app.py

import os

os.system("pip install --upgrade ./transformers-5.0.0.dev0-py3-none-any.whl")
import colorsys
import gc
import os
from typing import Optional

import cv2
import gradio as gr
import numpy as np
import torch
from gradio.themes import Soft
from PIL import Image, ImageDraw, ImageFont
from transformers import Sam3TrackerVideoModel, Sam3TrackerVideoProcessor, Sam3VideoModel, Sam3VideoProcessor

def get_device_and_dtype() -> tuple[str, torch.dtype]:
    device = "cuda" if torch.cuda.is_available() else "cpu"
    dtype = torch.bfloat16
    return device, dtype


_GLOBAL_DEVICE, _GLOBAL_DTYPE = get_device_and_dtype()
_GLOBAL_MODEL_REPO_ID = "facebook/sam3"
_GLOBAL_TOKEN = os.getenv("HF_TOKEN")

_GLOBAL_TRACKER_MODEL = Sam3TrackerVideoModel.from_pretrained(
    _GLOBAL_MODEL_REPO_ID, torch_dtype=_GLOBAL_DTYPE, device_map=_GLOBAL_DEVICE
).eval()
_GLOBAL_TRACKER_PROCESSOR = Sam3TrackerVideoProcessor.from_pretrained(_GLOBAL_MODEL_REPO_ID, token=_GLOBAL_TOKEN)

_GLOBAL_TEXT_VIDEO_MODEL = Sam3VideoModel.from_pretrained(_GLOBAL_MODEL_REPO_ID, token=_GLOBAL_TOKEN)
_GLOBAL_TEXT_VIDEO_MODEL = _GLOBAL_TEXT_VIDEO_MODEL.to(_GLOBAL_DEVICE, dtype=_GLOBAL_DTYPE).eval()
_GLOBAL_TEXT_VIDEO_PROCESSOR = Sam3VideoProcessor.from_pretrained(_GLOBAL_MODEL_REPO_ID, token=_GLOBAL_TOKEN)
print("Models loaded successfully!")


def try_load_video_frames(video_path_or_url: str) -> tuple[list[Image.Image], dict]:
    cap = cv2.VideoCapture(video_path_or_url)
    frames = []
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        frames.append(Image.fromarray(frame_rgb))
    fps_val = cap.get(cv2.CAP_PROP_FPS)
    cap.release()
    info = {
        "num_frames": len(frames),
        "fps": float(fps_val) if fps_val and fps_val > 0 else None,
    }
    return frames, info


def overlay_masks_on_frame(
    frame: Image.Image,
    masks_per_object: dict[int, np.ndarray],
    color_by_obj: dict[int, tuple[int, int, int]],
    alpha: float = 0.5,
) -> Image.Image:
    base = np.array(frame).astype(np.float32) / 255.0
    height, width = base.shape[:2]
    overlay = base.copy()

    for obj_id, mask in masks_per_object.items():
        if mask is None:
            continue
        if mask.dtype != np.float32:
            mask = mask.astype(np.float32)
        if mask.ndim == 3:
            mask = mask.squeeze()
        mask = np.clip(mask, 0.0, 1.0)
        color = np.array(color_by_obj.get(obj_id, (255, 0, 0)), dtype=np.float32) / 255.0
        a = alpha
        m = mask[..., None]
        overlay = (1.0 - a * m) * overlay + (a * m) * color

    out = np.clip(overlay * 255.0, 0, 255).astype(np.uint8)
    return Image.fromarray(out)


def pastel_color_for_object(obj_id: int) -> tuple[int, int, int]:
    golden_ratio_conjugate = 0.61
    hue = (obj_id * golden_ratio_conjugate) % 1.0
    saturation = 0.45
    value = 1.0
    r_f, g_f, b_f = colorsys.hsv_to_rgb(hue, saturation, value)
    return int(r_f * 255), int(g_f * 255), int(b_f * 255)


class AppState:
    def __init__(self):
        self.reset()

    def reset(self):
        self.video_frames: list[Image.Image] = []
        self.inference_session = None
        self.video_fps: float | None = None
        self.masks_by_frame: dict[int, dict[int, np.ndarray]] = {}
        self.color_by_obj: dict[int, tuple[int, int, int]] = {}
        self.clicks_by_frame_obj: dict[int, dict[int, list[tuple[int, int, int]]]] = {}
        self.boxes_by_frame_obj: dict[int, dict[int, list[tuple[int, int, int, int]]]] = {}
        self.text_prompts_by_frame_obj: dict[int, dict[int, str]] = {}
        self.composited_frames: dict[int, Image.Image] = {}
        self.current_frame_idx: int = 0
        self.current_obj_id: int = 1
        self.current_label: str = "positive"
        self.current_clear_old: bool = True
        self.current_prompt_type: str = "Points"
        self.pending_box_start: tuple[int, int] | None = None
        self.pending_box_start_frame_idx: int | None = None
        self.pending_box_start_obj_id: int | None = None
        self.active_tab: str = "point_box"

    def __repr__(self):
        return f"AppState(video_frames={len(self.video_frames)}, video_fps={self.video_fps}, masks_by_frame={len(self.masks_by_frame)}, color_by_obj={len(self.color_by_obj)})"

