import math
import random
import json
import os
import numpy as np
import gradio as gr

# ============================================================
# RFT Predator Space — First-Person Observer View (Pseudo-3D)
# FIXES:
#  1) NO flashing progression panel (do NOT update it on timer ticks)
#  2) AutoRun works in first-person POV (autopilot moves current POV agent)
#  3) queue() enabled for reliable timer updates on Spaces
# ============================================================

# -----------------------------
# View config (render)
# -----------------------------
VIEW_W, VIEW_H = 560, 360
RAY_W = 280
FOV_DEG = 78
MAX_DEPTH = 18

MOVE_STEP = 1
AUTO_TICK_HZ = 8

SKY = np.array([14, 16, 26], dtype=np.uint8)
FLOOR_NEAR = np.array([20, 22, 34], dtype=np.uint8)
FLOOR_FAR = np.array([10, 11, 18], dtype=np.uint8)

WALL_BASE = np.array([210, 210, 225], dtype=np.uint8)
WALL_SIDE = np.array([150, 150, 170], dtype=np.uint8)

AGENT_OTHER_COLOR = np.array([255, 140, 90], dtype=np.uint8)   # billboard for the "other" observer
RETICLE = np.array([120, 190, 255], dtype=np.uint8)

# 0=E,1=S,2=W,3=N
DIRS = [(1,0),(0,1),(-1,0),(0,-1)]
ORI_DEG = [0, 90, 180, 270]
DIR_TO_ORI = {(1,0):0, (0,1):1, (-1,0):2, (0,-1):3}

# -----------------------------
# Progression / unlocks
# -----------------------------
MAP_UNLOCKS = [
    ("Training Bay", 0),
    ("Arena+", 1),
    ("Corridor Maze", 3),
    ("Rooms", 6),
    ("Labyrinth", 10),
    ("Dense Field", 15),
]

# -----------------------------
# Saves
# -----------------------------
SAVE_DIR = "saves"
os.makedirs(SAVE_DIR, exist_ok=True)

def _slot_path(slot: str) -> str:
    slot = (slot or "slot1").strip().replace(" ", "_")
    if not slot:
        slot = "slot1"
    if not slot.lower().endswith(".json"):
        slot += ".json"
    return os.path.join(SAVE_DIR, slot)

def list_save_slots():
    try:
        files = [fn for fn in os.listdir(SAVE_DIR) if fn.lower().endswith(".json")]
        files.sort()
        return files
    except Exception:
        return []

# -----------------------------
# Utility
# -----------------------------
def clamp(x, lo, hi):
    return lo if x < lo else hi if x > hi else x

def angle_diff_rad(a, b):
    return (a - b + math.pi) % (2*math.pi) - math.pi

def seeded_rng(seed: int):
    return random.Random(int(seed) & 0xFFFFFFFF)

def neighbors4(x, y):
    return [(x+1,y),(x-1,y),(x,y+1),(x,y-1)]

def bfs_reachable(grid, start):
    H, W = grid.shape
    sx, sy = start
    if grid[sy, sx] == 1:
        return set()
    q = [(sx, sy)]
    seen = set([(sx, sy)])
    while q:
        x, y = q.pop(0)
        for nx, ny in neighbors4(x, y):
            if 0 <= nx < W and 0 <= ny < H and (nx, ny) not in seen and grid[ny, nx] == 0:
                seen.add((nx, ny))
                q.append((nx, ny))
    return seen

def pick_spawn_pair(grid, rng, min_dist=8):
    H, W = grid.shape
    empties = [(x, y) for y in range(1, H-1) for x in range(1, W-1) if grid[y, x] == 0]
    rng.shuffle(empties)
    for pred in empties[:800]:
        reach = bfs_reachable(grid, pred)
        if len(reach) < 30:
            continue
        candidates = [p for p in reach if (p[0]-pred[0])**2 + (p[1]-pred[1])**2 >= min_dist*min_dist]
        if candidates:
            prey = rng.choice(candidates)
            return pred, prey
    pred = empties[0] if empties else (1, 1)
    prey = empties[-1] if len(empties) > 1 else (2, 2)
    return pred, prey

def add_border_walls(grid):
    H, W = grid.shape
    grid[0, :] = 1
    grid[H-1, :] = 1
    grid[:, 0] = 1
    grid[:, W-1] = 1
    return grid

def compute_unlocks(catches: int):
    unlocked = set()
    for name, need in MAP_UNLOCKS:
        if catches >= need:
            unlocked.add(name)
    return unlocked

