"""
MotionScope Pro — Streamlit front-end
Run with:  streamlit run app.py
"""

import tempfile
import os
import cv2
import numpy as np
import streamlit as st
from detector import MovementDetector, DetectionConfig, DetectionMode

# ---------------------------------------------------------------------------
# Page config
# ---------------------------------------------------------------------------
st.set_page_config(
    page_title="MotionScope Pro",
    page_icon="🎥",
    layout="wide",
    initial_sidebar_state="expanded",
)

# ---------------------------------------------------------------------------
# Custom CSS — dark, polished look
# ---------------------------------------------------------------------------
st.markdown(
    """
    <style>
    /* ---- Global ---- */
    .stApp {
        background: linear-gradient(135deg, #0f0c29, #302b63, #24243e);
    }

    /* Hero header */
    .hero {
        text-align: center;
        padding: 1.5rem 0 0.5rem;
    }
    .hero h1 {
        font-size: 2.6rem;
        background: linear-gradient(90deg, #00d2ff, #3a7bd5);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        margin-bottom: 0.2rem;
    }
    .hero p {
        color: #b0b0cc;
        font-size: 1.05rem;
    }

    /* Sidebar */
    section[data-testid="stSidebar"] {
        background: rgba(15, 12, 41, 0.95);
        border-right: 1px solid rgba(58, 123, 213, 0.3);
    }

    /* Cards */
    .metric-card {
        background: rgba(255,255,255,0.06);
        border: 1px solid rgba(255,255,255,0.08);
        border-radius: 12px;
        padding: 1rem 1.2rem;
        margin-bottom: 0.8rem;
    }
    .metric-card h3 {
        margin: 0 0 0.3rem;
        font-size: 0.95rem;
        color: #7eb8f7;
    }
    .metric-card .val {
        font-size: 1.6rem;
        font-weight: 700;
        color: #fff;
    }

    /* Feature badges */
    .badge-row {
        display: flex;
        gap: 0.6rem;
        flex-wrap: wrap;
        justify-content: center;
        margin-bottom: 1.2rem;
    }
    .badge {
        background: rgba(58, 123, 213, 0.15);
        border: 1px solid rgba(58, 123, 213, 0.35);
        border-radius: 20px;
        padding: 0.35rem 0.9rem;
        font-size: 0.82rem;
        color: #a0c4ff;
    }

    /* Hide default Streamlit branding */
    #MainMenu, footer, header {visibility: hidden;}
    </style>
    """,
    unsafe_allow_html=True,
)

# ---------------------------------------------------------------------------
# Hero header
# ---------------------------------------------------------------------------
st.markdown(
    """
    <div class="hero">
        <h1>🎥 MotionScope Pro</h1>
        <p>Advanced Movement Detection &mdash; Hand Tracking &amp; Motion Analysis</p>
    </div>
    """,
    unsafe_allow_html=True,
)

# Feature badges
st.markdown(
    """
    <div class="badge-row">
        <span class="badge">🖐️ Hand Tracking</span>
        <span class="badge">🚗 Motion Detection</span>
        <span class="badge">⚡ Combined Mode</span>
        <span class="badge">📹 Video Upload</span>
        <span class="badge">📷 Webcam Snapshots</span>
    </div>
    """,
    unsafe_allow_html=True,
)

# ---------------------------------------------------------------------------
# Sidebar — settings
# ---------------------------------------------------------------------------
with st.sidebar:
    st.markdown("## ⚙️ Detection Settings")

    mode_label = st.selectbox(
        "Detection Mode",
        options=[m.value for m in DetectionMode],
        index=0,
        help="Choose what the detector should look for.",
    )
    mode = DetectionMode(mode_label)

    st.markdown("---")
    st.markdown("### 🔧 Motion Parameters")

    motion_threshold = st.slider(
        "Motion threshold",
        min_value=50, max_value=255, value=180, step=5,
        help="Higher → less sensitive (ignores faint motion).",
    )
    min_contour_area = st.slider(
        "Min object area (px²)",
        min_value=100, max_value=10000, value=1000, step=100,
        help="Ignore contours smaller than this area.",
    )

    st.markdown("---")
    st.markdown("### 🖐️ Hand Parameters")

    max_hands = st.slider("Max hands to detect", 1, 4, 2)
    det_confidence = st.slider(
        "Detection confidence", 0.1, 1.0, 0.5, 0.05,
    )
    track_confidence = st.slider(
        "Tracking confidence", 0.1, 1.0, 0.5, 0.05,
    )

    st.markdown("---")
    st.markdown(
        "<small style='color:#666'>Built with OpenCV · MediaPipe · Streamlit</small>",
        unsafe_allow_html=True,
    )

# Build config from sidebar values
config = DetectionConfig(
    min_detection_confidence=det_confidence,
    min_tracking_confidence=track_confidence,
    max_num_hands=max_hands,
    motion_threshold=motion_threshold,
    min_contour_area=min_contour_area,
)

# ---------------------------------------------------------------------------
# Cached detector (rebuilt when config changes)
# ---------------------------------------------------------------------------