    @property
    def num_frames(self) -> int:
        return len(self.video_frames)




def init_video_session(
    GLOBAL_STATE: gr.State, video: str | dict, active_tab: str = "point_box"
) -> tuple[AppState, int, int, Image.Image, str]:
    GLOBAL_STATE.video_frames = []
    GLOBAL_STATE.masks_by_frame = {}
    GLOBAL_STATE.color_by_obj = {}
    GLOBAL_STATE.text_prompts_by_frame_obj = {}
    GLOBAL_STATE.clicks_by_frame_obj = {}
    GLOBAL_STATE.boxes_by_frame_obj = {}
    GLOBAL_STATE.composited_frames = {}
    GLOBAL_STATE.inference_session = None
    GLOBAL_STATE.active_tab = active_tab

    device = _GLOBAL_DEVICE
    dtype = _GLOBAL_DTYPE

    video_path: Optional[str] = None
    if isinstance(video, dict):
        video_path = video.get("name") or video.get("path") or video.get("data")
    elif isinstance(video, str):
        video_path = video
    else:
        video_path = None

    if not video_path:
        raise gr.Error("Invalid video input.")

    frames, info = try_load_video_frames(video_path)
    if len(frames) == 0:
        raise gr.Error("No frames could be loaded from the video.")

    MAX_SECONDS = 8.0
    trimmed_note = ""
    fps_in = info.get("fps")
    max_frames_allowed = int(MAX_SECONDS * fps_in) if fps_in else len(frames)
    if len(frames) > max_frames_allowed:
        frames = frames[:max_frames_allowed]
        trimmed_note = f" (trimmed to {int(MAX_SECONDS)}s = {len(frames)} frames)"
        if isinstance(info, dict):
            info["num_frames"] = len(frames)
    GLOBAL_STATE.video_frames = frames
    GLOBAL_STATE.video_fps = float(fps_in) if fps_in else None

    raw_video = [np.array(frame) for frame in frames]

    if active_tab == "text":
        processor = _GLOBAL_TEXT_VIDEO_PROCESSOR
        GLOBAL_STATE.inference_session = processor.init_video_session(
            video=frames,
            inference_device=device,
            processing_device="cpu",
            video_storage_device="cpu",
            dtype=dtype,
        )
    else:
        processor = _GLOBAL_TRACKER_PROCESSOR
        GLOBAL_STATE.inference_session = processor.init_video_session(
            video=raw_video,
            inference_device=device,
            video_storage_device=device,
            processing_device=device,
            inference_state_device=device,
            dtype=dtype,
            max_vision_features_cache_size=1,
        )

    first_frame = frames[0]
    max_idx = len(frames) - 1
    if active_tab == "text":
        status = (
            f"Loaded {len(frames)} frames @ {GLOBAL_STATE.video_fps or 'unknown'} fps{trimmed_note}. "
            f"Device: {device}, dtype: bfloat16. Ready for text prompting."
        )
    else:
        status = (
            f"Loaded {len(frames)} frames @ {GLOBAL_STATE.video_fps or 'unknown'} fps{trimmed_note}. "
            f"Device: {device}, dtype: bfloat16. Video session initialized."
        )
    return GLOBAL_STATE, 0, max_idx, first_frame, status


def compose_frame(state: AppState, frame_idx: int) -> Image.Image:
    if state is None or state.video_frames is None or len(state.video_frames) == 0:
        return None
    frame_idx = int(np.clip(frame_idx, 0, len(state.video_frames) - 1))
    frame = state.video_frames[frame_idx]
    masks = state.masks_by_frame.get(frame_idx, {})
    out_img = frame
    if len(masks) != 0:
        out_img = overlay_masks_on_frame(out_img, masks, state.color_by_obj, alpha=0.65)

    clicks_map = state.clicks_by_frame_obj.get(frame_idx)
    if clicks_map:
        draw = ImageDraw.Draw(out_img)
        cross_half = 6
        for obj_id, pts in clicks_map.items():
            for x, y, lbl in pts:
                color = (0, 255, 0) if int(lbl) == 1 else (255, 0, 0)
                draw.line([(x - cross_half, y), (x + cross_half, y)], fill=color, width=2)
                draw.line([(x, y - cross_half), (x, y + cross_half)], fill=color, width=2)
    if (
        state.pending_box_start is not None
        and state.pending_box_start_frame_idx == frame_idx
        and state.pending_box_start_obj_id is not None
    ):
        draw = ImageDraw.Draw(out_img)
        x, y = state.pending_box_start
        cross_half = 6
        color = state.color_by_obj.get(state.pending_box_start_obj_id, (255, 255, 255))
        draw.line([(x - cross_half, y), (x + cross_half, y)], fill=color, width=2)
        draw.line([(x, y - cross_half), (x, y + cross_half)], fill=color, width=2)
    box_map = state.boxes_by_frame_obj.get(frame_idx)
    if box_map:
        draw = ImageDraw.Draw(out_img)
        for obj_id, boxes in box_map.items():
            color = state.color_by_obj.get(obj_id, (255, 255, 255))
            for x1, y1, x2, y2 in boxes:
                draw.rectangle([(x1, y1), (x2, y2)], outline=color, width=2)

