shared/eve_app_tabs.py

"""Tab builders for EVE-based Gradio demos.

Both tabs (Offline Inference, Live Inference) follow the same pattern:

- Outer ``gr.TabItem`` with the demo-supplied label.
- Demo-specific feature checkboxes built via a caller-provided callable so
  each demo defines its own feature set (Face Detection / Person /
  Hand Detection / Face ID / …).
- Optional ``extras_builder`` hook for demo-specific extras inside the tab
  (e.g. eve_hmi's Face ID summary thumbnails).

The tab builders return the underlying components so the caller can wire
events (``process_btn.click``, ``webrtc_stream.stream``, etc.) outside.
"""

from __future__ import annotations

from typing import Callable, TypeVar

import gradio as gr

from live_inference import RtcConfigurationInput, build_webrtc_stream
from video_processing import VideoLimits, build_video_constraints_accordion

# The exact tuple of Checkbox components is demo-specific.  We let the
# caller's checkbox-builder define the shape and return it transparently.
TCheckboxes = TypeVar("TCheckboxes")


def build_offline_inference_tab(
    *,
    feature_checkbox_builder: Callable[..., TCheckboxes],
    example_videos: list,
    video_limits: VideoLimits,
    extras_builder: Callable[[], None] | None = None,
    tab_label: str = "Offline Inference",
    feature_hint: str = "applied when processing starts",
) -> tuple[
    gr.TabItem,
    gr.Video,
    gr.Video,
    TCheckboxes,
    gr.Button,
    gr.Dataset,
]:
    """Build the Offline Inference tab. Must be called inside a ``gr.Tabs`` context.

    Args:
        feature_checkbox_builder: Callable that builds the demo's feature
            checkboxes inside the tab.  Receives the keyword ``hint`` (a
            short string shown next to the "Features" heading).  Returns a
            tuple of ``gr.Checkbox`` instances — the same tuple is returned
            unchanged so the caller can wire events.
        example_videos: Pre-loaded example videos (list of ``[path]`` rows).
        video_limits: Upload constraints rendered in the accordion.
        extras_builder: Optional callable invoked after the input/output
            video columns to add demo-specific widgets (e.g. a Face ID
            summary).  Called inside the same row, so it shares horizontal
            space with the videos.
        tab_label: Tab label text (default ``"Offline Inference"``).
        feature_hint: Short hint shown next to the Features heading.

    Returns:
        ``(tab, input_video, output_video, checkboxes, process_btn, example_dataset)``.
        ``checkboxes`` is exactly what ``feature_checkbox_builder`` returned.
    """
    with gr.TabItem(tab_label) as video_tab:
        with gr.Accordion("Instructions", open=False):
            gr.Markdown(
                "1. Select the features that will be processed on the video\n"
                "2. Select a video (or upload your own in the Input Video frame)\n"
                "3. Press the **Process Video** button\n\n"
                "Once the video has been processed, you can play the video in the "
                "Output Video frame"
            )

        checkboxes = feature_checkbox_builder(hint=feature_hint)

        with gr.Accordion("Video Examples", open=True):
            example_dataset = gr.Dataset(
                components=[gr.Video(visible=False)],
                samples=example_videos,
                show_label=False,
            )

        process_btn = gr.Button("Process Video", variant="primary", interactive=False)

        build_video_constraints_accordion(video_limits)

        with gr.Row(equal_height=True):
            with gr.Column(scale=5):
                input_video = gr.Video(label="Input Video", sources=["upload", "webcam"])
            with gr.Column(scale=5):
                output_video = gr.Video(label="Output Video")
            if extras_builder is not None:
                extras_builder()

    return video_tab, input_video, output_video, checkboxes, process_btn, example_dataset



def build_live_inference_tab(
    *,
    rtc_configuration: RtcConfigurationInput,
    feature_checkbox_builder: Callable[..., TCheckboxes],
    extras_builder: Callable[[], None] | None = None,
    max_fps: int = 15,
    width: int = 640,
    height: int = 360,
    tab_label: str = "Live Inference",
    description_html: str = (
        "<p>Use your webcam for real-time inference. "
        "Select features below, then grant camera access when prompted.</p>"
    ),
) -> tuple[gr.TabItem, object, TCheckboxes]:
    """Build the Live Inference tab with feature checkboxes + WebRTC stream.

    Args:
        rtc_configuration: ICE configuration dict, callable that returns
            one, or ``None`` for direct connection.  A callable is invoked
            per-connection by FastRTC, allowing credential refresh.
        feature_checkbox_builder: Callable that builds the demo's feature
            checkboxes (same shape as in the offline tab).
        extras_builder: Optional callable invoked after the WebRTC stream
            for demo-specific widgets (e.g. Face ID summary).
        max_fps: Maximum frame rate requested from the browser camera.
        width / height: Camera frame dimensions in pixels.
        tab_label: Tab label text.
        description_html: Optional HTML shown above the stream.

    Returns:
        ``(tab, webrtc_stream, checkboxes)``.
    """
    with gr.TabItem(tab_label) as tab:
        if description_html:
            gr.HTML(description_html)
        checkboxes = feature_checkbox_builder()
        webrtc_stream = build_webrtc_stream(
            rtc_configuration, max_fps=max_fps, width=width, height=height
        )
        if extras_builder is not None:
            extras_builder()

    return tab, webrtc_stream, checkboxes