@st.cache_resource
def get_detector():
    return MovementDetector()

detector = get_detector()
detector.rebuild(config)

# ---------------------------------------------------------------------------
# Tabs — Video Upload  |  Webcam Snapshot
# ---------------------------------------------------------------------------
tab_video, tab_webcam = st.tabs(["📹 Video Upload", "📷 Webcam Snapshot"])

# ========================  VIDEO UPLOAD TAB  ==============================
with tab_video:
    uploaded = st.file_uploader(
        "Upload a video file",
        type=["mp4", "avi", "mov", "mkv"],
        help="Supported formats: MP4, AVI, MOV, MKV",
    )

    if uploaded is not None:
        # Save upload to a temp file
        tfile = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
        tfile.write(uploaded.read())
        tfile.flush()
        input_path = tfile.name

        # Show the original video
        with st.expander("🎬 Original video", expanded=False):
            st.video(input_path)

        # Process button
        if st.button("🚀 Process Video", type="primary", use_container_width=True):
            output_path = os.path.join(tempfile.gettempdir(), "motionscope_output.mp4")

            progress_bar = st.progress(0, text="Processing…")
            frame_placeholder = st.empty()
            metrics_placeholder = st.empty()

            total_objects = 0
            frame_num = 0

            try:
                for display_frame, result_path, progress in detector.process_video(
                    input_path, mode=mode, output_path=output_path,
                ):
                    if display_frame is not None:
                        frame_num += 1
                        # Show every 4th frame for speed
                        if frame_num % 4 == 0 or progress >= 1.0:
                            frame_placeholder.image(
                                display_frame,
                                caption=f"Frame {detector.frame_count}",
                                use_container_width=True,
                            )
                        progress_bar.progress(
                            progress,
                            text=f"Processing… {int(progress * 100)}%",
                        )

                    if result_path is not None:
                        progress_bar.progress(1.0, text="✅ Done!")

                        st.success(
                            f"Processed **{detector.frame_count}** frames successfully!"
                        )

                        # Metrics row
                        col1, col2, col3 = st.columns(3)
                        col1.metric("Total Frames", detector.frame_count)
                        col2.metric("Mode", mode.value)
                        col3.metric("Status", "✅ Complete")

                        # Download button
                        with open(result_path, "rb") as f:
                            st.download_button(
                                "⬇️ Download Processed Video",
                                data=f,
                                file_name="motionscope_output.mp4",
                                mime="video/mp4",
                                use_container_width=True,
                            )

            except Exception as e:
                st.error(f"❌ Error during processing: {e}")
            finally:
                # Cleanup temp input
                try:
                    os.unlink(input_path)
                except OSError:
                    pass
    else:
        # Empty state
        st.markdown(
            """
            <div style="text-align:center; padding:3rem 0; color:#888;">
                <p style="font-size:3rem; margin-bottom:0.5rem;">📹</p>
                <p>Upload a video above to get started</p>
            </div>
            """,
            unsafe_allow_html=True,
        )

# ========================  WEBCAM SNAPSHOT TAB  ===========================
with tab_webcam:
    st.markdown(
        "Take a photo with your webcam and the detector will process it instantly."
    )

    if mode == DetectionMode.MOTION_DETECTION:
        st.warning("⚠️ **Motion Detection** requires a video stream to compare frames. For a single photo, use **Hand Tracking** or **Combined** mode.")


    camera_input = st.camera_input("📷 Take a photo")

    if camera_input is not None:
        # Decode the image
        file_bytes = np.frombuffer(camera_input.getvalue(), dtype=np.uint8)
        img_bgr = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)

        if img_bgr is not None:
            # Flip for mirror effect
            img_bgr = cv2.flip(img_bgr, 1)

            # Process
            processed_bgr = detector.process_frame(img_bgr, mode)
            processed_rgb = cv2.cvtColor(processed_bgr, cv2.COLOR_BGR2RGB)

            col_orig, col_proc = st.columns(2)
            with col_orig:
                st.markdown("**Original**")
                original_rgb = cv2.cvtColor(
                    cv2.flip(img_bgr, 1), cv2.COLOR_BGR2RGB  # undo our flip for display
                )
                st.image(original_rgb, use_container_width=True)
            with col_proc:
                st.markdown("**Processed**")
                st.image(processed_rgb, use_container_width=True)

            # Download processed image
            _, buf = cv2.imencode(".jpg", processed_bgr)
            st.download_button(
                "⬇️ Download Processed Image",
                data=buf.tobytes(),
                file_name="motionscope_snapshot.jpg",
                mime="image/jpeg",
                use_container_width=True,
            )
        else:
            st.error("Could not decode the captured image.")
    else:
        st.markdown(
            """
            <div style="text-align:center; padding:3rem 0; color:#888;">
                <p style="font-size:3rem; margin-bottom:0.5rem;">📷</p>
                <p>Click the camera button above to capture a snapshot</p>
            </div>
            """,
            unsafe_allow_html=True,
        )