    text_prompts_by_obj = {}
    for frame_texts in state.text_prompts_by_frame_obj.values():
        for obj_id, text_prompt in frame_texts.items():
            if obj_id not in text_prompts_by_obj:
                text_prompts_by_obj[obj_id] = text_prompt

    if text_prompts_by_obj and len(masks) > 0:
        draw = ImageDraw.Draw(out_img)
        font = ImageFont.load_default()

        for obj_id, text_prompt in text_prompts_by_obj.items():
            obj_mask = masks.get(obj_id)
            if obj_mask is not None:
                mask_array = np.array(obj_mask)
                if mask_array.size > 0 and np.any(mask_array):
                    rows = np.any(mask_array, axis=1)
                    cols = np.any(mask_array, axis=0)
                    if np.any(rows) and np.any(cols):
                        y_min, y_max = np.where(rows)[0][[0, -1]]
                        x_min, x_max = np.where(cols)[0][[0, -1]]
                        label_x = int(x_min)
                        label_y = int(y_min) - 20
                        label_y = max(5, label_y)

                        obj_color = state.color_by_obj.get(obj_id, (255, 255, 255))

                        # Include object ID in the label
                        label_text = f"{text_prompt} (ID: {obj_id})"
                        bbox = draw.textbbox((label_x, label_y), label_text, font=font)
                        padding = 4
                        draw.rectangle(
                            [(bbox[0] - padding, bbox[1] - padding), (bbox[2] + padding, bbox[3] + padding)],
                            fill=obj_color,
                            outline=None,
                            width=0,
                        )
                        draw.text((label_x, label_y), label_text, fill=(255, 255, 255), font=font)

    state.composited_frames[frame_idx] = out_img
    return out_img


def update_frame_display(state: AppState, frame_idx: int) -> Image.Image:
    if state is None or state.video_frames is None or len(state.video_frames) == 0:
        return None
    frame_idx = int(np.clip(frame_idx, 0, len(state.video_frames) - 1))
    cached = state.composited_frames.get(frame_idx)
    if cached is not None:
        return cached
    return compose_frame(state, frame_idx)


def _ensure_color_for_obj(state: AppState, obj_id: int):
    if obj_id not in state.color_by_obj:
        state.color_by_obj[obj_id] = pastel_color_for_object(obj_id)


def on_image_click(
    img: Image.Image | np.ndarray,
    state: AppState,
    frame_idx: int,
    obj_id: int,
    label: str,
    clear_old: bool,
    evt: gr.SelectData,
) -> Image.Image:
    if state is None or state.inference_session is None:
        return img

    model = _GLOBAL_TRACKER_MODEL
    processor = _GLOBAL_TRACKER_PROCESSOR

    x = y = None
    if evt is not None:
        try:
            if hasattr(evt, "index") and isinstance(evt.index, (list, tuple)) and len(evt.index) == 2:
                x, y = int(evt.index[0]), int(evt.index[1])
            elif hasattr(evt, "value") and isinstance(evt.value, dict) and "x" in evt.value and "y" in evt.value:
                x, y = int(evt.value["x"]), int(evt.value["y"])
        except Exception:
            x = y = None

    if x is None or y is None:
        raise gr.Error("Could not read click coordinates.")

    _ensure_color_for_obj(state, int(obj_id))
    ann_frame_idx = int(frame_idx)
    ann_obj_id = int(obj_id)

    if state.current_prompt_type == "Boxes":
        if state.pending_box_start is None:
            frame_clicks = state.clicks_by_frame_obj.setdefault(ann_frame_idx, {})
            frame_clicks[ann_obj_id] = []
            state.composited_frames.pop(ann_frame_idx, None)
            state.pending_box_start = (int(x), int(y))
            state.pending_box_start_frame_idx = ann_frame_idx
            state.pending_box_start_obj_id = ann_obj_id
            state.composited_frames.pop(ann_frame_idx, None)
            return update_frame_display(state, ann_frame_idx)
        else:
            x1, y1 = state.pending_box_start
            x2, y2 = int(x), int(y)
            state.pending_box_start = None
            state.pending_box_start_frame_idx = None
            state.pending_box_start_obj_id = None
            state.composited_frames.pop(ann_frame_idx, None)
            x_min, y_min = min(x1, x2), min(y1, y2)
            x_max, y_max = max(x1, x2), max(y1, y2)

            box = [[[x_min, y_min, x_max, y_max]]]
            processor.add_inputs_to_inference_session(
                inference_session=state.inference_session,
                frame_idx=ann_frame_idx,
                obj_ids=ann_obj_id,
                input_boxes=box,
            )

