Initial commit

app.py

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+import gradio as gr
+
+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?
+"""
+
+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):
+        return f"Submitted: '{_details}' for user '{_username}'"
+
+    generate_btn.click(on_click, inputs=[details, username], outputs=[output])
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio