services/app_service/pages/bushland_beacon.py

# pages/bushland_beacon.py

import base64 
import io
import time
import queue
import threading
import tempfile
from pathlib import Path
from contextlib import contextmanager

import cv2
import numpy as np
import streamlit as st
from PIL import Image

# Torch (optional)
try:
    import torch
except Exception:
    torch = None

from utils.model_manager import get_model_manager, load_model
import os

# Choose one of these:
# A) Env var: APP_ENV=prod on your server / cloud
APP_ENV = os.getenv("natsar", "local").lower()

# B) Or secrets: put env="prod" in .streamlit/secrets.toml on your server
# APP_ENV = st.secrets.get("env", "local").lower()

IS_LOCAL = True

# … later, where you currently render your Deploy controls …
if IS_LOCAL:
    st.markdown("""
    <style>
      /* Hide the toolbar and header (which creates the black bar) */
      [data-testid="stToolbar"] { display: none !important; }
      header[data-testid="stHeader"] { display: none !important; }

      /* (Legacy fallbacks) */
      #MainMenu { visibility: hidden; }
      footer { visibility: hidden; }

      /* Remove the empty space the header leaves behind */
      [data-testid="stAppViewContainer"] { padding-top: 0 !important; }
      [data-testid="stAppViewContainer"] .main .block-container { padding-top: 0 !important; }

      /* Give the sidebar a little breathing room at the top */
      section[data-testid="stSidebar"] > div:first-child { padding-top: 0.5rem; }
    </style>
    """, unsafe_allow_html=True)



# ================== CONFIG ==================
# --- User-tunable parameters ---
DEFAULT_CONF_THRESHOLD = 0.30            # Detection confidence
DEFAULT_TARGET_SHORT_SIDE = 960         # Resize short edge (px)
DEFAULT_MAX_PREVIEW_FPS = 30             # Limit UI update frequency
DEFAULT_DROP_IF_BEHIND = False            # Drop frames if lagging
DEFAULT_PROCESS_STRIDE = 1               # Process every Nth frame (1=all)
DEFAULT_QUEUE_SIZE = 24                  # Frame queue length
DEFAULT_WRITER_CODEC = "mp4v"            # Codec to avoid OpenH264 issue
DEFAULT_TMP_EXT = ".mp4"                 # Temp file extension
DEFAULT_MAX_SLIDER_SHORT_SIDE = 1080     # Max short side slider
DEFAULT_MIN_SLIDER_SHORT_SIDE = 256      # Min short side slider
DEFAULT_MIN_FPS_SLIDER = 1               # Min preview FPS slider
DEFAULT_MAX_FPS_SLIDER = 30              # Max preview FPS slider
# ============================================




def _image_to_data_url(path: str) -> str:
    p = Path(path)
    if not p.is_absolute():
        p = Path(__file__).parent / p
    mime = "image/png" if p.suffix.lower() == ".png" else "image/jpeg"
    b64 = base64.b64encode(p.read_bytes()).decode()
    return f"data:{mime};base64,{b64}"



# ============== Session state (stop flag) ==============
if "stop_video" not in st.session_state:
    st.session_state["stop_video"] = False


# ================== Page setup ==================
st.markdown("""
<style>
.block-container { padding-top: 1.2rem; padding-bottom: 0.6rem; max-width: 1400px; }
h2, h3, h4 { margin: 0.4rem 0; }
textarea { min-height: 70px !important; }
/* Light outline on inputs */
.stSelectbox > div, .stTextInput > div, .stNumberInput > div, .stDateInput > div, .stTextArea > div {
  border: 1px solid #D0D0D0; border-radius: 6px;
}
</style>
""", unsafe_allow_html=True)


st.set_page_config(page_title="Bushland Beacon", layout="wide", initial_sidebar_state="expanded")

st.markdown(
    "<h2 style='text-align:center;margin-top:0;font-size:4.0em;color:yellow'>SAR-X<sup>ai</h2>"
    "<h2 style='text-align:center;margin-top:0'>Bushland Beacon 🚨 </h2>",
    unsafe_allow_html=True,
)

# ================== Sidebar ==================
with st.sidebar:


    logo_data_url = _image_to_data_url("../resources/images/lucid_insights_logo.png")
    st.markdown(
        f"""
        <div style="text-align:left; margin: 0 0 0.8rem 0.25rem;">
            <img src="{logo_data_url}" alt="Lucid Insights" style="width:160px; height:auto; margin-bottom:0.5rem;">
        </div>
        """,
        unsafe_allow_html=True,
    )
    st.markdown("---")
    st.page_link("app.py", label="Home")
    #st.page_link("pages/lost_at_sea.py", label="Lost at Sea")
    st.page_link("pages/signal_watch.py", label="Signal Watch")
    st.page_link("pages/bushland_beacon.py", label="Bushland Beacon")
    st.page_link("pages/misc_find.py", label="Misc Finder")
    st.markdown("---")
    st.page_link("pages/task_drone.py", label="Task Drone")
    st.page_link("pages/task_satellite.py", label="Task Satellite")
    st.page_link("pages/information.py", label="Survival Information")
    st.markdown("---")

    st.sidebar.header("Image Detection")
    img_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"], key="img_up")
    run_img = st.button("🔎 Detect", use_container_width=True)

    st.sidebar.header("Video")
    vid_file = st.file_uploader("Upload a video", type=["mp4", "mov", "avi", "mkv"], key="vid_up")

    # New buttons
    run_vid_plain = st.button("▶️ Play", use_container_width=True)
    run_vid = st.button("📽️ Detect", use_container_width=True)
    stop_vid = st.button("🛑 Stop", use_container_width=True)

    if stop_vid:
        st.session_state["stop_video"] = True

    st.sidebar.markdown("---")
    st.sidebar.header("Parameters")

    conf_thr = st.slider("Minimum confidence threshold", 0.05, 0.95, DEFAULT_CONF_THRESHOLD, 0.01)

    target_short_side = st.select_slider(
        "Target short-side (downscale)",
        options=[256, 320, 384, 448, 512, 640, 720, 800, 864, 960, 1080],
        value=DEFAULT_TARGET_SHORT_SIDE,
        help="Resize so the shorter edge equals this value. Smaller = faster."
    )

    max_preview_fps = st.slider(
        "Max preview FPS",
        min_value=DEFAULT_MIN_FPS_SLIDER,
        max_value=DEFAULT_MAX_FPS_SLIDER,
        value=DEFAULT_MAX_PREVIEW_FPS,
        help="Throttles UI updates for smoother preview."
    )

    drop_if_behind = st.toggle(
        "Drop frames if behind",
        value=DEFAULT_DROP_IF_BEHIND,
        help="Drop frames to maintain smooth preview."
    )

    process_stride = st.slider(
        "Process every Nth frame",
        min_value=1,
        max_value=5,
        value=DEFAULT_PROCESS_STRIDE,
        help="1 = every frame; higher values reuse last result."
    )

    st.sidebar.markdown("---")
    model_manager = get_model_manager()
    model_label, model_key = model_manager.render_model_selection(key_prefix="bushland_beacon")
    st.sidebar.markdown("---")
    model_manager.render_device_info()


# ================== Perf knobs for OpenCV ==================
try:
    cv2.setNumThreads(1)
except Exception:
    pass
try:
    cv2.ocl.setUseOpenCL(False)
except Exception:
    pass


# ================== Helper functions ==================
def _resize_keep_aspect(img_bgr: np.ndarray, short_side: int) -> np.ndarray:
    h, w = img_bgr.shape[:2]
    if min(h, w) == short_side:
        return img_bgr
    if h < w:
        new_h = short_side
        new_w = int(round(w * (short_side / h)))
    else:
        new_w = short_side
        new_h = int(round(h * (short_side / w)))
    return cv2.resize(img_bgr, (new_w, new_h), interpolation=cv2.INTER_AREA)


def _should_force_cpu_for_model(model_key: str) -> bool:
    return (model_key or "").lower() == "deim"


def _choose_device(model_key: str) -> str:
    if _should_force_cpu_for_model(model_key):
        return "cpu"
    if torch is not None and torch.cuda.is_available():
        return "cuda"
    return "cpu"


def _warmup_model(model, model_key: str, shape=(720, 1280, 3), conf: float = 0.25):
    dummy = np.zeros(shape, dtype=np.uint8)
    try:
        if (model_key or "").lower() == "deim":
            pil = Image.fromarray(cv2.cvtColor(dummy, cv2.COLOR_BGR2RGB))
            model.predict_image(pil, min_confidence=conf)
        else:
            model.predict_and_visualize(dummy, min_confidence=conf, show_score=False)
    except Exception:
        pass


@contextmanager
def maybe_autocast(enabled: bool):
    if enabled and torch is not None and torch.cuda.is_available():
        with torch.cuda.amp.autocast():
            yield
    else:
        yield


def _device_hint() -> str:
    if torch is None:
        return "cpu"
    return "cuda" if torch.cuda.is_available() else "cpu"


# ================== Passthrough (no model, no boxes) ==================
def run_video_passthrough(
    vid_bytes: bytes,
    target_short_side: int = DEFAULT_TARGET_SHORT_SIDE,
    max_preview_fps: int = DEFAULT_MAX_PREVIEW_FPS,
    drop_if_behind: bool = DEFAULT_DROP_IF_BEHIND,
):
    """Play the uploaded video with scaling & pacing only (no inference, no overlays)."""
    ts = int(time.time() * 1000)
    tmp_in = Path(tempfile.gettempdir()) / f"in_{ts}{DEFAULT_TMP_EXT}"
    with open(tmp_in, "wb") as f:
        f.write(vid_bytes)

    cap = cv2.VideoCapture(str(tmp_in), cv2.CAP_FFMPEG)
    if not cap.isOpened():
        st.error("Failed to open the uploaded video.")
        return

    try:
        cap.set(cv2.CAP_PROP_BUFFERSIZE, 2)
    except Exception:
        pass

    src_fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0)

    # UI placeholders
    frame_ph = st.empty()
    info_ph = st.empty()
    prog = st.progress(0.0, text="Preparing…")

    # Reader thread -> queue
    q: "queue.Queue[tuple[int, np.ndarray] | None]" = queue.Queue(maxsize=DEFAULT_QUEUE_SIZE)

    def reader():
        idx = 0
        while True:
            if st.session_state.get("stop_video", False):
                break
            ok, frm = cap.read()
            if not ok:
                break
            if drop_if_behind and q.full():
                try:
                    q.get_nowait()
                except queue.Empty:
                    pass
            try:
                q.put((idx, frm), timeout=0.05)
            except queue.Full:
                pass
            idx += 1
        q.put(None)

    reader_th = threading.Thread(target=reader, daemon=True)
    reader_th.start()

    # Writer (optional export)
    tmp_out = Path(tempfile.gettempdir()) / f"out_{ts}{DEFAULT_TMP_EXT}"
    writer = None

    # Pacing and preview throttle
    min_preview_interval = 1.0 / float(max_preview_fps)
    last_preview_ts = 0.0
    frame_interval = 1.0 / float(src_fps if src_fps > 0 else 25.0)
    next_write_ts = time.perf_counter() + frame_interval

    frames_done = 0
    t0 = time.perf_counter()

    try:
        with st.spinner("Playing video…"):
            while True:
                if st.session_state.get("stop_video", False):
                    break

                item = q.get()
                if item is None:
                    break
                idx, frame_bgr = item

                # Downscale for speed/preview
                vis_bgr = _resize_keep_aspect(frame_bgr, short_side=target_short_side)

                # Init writer lazily
                if writer is None:
                    H, W = vis_bgr.shape[:2]
                    fourcc = cv2.VideoWriter_fourcc(*DEFAULT_WRITER_CODEC)
                    writer = cv2.VideoWriter(str(tmp_out), fourcc, src_fps, (W, H))

                # Pace writing to match source
                now = time.perf_counter()
                if now < next_write_ts:
                    time.sleep(max(0.0, next_write_ts - now))
                writer.write(vis_bgr)
                next_write_ts += frame_interval

                frames_done += 1

                # UI updates (throttled)
                now = time.perf_counter()
                if (now - last_preview_ts) >= min_preview_interval:
                    frame_ph.image(
                        cv2.cvtColor(vis_bgr, cv2.COLOR_BGR2RGB),
                        use_container_width=True,
                        output_format="JPEG",
                        channels="RGB",
                    )
                    elapsed = now - t0
                    fps_est = frames_done / max(elapsed, 1e-6)
                    info_ph.info(
                        f"Frames: {frames_done}/{total_frames or '?'}  •  "
                        f"Throughput: {fps_est:.1f} FPS  •  Source FPS: {src_fps:.1f}  •  "
                        f"Mode: Passthrough"
                    )
                    last_preview_ts = now

                # Progress
                progress = ((idx + 1) / total_frames) if total_frames > 0 else min(frames_done / (frames_done + 30), 0.99)
                prog.progress(progress, text=f"Playing frame {idx + 1}{'/' + str(total_frames) if total_frames>0 else ''}…")

    except Exception as exc:
        st.error(f"Video playback failed: {exc}")
        return
    finally:
        try:
            cap.release()
            if writer is not None:
                writer.release()
        except Exception:
            pass

    # Reset stop flag after finishing
    st.session_state["stop_video"] = False

    st.success("Done!")
    if tmp_out.exists():
        st.video(str(tmp_out))
        with open(tmp_out, "rb") as f:
            st.download_button(
                "Download video",
                data=f.read(),
                file_name=tmp_out.name,
                mime="video/mp4",
            )
    else:
        st.error("Playback completed but output file was not created.")


# ================== Detection routines ==================
def run_image_detection(uploaded_file, conf_thr: float = 0.5, model_key: str = "deim"):
    try:
        data = uploaded_file.getvalue()
        img = Image.open(io.BytesIO(data)).convert("RGB")
        st.image(img, caption="Uploaded Image", use_container_width=True)
    except Exception as e:
        st.error(f"Error loading image: {e}")
        return

    try:
        model = load_model(model_key)
        device = _choose_device(model_key)
        if torch is not None:
            try:
                model.to(device)
            except Exception:
                pass

        _warmup_model(model, model_key=model_key, shape=(img.height, img.width, 3), conf=conf_thr)
        use_amp = (device == "cuda") and not _should_force_cpu_for_model(model_key)

        with st.spinner(f"Running detection on {device.upper()}…"):
            with maybe_autocast(use_amp):
                if (model_key or "").lower() == "deim":
                    annotated = model.predict_image(img, min_confidence=conf_thr)
                else:
                    try:
                        annotated = model.predict_image(img, min_confidence=conf_thr)
                    except Exception:
                        np_bgr = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
                        _, vis = model.predict_and_visualize(np_bgr, min_confidence=conf_thr, show_score=True)
                        annotated = Image.fromarray(cv2.cvtColor(vis, cv2.COLOR_BGR2RGB))

        st.subheader("🎯 Detection Results")
        st.image(annotated, caption="Detections", use_container_width=True)

        if _should_force_cpu_for_model(model_key):
            st.info("DEIM runs on CPU to avoid TorchScript device mismatch.")

    except Exception as e:
        st.error(f"Error during detection: {e}")


def run_video_detection(
    vid_bytes: bytes,
    conf_thr: float = 0.5,
    model_key: str = "deim",
    target_short_side: int = DEFAULT_TARGET_SHORT_SIDE,
    max_preview_fps: int = DEFAULT_MAX_PREVIEW_FPS,
    drop_if_behind: bool = DEFAULT_DROP_IF_BEHIND,
    process_stride: int = DEFAULT_PROCESS_STRIDE,
):
    # Save upload to a temp file
    ts = int(time.time() * 1000)
    tmp_in = Path(tempfile.gettempdir()) / f"in_{ts}{DEFAULT_TMP_EXT}"
    with open(tmp_in, "wb") as f:
        f.write(vid_bytes)

    # Load model & choose device
    model = load_model(model_key)
    device = _choose_device(model_key)
    if torch is not None:
        try:
            model.to(device)
        except Exception:
            pass

    # Capture
    cap = cv2.VideoCapture(str(tmp_in), cv2.CAP_FFMPEG)
    if not cap.isOpened():
        st.error("Failed to open the uploaded video.")
        return

    try:
        cap.set(cv2.CAP_PROP_BUFFERSIZE, 2)
    except Exception:
        pass

    src_fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) or 0)
    src_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    src_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    frame_ph = st.empty()
    info_ph = st.empty()
    prog = st.progress(0.0, text="Preparing…")

    _warmup_model(model, model_key=model_key, shape=(min(src_h, src_w), max(src_h, src_w), 3), conf=conf_thr)

    # Reader thread -> bounded queue
    q: "queue.Queue[tuple[int, np.ndarray] | None]" = queue.Queue(maxsize=DEFAULT_QUEUE_SIZE)

    def reader():
        idx = 0
        while True:
            if st.session_state.get("stop_video", False):
                break
            ok, frm = cap.read()
            if not ok:
                break
            if drop_if_behind and q.full():
                # drop the oldest frame to keep things moving
                try:
                    q.get_nowait()
                except queue.Empty:
                    pass
            try:
                q.put((idx, frm), timeout=0.05)
            except queue.Full:
                pass
            idx += 1
        q.put(None)

    reader_th = threading.Thread(target=reader, daemon=True)
    reader_th.start()

    tmp_out = Path(tempfile.gettempdir()) / f"out_{ts}{DEFAULT_TMP_EXT}"
    writer = None

    # Preview throttle
    min_preview_interval = 1.0 / float(max_preview_fps)
    last_preview_ts = 0.0

    # Source pacing
    frame_interval = 1.0 / float(src_fps if src_fps > 0 else 25.0)
    next_write_ts = time.perf_counter() + frame_interval

    frames_done = 0
    t0 = time.perf_counter()
    use_amp = (device == "cuda") and not _should_force_cpu_for_model(model_key)

    last_vis_bgr = None  # for stride reuse

    try:
        with st.spinner(f"Processing video on {device.upper()} with live preview…"):
            while True:
                if st.session_state.get("stop_video", False):
                    break

                item = q.get()
                if item is None:
                    break
                idx, frame_bgr = item

                # Downscale for speed
                proc_bgr = _resize_keep_aspect(frame_bgr, short_side=target_short_side)

                run_infer = (process_stride <= 1) or ((idx % process_stride) == 0)

                if run_infer:
                    # Run model
                    if (model_key or "").lower() == "deim":
                        img_rgb = cv2.cvtColor(proc_bgr, cv2.COLOR_BGR2RGB)
                        pil_img = Image.fromarray(img_rgb)
                        annotated_pil = model.predict_image(pil_img, min_confidence=conf_thr)
                        vis_bgr = cv2.cvtColor(np.array(annotated_pil), cv2.COLOR_RGB2BGR)
                    else:
                        with maybe_autocast(use_amp):
                            try:
                                _, vis_bgr = model.predict_and_visualize(
                                    proc_bgr, min_confidence=conf_thr, show_score=True
                                )
                            except Exception:
                                pil = Image.fromarray(cv2.cvtColor(proc_bgr, cv2.COLOR_BGR2RGB))
                                annotated = model.predict_image(pil, min_confidence=conf_thr)
                                vis_bgr = cv2.cvtColor(np.array(annotated), cv2.COLOR_RGB2BGR)
                    last_vis_bgr = vis_bgr
                else:
                    # Reuse last visualised frame to avoid visible “skips”
                    vis_bgr = last_vis_bgr if last_vis_bgr is not None else proc_bgr

                # Init writer when first output frame is ready
                if writer is None:
                    H, W = vis_bgr.shape[:2]
                    fourcc = cv2.VideoWriter_fourcc(*DEFAULT_WRITER_CODEC)  # avoids OpenH264 issues
                    out_fps = src_fps  # preserve source FPS in output
                    writer = cv2.VideoWriter(str(tmp_out), fourcc, out_fps, (W, H))

                # Pace writing to match the source timeline
                now = time.perf_counter()
                if now < next_write_ts:
                    time.sleep(max(0.0, next_write_ts - now))
                writer.write(vis_bgr)
                next_write_ts += frame_interval

                frames_done += 1

                # UI updates (throttled)
                now = time.perf_counter()
                if (now - last_preview_ts) >= min_preview_interval:
                    frame_ph.image(
                        cv2.cvtColor(vis_bgr, cv2.COLOR_BGR2RGB),
                        use_container_width=True,
                        output_format="JPEG",
                        channels="RGB",
                    )
                    elapsed = now - t0
                    fps_est = frames_done / max(elapsed, 1e-6)
                    device_msg = f"{device.upper()}" if device != "cuda" else f"{device.upper()} ({_device_hint().upper()})"
                    info_text = (
                        f"Processed: {frames_done} / {total_frames if total_frames>0 else '?'}  •  "
                        f"Throughput: {fps_est:.1f} FPS  •  "
                        f"Source FPS: {src_fps:.1f}  •  Device: {device_msg}  •  "
                        f"Stride: {process_stride}x"
                    )
                    if _should_force_cpu_for_model(model_key):
                        info_text += "  •  Note: DEIM forced to CPU."
                    info_ph.info(info_text)
                    last_preview_ts = now

                # Progress bar
                progress = ((idx + 1) / total_frames) if total_frames > 0 else min(frames_done / (frames_done + 30), 0.99)
                prog.progress(progress, text=f"Processing frame {idx + 1}{'/' + str(total_frames) if total_frames>0 else ''}…")

    except Exception as exc:
        st.error(f"Video detection failed: {exc}")
        return
    finally:
        try:
            cap.release()
            if writer is not None:
                writer.release()
        except Exception:
            pass

    # Reset stop flag after finishing
    st.session_state["stop_video"] = False

    st.success("Done!")

    if tmp_out.exists():
        st.video(str(tmp_out))
        with open(tmp_out, "rb") as f:
            st.download_button(
                "Download processed video",
                data=f.read(),
                file_name=tmp_out.name,
                mime="video/mp4",
            )
    else:
        st.error("Video processing completed but output file was not created.")


# ================== Main Actions ==================
if run_img:
    if img_file is None:
        st.warning("Please upload an image first.")
    else:
        run_image_detection(img_file, conf_thr=conf_thr, model_key=model_key)

# New: Passthrough mode
if run_vid_plain:
    if vid_file is None:
        st.warning("Please upload a video first.")
    else:
        st.session_state["stop_video"] = False
        run_video_passthrough(
            vid_bytes=vid_file.read(),
            target_short_side=target_short_side,
            max_preview_fps=max_preview_fps,
            drop_if_behind=drop_if_behind,
        )

# Original: Detection mode
if run_vid:
    if vid_file is None:
        st.warning("Please upload a video first.")
    else:
        st.session_state["stop_video"] = False
        run_video_detection(
            vid_bytes=vid_file.read(),
            conf_thr=conf_thr,
            model_key=model_key,
            target_short_side=target_short_side,
            max_preview_fps=max_preview_fps,
            drop_if_behind=drop_if_behind,
            process_stride=process_stride,
        )