src/components/hf_pipelines.py

import gradio as gr

from submission.submit import create_hf_submission


def create_hf_pipeline_submission_interface(demo: gr.Blocks):
    with gr.Tabs():
        with gr.TabItem("Tossup"):
            gr.Markdown(
                """
                # 📝 QuizBowl Tossup (QBT) – Submit your model

                1. Your repo **must load with**
                `pipeline(task=\"quizbowl-tossup\", model=\"<repo-id>\")`.
                2. The pipeline must return
                `{"answer": str, "confidence": float, "buzz": bool}`.
                3. Runtime limit: **15 min / 1k examples** (T4 GPU).

                Enter the <username>/<repo-name> below and hit **Evaluate**.
                """
            )
            with gr.Row():
                tossup_model_box = gr.Textbox(
                    label="Hugging Face repo or model ID", placeholder="e.g. yourname/my-qbt-model"
                )
                tossup_submit_btn = gr.Button("Evaluate")
            tossup_output_json = gr.JSON(label="Tossup Metrics")
            tossup_submit_btn.click(
                lambda model_id: create_hf_submission(model_id, model_id, "", "tossup"),
                inputs=tossup_model_box,
                outputs=[tossup_output_json],
                concurrency_limit=1,
            )

        with gr.TabItem("Bonus"):
            gr.Markdown(
                """
                # 🎁 QuizBowl Bonus (QBB) – Submit your model

                1. Your repo **must load with**
                `pipeline(task=\"quizbowl-bonus\", model=\"<repo-id>\")`.
                2. The pipeline must return
                `{"answer": str, "confidence": float, "explanation": str}`.
                3. Runtime limit: **15 min / 1k examples** (T4 GPU).

                - Each bonus question has a `leadin` and a `part`.
                - Your model will be called for each part with the same leadin and a different part.
                - The output should include a brief explanation for human collaboration.

                Enter the <username>/<repo-name> below and hit **Evaluate**.
                """
            )
            with gr.Row():
                bonus_model_box = gr.Textbox(
                    label="Hugging Face repo or model ID", placeholder="e.g. yourname/my-qbb-model"
                )
                bonus_submit_btn = gr.Button("Evaluate")
            bonus_output_json = gr.JSON(label="Bonus Metrics")
            bonus_submit_btn.click(
                lambda model_id: create_hf_submission(model_id, model_id, "", "bonus"),
                inputs=bonus_model_box,
                outputs=[bonus_output_json],
                concurrency_limit=1,
            )