            frame_boxes = state.boxes_by_frame_obj.setdefault(ann_frame_idx, {})
            obj_boxes = frame_boxes.setdefault(ann_obj_id, [])
            obj_boxes.clear()
            obj_boxes.append((x_min, y_min, x_max, y_max))
            state.composited_frames.pop(ann_frame_idx, None)
    else:
        label_int = 1 if str(label).lower().startswith("pos") else 0

        frame_clicks = state.clicks_by_frame_obj.setdefault(ann_frame_idx, {})
        obj_clicks = frame_clicks.setdefault(ann_obj_id, [])

        if bool(clear_old):
            obj_clicks.clear()
            frame_boxes = state.boxes_by_frame_obj.setdefault(ann_frame_idx, {})
            frame_boxes[ann_obj_id] = []
            if hasattr(state.inference_session, "reset_inference_session"):
                pass

        obj_clicks.append((int(x), int(y), int(label_int)))

        points = [[[[click[0], click[1]] for click in obj_clicks]]]
        labels = [[[click[2] for click in obj_clicks]]]

        processor.add_inputs_to_inference_session(
            inference_session=state.inference_session,
            frame_idx=ann_frame_idx,
            obj_ids=ann_obj_id,
            input_points=points,
            input_labels=labels,
        )
        state.composited_frames.pop(ann_frame_idx, None)

    with torch.no_grad():
        outputs = model(
            inference_session=state.inference_session,
            frame_idx=ann_frame_idx,
        )

    out_mask_logits = processor.post_process_masks(
        [outputs.pred_masks],
        [[state.inference_session.video_height, state.inference_session.video_width]],
        binarize=False,
    )[0]

    mask_2d = (out_mask_logits[0] > 0.0).cpu().numpy()
    masks_for_frame = state.masks_by_frame.setdefault(ann_frame_idx, {})
    masks_for_frame[ann_obj_id] = mask_2d

    state.composited_frames.pop(ann_frame_idx, None)

    return update_frame_display(state, ann_frame_idx)


def on_text_prompt(
    state: AppState,
    frame_idx: int,
    text_prompt: str,
) -> tuple[Image.Image, str]:
    if state is None or state.inference_session is None:
        return None, "Upload a video and enter text prompt."

    model = _GLOBAL_TEXT_VIDEO_MODEL
    processor = _GLOBAL_TEXT_VIDEO_PROCESSOR

    if not text_prompt or not text_prompt.strip():
        return update_frame_display(state, int(frame_idx)), "Please enter a text prompt."

    frame_idx = int(np.clip(frame_idx, 0, len(state.video_frames) - 1))

    state.inference_session = processor.add_text_prompt(
        inference_session=state.inference_session,
        text=text_prompt.strip(),
    )

    masks_for_frame = state.masks_by_frame.setdefault(frame_idx, {})
    frame_texts = state.text_prompts_by_frame_obj.setdefault(int(frame_idx), {})

    num_objects = 0
    detected_obj_ids = []
    with torch.no_grad():
        for model_outputs in model.propagate_in_video_iterator(
            inference_session=state.inference_session,
            start_frame_idx=frame_idx,
            max_frame_num_to_track=1,
        ):
            processed_outputs = processor.postprocess_outputs(
                state.inference_session,
                model_outputs,
            )

            current_frame_idx = model_outputs.frame_idx
            if current_frame_idx == frame_idx:
                object_ids = processed_outputs["object_ids"]
                masks = processed_outputs["masks"]
                scores = processed_outputs["scores"]

                num_objects = len(object_ids)
                if num_objects > 0:
                    if len(scores) > 0:
                        sorted_indices = torch.argsort(scores, descending=True).cpu().tolist()
                    else:
                        sorted_indices = list(range(num_objects))

                    for mask_idx in sorted_indices:
                        current_obj_id = int(object_ids[mask_idx].item())
                        detected_obj_ids.append(current_obj_id)
                        _ensure_color_for_obj(state, current_obj_id)
                        mask_2d = masks[mask_idx].float().cpu().numpy()
                        if mask_2d.ndim == 3:
                            mask_2d = mask_2d.squeeze()
                        mask_2d = (mask_2d > 0.0).astype(np.float32)
                        masks_for_frame[current_obj_id] = mask_2d
                        frame_texts[current_obj_id] = text_prompt.strip()

    state.composited_frames.pop(frame_idx, None)

    if detected_obj_ids:
        obj_ids_str = ", ".join(map(str, detected_obj_ids))
        status = f"Processed text prompt '{text_prompt.strip()}' on frame {frame_idx}. Found {num_objects} object(s) with IDs: {obj_ids_str}."
    else:
        status = f"Processed text prompt '{text_prompt.strip()}' on frame {frame_idx}. No objects detected."
    return update_frame_display(state, int(frame_idx)), status


def propagate_masks(GLOBAL_STATE: gr.State):
    if GLOBAL_STATE is None:
        return GLOBAL_STATE, "Load a video first.", gr.update()

    if GLOBAL_STATE.active_tab != "text" and GLOBAL_STATE.inference_session is None:
        return GLOBAL_STATE, "Load a video first.", gr.update()

