)</a></code> model checkpoint. | |
| <br/><br/> | |
| Start by loading the <code>wikitext-2-raw-v1</code> version of that dataset, and take the 11th example (index 10) of the <code>train</code> split.<br/> | |
| We'll tokenize this using the appropriate tokenizer, and we'll mask the sixth token (index 5) the sequence. | |
| <br/><br/> | |
| When using the <code>bert-base-cased</code> checkpoint to unmask that token, what is the most probable prediction? | |
| <br/> | |
| <br/> | |
| Tips: | |
| <br/> | |
| - You might find the <a href="https://huggingface.co/docs/transformers/index">transformers docs (link)</a> useful. | |
| <br/> | |
| - You might find the <a href="https://huggingface.co/docs/datasets/index">datasets docs (link)</a> useful. | |
| """ | |
| internships = { | |
| 'Accelerate': default_question, | |
| 'Diffusion distillation': default_question, | |
| 'Skops & Scikit-Learn': default_question, | |
| "Code Generation": default_question, | |
| "Document AI Democratization": default_question, | |
| "Evaluate": default_question, | |
| "ASR": default_question, | |
| "Efficient video pretraining": default_question, | |
| "Embodied AI": default_question, | |
| "Emergence of scene and text understanding": default_question, | |
| "Everything is multimodal": default_question, | |
| "Everything is vision": default_question, | |
| "Retrieval augmentation as prompting": default_question, | |
| "Social impact evaluations": default_question, | |
| "Toolkit for detecting distribution shift": default_question, | |
| "AI Art Tooling Residency": default_question, | |
| "Gradio as an ecosystem": default_question, | |
| } | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| # Internship introduction | |
| Please select the internship you would like to apply to and answer the question asked in the Answer box. | |
| """ | |
| ) | |
| internship_choice = gr.Dropdown(label='Internship', choices=list(internships.keys())) | |
| with gr.Column(visible=False) as details_col: | |
| summary = gr.HTML(label='Question') | |
| details = gr.Textbox(label="Answer") | |
| username = gr.Textbox(label="Hugging Face Username") | |
| generate_btn = gr.Button("Submit") | |
| output = gr.Label() | |
| def filter_species(species): | |
| return gr.Label.update( | |
| internships[species] | |
| ), gr.update(visible=True) | |
| internship_choice.change(filter_species, internship_choice, [summary, details_col]) | |
| def on_click(_details, _username, _internship_choice): | |
| response = {'response': _details, "internship": _internship_choice} | |
| upload_file( | |
| path_or_fileobj=BytesIO(bytes(json.dumps(response), 'utf-8')), | |
| path_in_repo=_username, | |
| repo_id='lysandre/internships', | |
| repo_type='dataset', | |
| token=os.environ['HF_TOKEN'] | |
| ) | |
| return f"Submitted: '{_details}' for user '{_username}'" | |
| generate_btn.click(on_click, inputs=[details, username, internship_choice], outputs=[output]) | |
| if __name__ == "__main__": | |
| demo.launch() |