    total = max(1, GLOBAL_STATE.num_frames)
    processed = 0

    yield GLOBAL_STATE, f"Propagating masks: {processed}/{total}", gr.update()

    last_frame_idx = 0

    with torch.no_grad():
        if GLOBAL_STATE.active_tab == "text":
            if GLOBAL_STATE.inference_session is None:
                yield GLOBAL_STATE, "Text video model not loaded.", gr.update()
                return

            model = _GLOBAL_TEXT_VIDEO_MODEL
            processor = _GLOBAL_TEXT_VIDEO_PROCESSOR

            text_prompt_to_obj_ids = {}
            for frame_idx, frame_texts in GLOBAL_STATE.text_prompts_by_frame_obj.items():
                for obj_id, text_prompt in frame_texts.items():
                    if text_prompt not in text_prompt_to_obj_ids:
                        text_prompt_to_obj_ids[text_prompt] = []
                    if obj_id not in text_prompt_to_obj_ids[text_prompt]:
                        text_prompt_to_obj_ids[text_prompt].append(obj_id)

            for text_prompt in text_prompt_to_obj_ids:
                text_prompt_to_obj_ids[text_prompt].sort()

            if not text_prompt_to_obj_ids:
                yield GLOBAL_STATE, "No text prompts found. Please add a text prompt first.", gr.update()
                return

            for text_prompt in text_prompt_to_obj_ids.keys():
                GLOBAL_STATE.inference_session = processor.add_text_prompt(
                    inference_session=GLOBAL_STATE.inference_session,
                    text=text_prompt,
                )

            earliest_frame = (
                min(GLOBAL_STATE.text_prompts_by_frame_obj.keys()) if GLOBAL_STATE.text_prompts_by_frame_obj else 0
            )

            frames_to_track = GLOBAL_STATE.num_frames - earliest_frame

            outputs_per_frame = {}

            for model_outputs in model.propagate_in_video_iterator(
                inference_session=GLOBAL_STATE.inference_session,
                start_frame_idx=earliest_frame,
                max_frame_num_to_track=frames_to_track,
            ):
                processed_outputs = processor.postprocess_outputs(
                    GLOBAL_STATE.inference_session,
                    model_outputs,
                )
                frame_idx = model_outputs.frame_idx
                outputs_per_frame[frame_idx] = processed_outputs

                object_ids = processed_outputs["object_ids"]
                masks = processed_outputs["masks"]
                scores = processed_outputs["scores"]

                masks_for_frame = GLOBAL_STATE.masks_by_frame.setdefault(frame_idx, {})
                frame_texts = GLOBAL_STATE.text_prompts_by_frame_obj.setdefault(frame_idx, {})

                num_objects = len(object_ids)
                if num_objects > 0:
                    if len(scores) > 0:
                        sorted_indices = torch.argsort(scores, descending=True).cpu().tolist()
                    else:
                        sorted_indices = list(range(num_objects))

                    for mask_idx in sorted_indices:
                        current_obj_id = int(object_ids[mask_idx].item())
                        _ensure_color_for_obj(GLOBAL_STATE, current_obj_id)
                        mask_2d = masks[mask_idx].float().cpu().numpy()
                        if mask_2d.ndim == 3:
                            mask_2d = mask_2d.squeeze()
                        mask_2d = (mask_2d > 0.0).astype(np.float32)
                        masks_for_frame[current_obj_id] = mask_2d

                        found_prompt = None
                        for existing_frame_idx, existing_frame_texts in GLOBAL_STATE.text_prompts_by_frame_obj.items():
                            if current_obj_id in existing_frame_texts:
                                found_prompt = existing_frame_texts[current_obj_id]
                                break

                        if found_prompt is None and text_prompt_to_obj_ids:
                            found_prompt = list(text_prompt_to_obj_ids.keys())[0]

                        if found_prompt:
                            frame_texts[current_obj_id] = found_prompt

                GLOBAL_STATE.composited_frames.pop(frame_idx, None)
                last_frame_idx = frame_idx
                processed += 1
                if processed % 30 == 0 or processed == total:
                    yield GLOBAL_STATE, f"Propagating masks: {processed}/{total}", gr.update(value=frame_idx)
        else:
            if GLOBAL_STATE.inference_session is None:
                yield GLOBAL_STATE, "Tracker model not loaded.", gr.update()
                return

            model = _GLOBAL_TRACKER_MODEL
            processor = _GLOBAL_TRACKER_PROCESSOR

            for sam2_video_output in model.propagate_in_video_iterator(
                inference_session=GLOBAL_STATE.inference_session
            ):
                video_res_masks = processor.post_process_masks(
                    [sam2_video_output.pred_masks],
                    original_sizes=[
                        [GLOBAL_STATE.inference_session.video_height, GLOBAL_STATE.inference_session.video_width]
                    ],
                )[0]

                frame_idx = sam2_video_output.frame_idx
                for i, out_obj_id in enumerate(GLOBAL_STATE.inference_session.obj_ids):
                    _ensure_color_for_obj(GLOBAL_STATE, int(out_obj_id))
                    mask_2d = video_res_masks[i].cpu().numpy()
                    masks_for_frame = GLOBAL_STATE.masks_by_frame.setdefault(frame_idx, {})
                    masks_for_frame[int(out_obj_id)] = mask_2d
                    GLOBAL_STATE.composited_frames.pop(frame_idx, None)

                last_frame_idx = frame_idx
                processed += 1
                if processed % 30 == 0 or processed == total:
                    yield GLOBAL_STATE, f"Propagating masks: {processed}/{total}", gr.update(value=frame_idx)

    text = f"Propagated masks across {processed} frames."
    yield GLOBAL_STATE, text, gr.update(value=last_frame_idx)


def reset_session(GLOBAL_STATE: gr.State) -> tuple[AppState, Image.Image, int, int, str]:
    if not GLOBAL_STATE.video_frames:
        return GLOBAL_STATE, None, 0, 0, "Session reset. Load a new video."

    if GLOBAL_STATE.active_tab == "text":
        if GLOBAL_STATE.video_frames:
            processor = _GLOBAL_TEXT_VIDEO_PROCESSOR
            GLOBAL_STATE.inference_session = processor.init_video_session(
                video=GLOBAL_STATE.video_frames,
                inference_device=_GLOBAL_DEVICE,
                processing_device="cpu",
                video_storage_device="cpu",
                dtype=_GLOBAL_DTYPE,
            )
    elif GLOBAL_STATE.inference_session is not None and hasattr(
        GLOBAL_STATE.inference_session, "reset_inference_session"
    ):
        GLOBAL_STATE.inference_session.reset_inference_session()
    else:
        if GLOBAL_STATE.video_frames:
            processor = _GLOBAL_TRACKER_PROCESSOR
            raw_video = [np.array(frame) for frame in GLOBAL_STATE.video_frames]
            GLOBAL_STATE.inference_session = processor.init_video_session(
                video=raw_video,
                inference_device=_GLOBAL_DEVICE,
                video_storage_device=_GLOBAL_DEVICE,
                processing_device=_GLOBAL_DEVICE,
                inference_state_device=_GLOBAL_DEVICE,
                dtype=_GLOBAL_DTYPE,
                max_vision_features_cache_size=1,
            )

    GLOBAL_STATE.masks_by_frame.clear()
    GLOBAL_STATE.clicks_by_frame_obj.clear()
    GLOBAL_STATE.boxes_by_frame_obj.clear()
    GLOBAL_STATE.text_prompts_by_frame_obj.clear()
    GLOBAL_STATE.composited_frames.clear()
    GLOBAL_STATE.pending_box_start = None
    GLOBAL_STATE.pending_box_start_frame_idx = None
    GLOBAL_STATE.pending_box_start_obj_id = None

    gc.collect()

    current_idx = int(getattr(GLOBAL_STATE, "current_frame_idx", 0))
    current_idx = max(0, min(current_idx, GLOBAL_STATE.num_frames - 1))
    preview_img = update_frame_display(GLOBAL_STATE, current_idx)
    slider_minmax = gr.update(minimum=0, maximum=max(GLOBAL_STATE.num_frames - 1, 0), interactive=True)
    slider_value = gr.update(value=current_idx)
    status = "Session reset. Prompts cleared; video preserved."
    return GLOBAL_STATE, preview_img, slider_minmax, slider_value, status


def _on_video_change_pointbox(GLOBAL_STATE: gr.State, video):
    GLOBAL_STATE, min_idx, max_idx, first_frame, status = init_video_session(GLOBAL_STATE, video, "point_box")
    return (
        GLOBAL_STATE,
        gr.update(minimum=min_idx, maximum=max_idx, value=min_idx, interactive=True),
        first_frame,
        status,
    )


def _on_video_change_text(GLOBAL_STATE: gr.State, video):
    GLOBAL_STATE, min_idx, max_idx, first_frame, status = init_video_session(GLOBAL_STATE, video, "text")
    return (
        GLOBAL_STATE,
        gr.update(minimum=min_idx, maximum=max_idx, value=min_idx, interactive=True),
        first_frame,
        status,
    )


theme = Soft(primary_hue="blue", secondary_hue="rose", neutral_hue="slate")

with gr.Blocks(title="SAM3", theme=theme) as demo:
    GLOBAL_STATE = gr.State(AppState())

    gr.Markdown(
        """
        ### SAM3 Video Tracking · powered by Hugging Face 🤗 Transformers
        Segment and track objects across a video with SAM3 (Segment Anything 3). This demo runs the official implementation from the Hugging Face Transformers library for interactive, promptable video segmentation with point, box, and text prompts.
        """
    )

    with gr.Tabs() as main_tabs:
        with gr.Tab("Text Prompting"):
            with gr.Row():
                with gr.Column():
                    gr.Markdown(
                        """
                        **Quick start**
                        - **Load a video**: Upload your own or pick an example below.
                        - Select a frame and enter a text description to segment objects (e.g., "red car", "penguin"). The text prompt will return all the instances of the object in the frame and not specific ones (e.g. not "penguin on the left" but "penguin").
                        """
                    )
                with gr.Column():
                    gr.Markdown(
                        """
                        **Working with results**
                        - **Preview**: Use the slider to navigate frames and see the current masks.
                        - **Propagate**: Click "Propagate across video" to track all defined objects through the entire video.
                        - **Export**: Render an MP4 for smooth playback using the original video FPS.
                        """
                    )

            with gr.Row():
                with gr.Column(scale=1):
                    video_in_text = gr.Video(label="Upload video", sources=["upload", "webcam"], interactive=True)
                    load_status_text = gr.Markdown(visible=True)
                    reset_btn_text = gr.Button("Reset Session", variant="secondary")
                with gr.Column(scale=2):
                    preview_text = gr.Image(label="Preview", interactive=True)
                    with gr.Row():
                        frame_slider_text = gr.Slider(
                            label="Frame", minimum=0, maximum=0, step=1, value=0, interactive=True
                        )
                        with gr.Column(scale=0):
                            propagate_btn_text = gr.Button("Propagate across video", variant="primary")
                            propagate_status_text = gr.Markdown(visible=True)
                    with gr.Row():
                        text_prompt_input = gr.Textbox(
                            label="Text Prompt",
                            placeholder="Enter a text description (e.g., 'person', 'red car', 'short hair')",
                            lines=2,
                        )
                        text_apply_btn = gr.Button("Apply Text Prompt", variant="primary")
                    text_status = gr.Markdown(visible=True)

            with gr.Row():
                render_btn_text = gr.Button("Render MP4 for smooth playback", variant="primary")
            playback_video_text = gr.Video(label="Rendered Playback", interactive=False)

            examples_list_text = [
                [None, "./deers.mp4"],
                [None, "./penguins.mp4"],
                [None, "./foot.mp4"],
            ]
            with gr.Row():
                gr.Examples(
                    examples=examples_list_text,
                    inputs=[GLOBAL_STATE, video_in_text],
                    fn=_on_video_change_text,
                    outputs=[GLOBAL_STATE, frame_slider_text, preview_text, load_status_text],
                    label="Examples",
                    cache_examples=False,
                    examples_per_page=5,
                )

        with gr.Tab("Point/Box Prompting"):
            with gr.Row():
                with gr.Column():
                    gr.Markdown(
                        """
                        **Quick start**
                        - **Load a video**: Upload your own or pick an example below.
                        - Select an Object ID and point label (positive/negative), then click the frame to add guidance. You can add **multiple points per object** and define **multiple objects** across frames.
                        """
                    )
                with gr.Column():
                    gr.Markdown(
                        """
                        **Working with results**
                        - **Preview**: Use the slider to navigate frames and see the current masks.
                        - **Propagate**: Click "Propagate across video" to track all defined objects through the entire video.
                        - **Export**: Render an MP4 for smooth playback using the original video FPS.
                        """
                    )

            with gr.Row():
                with gr.Column(scale=1):
                    video_in_pointbox = gr.Video(label="Upload video", sources=["upload", "webcam"], interactive=True)
                    load_status_pointbox = gr.Markdown(visible=True)
                    reset_btn_pointbox = gr.Button("Reset Session", variant="secondary")
                with gr.Column(scale=2):
                    preview_pointbox = gr.Image(label="Preview", interactive=True)
                    with gr.Row():
                        frame_slider_pointbox = gr.Slider(
                            label="Frame", minimum=0, maximum=0, step=1, value=0, interactive=True
                        )
                        with gr.Column(scale=0):
                            propagate_btn_pointbox = gr.Button("Propagate across video", variant="primary")
                            propagate_status_pointbox = gr.Markdown(visible=True)

            with gr.Row():
                obj_id_inp = gr.Number(value=1, precision=0, label="Object ID", scale=0)
                label_radio = gr.Radio(choices=["positive", "negative"], value="positive", label="Point label")
                clear_old_chk = gr.Checkbox(value=False, label="Clear old inputs for this object")
                prompt_type = gr.Radio(choices=["Points", "Boxes"], value="Points", label="Prompt type")

            with gr.Row():
                render_btn_pointbox = gr.Button("Render MP4 for smooth playback", variant="primary")
            playback_video_pointbox = gr.Video(label="Rendered Playback", interactive=False)

            examples_list_pointbox = [
                [None, "./deers.mp4"],
                [None, "./penguins.mp4"],
                [None, "./foot.mp4"],
            ]
            with gr.Row():
                gr.Examples(
                    examples=examples_list_pointbox,
                    inputs=[GLOBAL_STATE, video_in_pointbox],
                    fn=_on_video_change_pointbox,
                    outputs=[GLOBAL_STATE, frame_slider_pointbox, preview_pointbox, load_status_pointbox],
                    label="Examples",
                    cache_examples=False,
                    examples_per_page=5,
                )

    video_in_pointbox.change(
        _on_video_change_pointbox,
        inputs=[GLOBAL_STATE, video_in_pointbox],
        outputs=[GLOBAL_STATE, frame_slider_pointbox, preview_pointbox, load_status_pointbox],
        show_progress=True,
    )

    def _sync_frame_idx_pointbox(state_in: AppState, idx: int):
        if state_in is not None:
            state_in.current_frame_idx = int(idx)
        return update_frame_display(state_in, int(idx))

    frame_slider_pointbox.change(
        _sync_frame_idx_pointbox,
        inputs=[GLOBAL_STATE, frame_slider_pointbox],
        outputs=preview_pointbox,
    )

    video_in_text.change(
        _on_video_change_text,
        inputs=[GLOBAL_STATE, video_in_text],
        outputs=[GLOBAL_STATE, frame_slider_text, preview_text, load_status_text],
        show_progress=True,
    )

    def _sync_frame_idx_text(state_in: AppState, idx: int):
        if state_in is not None:
            state_in.current_frame_idx = int(idx)
        return update_frame_display(state_in, int(idx))

    frame_slider_text.change(
        _sync_frame_idx_text,
        inputs=[GLOBAL_STATE, frame_slider_text],
        outputs=preview_text,
    )

    def _sync_obj_id(s: AppState, oid):
        if s is not None and oid is not None:
            s.current_obj_id = int(oid)
        return gr.update()

    obj_id_inp.change(_sync_obj_id, inputs=[GLOBAL_STATE, obj_id_inp], outputs=[])

    def _sync_label(s: AppState, lab: str):
        if s is not None and lab is not None:
            s.current_label = str(lab)
        return gr.update()

    label_radio.change(_sync_label, inputs=[GLOBAL_STATE, label_radio], outputs=[])

    def _sync_prompt_type(s: AppState, val: str):
        if s is not None and val is not None:
            s.current_prompt_type = str(val)
            s.pending_box_start = None
        is_points = str(val).lower() == "points"
        updates = [
            gr.update(visible=is_points),
            gr.update(interactive=is_points) if is_points else gr.update(value=True, interactive=False),
        ]
        return updates

    prompt_type.change(
        _sync_prompt_type,
        inputs=[GLOBAL_STATE, prompt_type],
        outputs=[label_radio, clear_old_chk],
    )

    preview_pointbox.select(
        on_image_click,
        [preview_pointbox, GLOBAL_STATE, frame_slider_pointbox, obj_id_inp, label_radio, clear_old_chk],
        preview_pointbox,
    )

    def _on_text_apply(state: AppState, frame_idx: int, text: str):
        img, status = on_text_prompt(state, frame_idx, text)
        return img, status

    text_apply_btn.click(
        _on_text_apply,
        inputs=[GLOBAL_STATE, frame_slider_text, text_prompt_input],
        outputs=[preview_text, text_status],
    )

    def _render_video(s: AppState):
        if s is None or s.num_frames == 0:
            raise gr.Error("Load a video first.")
        fps = s.video_fps if s.video_fps and s.video_fps > 0 else 12
        frames_np = []
        first = compose_frame(s, 0)
        h, w = first.size[1], first.size[0]
        for idx in range(s.num_frames):
            img = s.composited_frames.get(idx)
            if img is None:
                img = compose_frame(s, idx)
            frames_np.append(np.array(img)[:, :, ::-1])
            if (idx + 1) % 60 == 0:
                gc.collect()
        out_path = "/tmp/sam3_playback.mp4"
        try:
            fourcc = cv2.VideoWriter_fourcc(*"mp4v")
            writer = cv2.VideoWriter(out_path, fourcc, fps, (w, h))
            for fr_bgr in frames_np:
                writer.write(fr_bgr)
            writer.release()
            return out_path
        except Exception as e:
            print(f"Failed to render video with cv2: {e}")
            raise gr.Error(f"Failed to render video: {e}")

    render_btn_pointbox.click(_render_video, inputs=[GLOBAL_STATE], outputs=[playback_video_pointbox])
    render_btn_text.click(_render_video, inputs=[GLOBAL_STATE], outputs=[playback_video_text])

    propagate_btn_pointbox.click(
        propagate_masks,
        inputs=[GLOBAL_STATE],
        outputs=[GLOBAL_STATE, propagate_status_pointbox, frame_slider_pointbox],
    )

    propagate_btn_text.click(
        propagate_masks,
        inputs=[GLOBAL_STATE],
        outputs=[GLOBAL_STATE, propagate_status_text, frame_slider_text],
    )

    reset_btn_pointbox.click(
        reset_session,
        inputs=GLOBAL_STATE,
        outputs=[GLOBAL_STATE, preview_pointbox, frame_slider_pointbox, frame_slider_pointbox, load_status_pointbox],
    )

    reset_btn_text.click(
        reset_session,
        inputs=GLOBAL_STATE,
        outputs=[GLOBAL_STATE, preview_text, frame_slider_text, frame_slider_text, load_status_text],
    )


demo.queue(api_open=False).launch()