# -----------------------------
# Map generators (deterministic by seed)
# -----------------------------
def map_training(seed, w=23, h=23):
    rng = seeded_rng(seed)
    grid = np.zeros((h, w), dtype=np.int8)
    add_border_walls(grid)
    for y in range(2, h-2):
        for x in range(2, w-2):
            if rng.random() < 0.08:
                grid[y, x] = 1
    return grid

def map_arena_plus(seed, w=23, h=23):
    rng = seeded_rng(seed)
    grid = np.zeros((h, w), dtype=np.int8)
    add_border_walls(grid)
    cx, cy = w//2, h//2
    for y in range(1, h-1):
        for x in range(1, w-1):
            r2 = (x-cx)**2 + (y-cy)**2
            if 36 <= r2 <= 44 and rng.random() < 0.85:
                grid[y, x] = 1
    for _ in range(8):
        x = rng.randint(3, w-4)
        y = rng.randint(3, h-4)
        grid[y, x] = 1
    return grid

def map_corridor_maze(seed, w=23, h=23):
    rng = seeded_rng(seed)
    grid = np.ones((h, w), dtype=np.int8)
    add_border_walls(grid)
    for y in range(1, h-1):
        for x in range(1, w-1):
            if x % 2 == 1 and y % 2 == 1:
                grid[y, x] = 0

    start = (1, 1)
    stack = [start]
    visited = set([start])

    def carve_between(a, b):
        ax, ay = a; bx, by = b
        mx, my = (ax+bx)//2, (ay+by)//2
        grid[my, mx] = 0

    while stack:
        x, y = stack[-1]
        dirs = [(2,0),(-2,0),(0,2),(0,-2)]
        rng.shuffle(dirs)
        moved = False
        for dx, dy in dirs:
            nx, ny = x+dx, y+dy
            if 1 <= nx < w-1 and 1 <= ny < h-1 and (nx, ny) not in visited:
                visited.add((nx, ny))
                carve_between((x, y), (nx, ny))
                stack.append((nx, ny))
                moved = True
                break
        if not moved:
            stack.pop()

    grid[1,1] = 0
    grid[1,2] = 0
    grid[2,1] = 0
    return grid

def map_rooms(seed, w=25, h=25):
    rng = seeded_rng(seed)
    grid = np.ones((h, w), dtype=np.int8)
    add_border_walls(grid)

    rooms = []
    for _ in range(10):
        rw = rng.randint(4, 7)
        rh = rng.randint(4, 7)
        rx = rng.randint(1, w-rw-2)
        ry = rng.randint(1, h-rh-2)
        grid[ry:ry+rh, rx:rx+rw] = 0
        rooms.append((rx, ry, rw, rh))

    for i in range(len(rooms)-1):
        x1 = rooms[i][0] + rooms[i][2]//2
        y1 = rooms[i][1] + rooms[i][3]//2
        x2 = rooms[i+1][0] + rooms[i+1][2]//2
        y2 = rooms[i+1][1] + rooms[i+1][3]//2
        if rng.random() < 0.5:
            grid[y1, min(x1,x2):max(x1,x2)+1] = 0
            grid[min(y1,y2):max(y1,y2)+1, x2] = 0
        else:
            grid[min(y1,y2):max(y1,y2)+1, x1] = 0
            grid[y2, min(x1,x2):max(x1,x2)+1] = 0
    return grid

def map_labyrinth(seed, w=31, h=23):
    rng = seeded_rng(seed)
    grid = np.zeros((h, w), dtype=np.int8)
    add_border_walls(grid)
    for y in range(1, h-1):
        for x in range(1, w-1):
            if (x % 2 == 0 and rng.random() < 0.85) or (y % 3 == 0 and rng.random() < 0.55):
                grid[y, x] = 1
    for x in range(1, w-1):
        grid[h//2, x] = 0
    for x in range(3, w-3, 6):
        for y in range(2, h-2):
            if rng.random() < 0.75:
                grid[y, x] = 0
    return grid

def map_dense_field(seed, w=23, h=23):
    rng = seeded_rng(seed)
    grid = np.zeros((h, w), dtype=np.int8)
    add_border_walls(grid)
    for y in range(1, h-1):
        for x in range(1, w-1):
            if rng.random() < 0.22:
                grid[y, x] = 1
    for _ in range(6):
        cx = rng.randint(3, w-4)
        cy = rng.randint(3, h-4)
        for yy in range(cy-2, cy+3):
            for xx in range(cx-2, cx+3):
                if 1 <= xx < w-1 and 1 <= yy < h-1:
                    grid[yy, xx] = 0
    return grid

MAP_BUILDERS = {
    "Training Bay": map_training,
    "Arena+": map_arena_plus,
    "Corridor Maze": map_corridor_maze,
    "Rooms": map_rooms,
    "Labyrinth": map_labyrinth,
    "Dense Field": map_dense_field,
}

# -----------------------------
# State construction
# -----------------------------
def build_state(seed, map_name, progress=None, override=None):
    rng = seeded_rng(seed)
    grid = MAP_BUILDERS[map_name](seed)
    pred, prey = pick_spawn_pair(grid, rng, min_dist=8)
    pred_ori = rng.randint(0, 3)
    prey_ori = (pred_ori + 2) % 4

    if progress is None:
        progress = {"catches": 0, "unlocked": compute_unlocks(0)}

    st = {
        "seed": int(seed),
        "grid": grid,

        "pred": pred,
        "prey": prey,

        "ori": pred_ori,
        "prey_ori": prey_ori,

        "control": "pred",          # "pred" or "prey" (view + manual inputs)
        "overlay": False,           # coherence overlay
        "disturbance": 0.0,
        "last_impulse": 0.0,

        "step": 0,
        "caught": False,
        "auto_chase": False,
        "auto_run": False,

        "log": [f"Reset into map: {map_name}"],
        "map_name": map_name,
        "progress": progress,
    }

    if override:
        for k, v in override.items():
            if k == "grid":
                continue
            st[k] = v

    return st

# -----------------------------
# Save / Load helpers
# -----------------------------
def serialize_state(st):
    catches = int(st["progress"]["catches"])
    payload = {
        "version": 2,
        "seed": int(st["seed"]),
        "map_name": str(st["map_name"]),
        "step": int(st["step"]),
        "pred": [int(st["pred"][0]), int(st["pred"][1])],
        "prey": [int(st["prey"][0]), int(st["prey"][1])],
        "ori": int(st["ori"]),
        "prey_ori": int(st.get("prey_ori", 0)),
        "control": str(st.get("control", "pred")),
        "overlay": bool(st.get("overlay", False)),
        "disturbance": float(st.get("disturbance", 0.0)),
        "caught": bool(st["caught"]),
        "auto_chase": bool(st["auto_chase"]),
        "auto_run": bool(st["auto_run"]),
        "catches": catches,
        "log_tail": st["log"][-20:],
    }
    return payload

def deserialize_state(payload):
    seed = int(payload.get("seed", 1))
    map_name = str(payload.get("map_name", "Training Bay"))
    if map_name not in MAP_BUILDERS:
        map_name = "Training Bay"

    catches = int(payload.get("catches", 0))
    progress = {"catches": catches, "unlocked": compute_unlocks(catches)}

    override = {
        "step": int(payload.get("step", 0)),
        "pred": tuple(payload.get("pred", [1, 1])),
        "prey": tuple(payload.get("prey", [2, 2])),
        "ori": int(payload.get("ori", 0)) % 4,
        "prey_ori": int(payload.get("prey_ori", 0)) % 4,
        "control": str(payload.get("control", "pred")) if str(payload.get("control", "pred")) in ("pred", "prey") else "pred",
        "overlay": bool(payload.get("overlay", False)),
        "disturbance": float(payload.get("disturbance", 0.0)),
        "last_impulse": 0.0,
        "caught": bool(payload.get("caught", False)),
        "auto_chase": bool(payload.get("auto_chase", False)),
        "auto_run": bool(payload.get("auto_run", False)),
        "log": (payload.get("log_tail", []) or [])[:],
    }

    st = build_state(seed, map_name, progress=progress, override=override)

    grid = st["grid"]
    H, W = grid.shape
    px, py = st["pred"]
    qx, qy = st["prey"]
    ok = (
        0 <= px < W and 0 <= py < H and 0 <= qx < W and 0 <= qy < H
        and grid[py, px] == 0 and grid[qy, qx] == 0
    )
    if not ok:
        rng = seeded_rng(seed + 777)
        st["pred"], st["prey"] = pick_spawn_pair(grid, rng, min_dist=8)
        st["log"].append("Loaded save had invalid positions; respawned safely.")

    st["log"].append("Loaded save.")
    return st

def save_to_path(st, path):
    payload = serialize_state(st)
    with open(path, "w", encoding="utf-8") as f:
        json.dump(payload, f, indent=2)
    st["log"].append(f"Saved to: {path}")

def load_from_path(path):
    with open(path, "r", encoding="utf-8") as f:
        payload = json.load(f)
    return deserialize_state(payload)

# -----------------------------
# Perception + rendering
# -----------------------------
def los_clear(grid, a, b):
    ax, ay = a[0] + 0.5, a[1] + 0.5
    bx, by = b[0] + 0.5, b[1] + 0.5
    dx, dy = bx - ax, by - ay
    dist = math.hypot(dx, dy)
    if dist < 1e-6:
        return True
    dx /= dist
    dy /= dist

    x, y = ax, ay
    steps = int(dist * 20)
    H, W = grid.shape
    for _ in range(steps):
        x += dx * (dist / steps)
        y += dy * (dist / steps)
        cx, cy = int(x), int(y)
        cx = clamp(cx, 0, W-1)
        cy = clamp(cy, 0, H-1)
        if grid[cy, cx] == 1:
            return False
    return True

def dda_raycast(grid, px, py, ray_dx, ray_dy, max_depth=MAX_DEPTH):
    H, W = grid.shape
    map_x = int(px)
    map_y = int(py)

    delta_dist_x = abs(1.0 / ray_dx) if abs(ray_dx) > 1e-9 else 1e9
    delta_dist_y = abs(1.0 / ray_dy) if abs(ray_dy) > 1e-9 else 1e9

    if ray_dx < 0:
        step_x = -1
        side_dist_x = (px - map_x) * delta_dist_x
    else:
        step_x = 1
        side_dist_x = (map_x + 1.0 - px) * delta_dist_x

    if ray_dy < 0:
        step_y = -1
        side_dist_y = (py - map_y) * delta_dist_y
    else:
        step_y = 1
        side_dist_y = (map_y + 1.0 - py) * delta_dist_y

    hit = False
    side = 0
    for _ in range(max_depth * 10):
        if side_dist_x < side_dist_y:
            side_dist_x += delta_dist_x
            map_x += step_x
            side = 0
        else:
            side_dist_y += delta_dist_y
            map_y += step_y
            side = 1

        if map_x < 0 or map_x >= W or map_y < 0 or map_y >= H:
            break
        if grid[map_y, map_x] == 1:
            hit = True
            break

    if not hit:
        return max_depth, 0, map_x, map_y

    if side == 0:
        denom = ray_dx if abs(ray_dx) > 1e-9 else 1e-9
        perp = (map_x - px + (1 - step_x) / 2) / denom
    else:
        denom = ray_dy if abs(ray_dy) > 1e-9 else 1e-9
        perp = (map_y - py + (1 - step_y) / 2) / denom

    perp = abs(perp)
    perp = clamp(perp, 0.0005, max_depth)
    return perp, side, map_x, map_y

def _apply_coherence_overlay(img, disturbance: float):
    d = float(disturbance)
    if d <= 0.001:
        return img

    alpha = clamp(d * 0.06, 0.0, 0.22)  # subtle
    h, w, _ = img.shape
    cx, cy = w // 2, h // 2

    edge = int(min(w, h) * 0.08)
    if edge >= 2:
        tint = np.array([22, 8, 18], dtype=np.float32)
        img[:edge, :, :] = np.clip(img[:edge, :, :].astype(np.float32) + tint * alpha, 0, 255).astype(np.uint8)
        img[h-edge:, :, :] = np.clip(img[h-edge:, :, :].astype(np.float32) + tint * alpha, 0, 255).astype(np.uint8)
        img[:, :edge, :] = np.clip(img[:, :edge, :].astype(np.float32) + tint * alpha, 0, 255).astype(np.uint8)
        img[:, w-edge:, :] = np.clip(img[:, w-edge:, :].astype(np.float32) + tint * alpha, 0, 255).astype(np.uint8)

    line_col = np.array([180, 70, 160], dtype=np.float32)
    for i in range(-40, 41):
        x = cx + i
        y = cy + int(i * 0.35)
        if 0 <= x < w and 0 <= y < h:
            img[y:y+1, x:x+1, :] = np.clip(img[y:y+1, x:x+1, :].astype(np.float32) * (1-alpha) + line_col * alpha, 0, 255).astype(np.uint8)

        y2 = cy - int(i * 0.35)
        if 0 <= x < w and 0 <= y2 < h:
            img[y2:y2+1, x:x+1, :] = np.clip(img[y2:y2+1, x:x+1, :].astype(np.float32) * (1-alpha) + line_col * alpha, 0, 255).astype(np.uint8)

    return img

def render_first_person(st):
    grid = st["grid"]

    if st["control"] == "prey":
        view_cell = st["prey"]
        view_ori = st["prey_ori"]
        other_cell = st["pred"]
    else:
        view_cell = st["pred"]
        view_ori = st["ori"]
        other_cell = st["prey"]

    (cx, cy) = view_cell
    px = cx + 0.5
    py = cy + 0.5

    fov = math.radians(FOV_DEG)
    base = math.radians(ORI_DEG[view_ori])

    img = np.zeros((VIEW_H, VIEW_W, 3), dtype=np.uint8)
    img[:VIEW_H//2, :, :] = SKY

    for y in range(VIEW_H//2, VIEW_H):
        t = (y - VIEW_H//2) / max(1, (VIEW_H//2 - 1))
        col = (FLOOR_NEAR * (1 - t) + FLOOR_FAR * t).astype(np.uint8)
        img[y, :, :] = col

    wall_dists = np.full(RAY_W, MAX_DEPTH, dtype=np.float32)

    for x in range(RAY_W):
        u = (x / (RAY_W - 1)) if RAY_W > 1 else 0.5
        ang = base + (u - 0.5) * fov
        ray_dx = math.cos(ang)
        ray_dy = math.sin(ang)

        dist, side, hitx, hity = dda_raycast(grid, px, py, ray_dx, ray_dy, MAX_DEPTH)
        dist *= math.cos(ang - base)
        dist = clamp(dist, 0.001, MAX_DEPTH)
        wall_dists[x] = dist

        slice_h = int((VIEW_H * 0.92) / dist)
        slice_h = clamp(slice_h, 1, VIEW_H)
        top = (VIEW_H - slice_h) // 2
        bot = top + slice_h

        shade = 1.0 / (1.0 + dist * 0.12)
        shade = clamp(shade, 0.12, 1.0)
        base_col = WALL_SIDE if side == 1 else WALL_BASE
        checker = ((hitx + hity) & 1)
        tex = 0.90 if checker == 0 else 1.05
        col = np.clip(base_col.astype(np.float32) * shade * tex, 0, 255).astype(np.uint8)

        x0 = int(x * VIEW_W / RAY_W)
        x1 = int((x + 1) * VIEW_W / RAY_W)
        if x1 <= x0:
            x1 = x0 + 1
        img[top:bot, x0:x1, :] = col

    other_vis = False
    if not st["caught"] and los_clear(grid, view_cell, other_cell):
        vx = (other_cell[0] + 0.5) - px
        vy = (other_cell[1] + 0.5) - py
        other_dist = math.hypot(vx, vy)
        other_ang = math.atan2(vy, vx)
        rel = angle_diff_rad(other_ang, base)
        if abs(rel) <= fov * 0.5 and other_dist < MAX_DEPTH:
            other_vis = True
            u = (rel / fov) + 0.5
            sx_ray = int(u * (RAY_W - 1))
            sx_ray = clamp(sx_ray, 0, RAY_W - 1)

            sprite_h = int((VIEW_H * 0.75) / max(0.2, other_dist))
            sprite_w = int(sprite_h * 0.45)
            sprite_h = clamp(sprite_h, 8, VIEW_H)
            sprite_w = clamp(sprite_w, 6, VIEW_W)

            sx = int(sx_ray * VIEW_W / RAY_W)
            sy = VIEW_H // 2

            x0 = clamp(sx - sprite_w // 2, 0, VIEW_W - 1)
            x1 = clamp(sx + sprite_w // 2, 0, VIEW_W - 1)
            y0 = clamp(sy - sprite_h // 2, 0, VIEW_H - 1)
            y1 = clamp(sy + sprite_h // 2, 0, VIEW_H - 1)

            for vxcol in range(x0, x1):
                rx = int(vxcol * RAY_W / VIEW_W)
                rx = clamp(rx, 0, RAY_W - 1)
                if other_dist < wall_dists[rx]:
                    img[y0:y1, vxcol:vxcol+1, :] = AGENT_OTHER_COLOR

    cxh, cyh = VIEW_W // 2, VIEW_H // 2
    img[cyh-1:cyh+2, cxh-12:cxh+13, :] = RETICLE
    img[cyh-12:cyh+13, cxh-1:cxh+2, :] = RETICLE

    hud_h = 26
    img[:hud_h, :, :] = np.clip(img[:hud_h, :, :].astype(np.int16) + 20, 0, 255).astype(np.uint8)

    def dot(x, y, c):
        img[y:y+6, x:x+6, :] = c

    dot(8, 10, np.array([90, 255, 140], np.uint8) if st["auto_chase"] else np.array([60, 60, 70], np.uint8))
    dot(20, 10, np.array([120, 190, 255], np.uint8) if st["auto_run"] else np.array([60, 60, 70], np.uint8))
    dot(32, 10, np.array([255, 140, 90], np.uint8) if other_vis else np.array([60, 60, 70], np.uint8))

    if st.get("overlay", False):
        img = _apply_coherence_overlay(img, st.get("disturbance", 0.0))

    return img

def render_minimap(st, scale=14):
    grid = st["grid"]
    H, W = grid.shape
    img = np.zeros((H*scale, W*scale, 3), dtype=np.uint8)
    img[:, :, :] = np.array([18, 20, 32], dtype=np.uint8)

    wall = np.array([220, 220, 235], dtype=np.uint8)
    for y in range(H):
        for x in range(W):
            if grid[y, x] == 1:
                img[y*scale:(y+1)*scale, x*scale:(x+1)*scale, :] = wall

    px, py = st["pred"]
    qx, qy = st["prey"]
    pred_col = np.array([120, 190, 255], np.uint8)
    prey_col = np.array([255, 140, 90], np.uint8)

    img[py*scale:(py+1)*scale, px*scale:(px+1)*scale, :] = pred_col
    img[qy*scale:(qy+1)*scale, qx*scale:(qx+1)*scale, :] = prey_col

    dx, dy = DIRS[st["ori"]]
    hx, hy = px + dx, py + dy
    if 0 <= hx < W and 0 <= hy < H:
        img[hy*scale:(hy+1)*scale, hx*scale:(hx+1)*scale, :] = np.array([80, 255, 160], np.uint8)

    dx2, dy2 = DIRS[st["prey_ori"]]
    hx2, hy2 = qx + dx2, qy + dy2
    if 0 <= hx2 < W and 0 <= hy2 < H:
        img[hy2*scale:(hy2+1)*scale, hx2*scale:(hx2+1)*scale, :] = np.array([255, 220, 120], np.uint8)

    if st["control"] == "pred":
        x0, y0 = px*scale, py*scale
    else:
        x0, y0 = qx*scale, qy*scale
    ring = np.array([240, 240, 140], np.uint8)
    img[y0:y0+scale, x0:x0+2, :] = ring
    img[y0:y0+scale, x0+scale-2:x0+scale, :] = ring
    img[y0:y0+2, x0:x0+scale, :] = ring
    img[y0+scale-2:y0+scale, x0:x0+scale, :] = ring

    return img

def unlock_summary(st):
    catches = st["progress"]["catches"]
    unlocked = st["progress"]["unlocked"]
    lines = []
    for name, need in MAP_UNLOCKS:
        if name in unlocked:
            lines.append(f"✅ {name} (unlocked)")
        else:
            lines.append(f"🔒 {name} (needs {need} catches)")
    return "### Map progression\n" + "\n".join(lines) + f"\n\n**Total catches:** {catches}"

def status(st):
    pred_ori_txt = ["E", "S", "W", "N"][st["ori"]]
    prey_ori_txt = ["E", "S", "W", "N"][st["prey_ori"]]
    tail = st["log"][-10:]
    catches = st["progress"]["catches"]
    current = st["map_name"]

    mode = "Manual"
    if st["auto_run"] and st["auto_chase"]:
        mode = "AutoRun+AutoChase"
    elif st["auto_run"] and not st["auto_chase"]:
        mode = "Hybrid AutoRun (wander)"

    ctrl = "Predator" if st["control"] == "pred" else "Prey"
    coh = st.get("disturbance", 0.0)
    return (
        f"Map: {current} | Catches: {catches} | Step: {st['step']} | Mode: {mode} | Control: {ctrl} | Overlay: {st.get('overlay', False)}\n"
        f"Predator: {st['pred']} {pred_ori_txt} | Prey: {st['prey']} {prey_ori_txt} | "
        f"AutoChase: {st['auto_chase']} | AutoRun: {st['auto_run']} | Caught: {st['caught']} | Coherence: {coh:.2f}\n\n"
        + "\n".join(tail)
    )

# -----------------------------
# Actions (manual + autonomous)
# -----------------------------
def _add_impulse(st, x):
    st["last_impulse"] = float(st.get("last_impulse", 0.0)) + float(x)

def _step_disturbance(st):
    d = float(st.get("disturbance", 0.0))
    imp = float(st.get("last_impulse", 0.0))
    st["disturbance"] = 0.92 * d + imp
    st["last_impulse"] = 0.0

def _agent_pos_ori(st, who):
    if who == "prey":
        return st["prey"], st["prey_ori"]
    return st["pred"], st["ori"]

def _set_agent_pos_ori(st, who, pos=None, ori=None):
    if who == "prey":
        if pos is not None: st["prey"] = pos
        if ori is not None: st["prey_ori"] = int(ori) % 4
    else:
        if pos is not None: st["pred"] = pos
        if ori is not None: st["ori"] = int(ori) % 4

def _turn(st, who, direction):
    if st["caught"]:
        return
    _, ori = _agent_pos_ori(st, who)
    ori = (ori + direction) % 4
    _set_agent_pos_ori(st, who, ori=ori)
    _add_impulse(st, 0.9)

def _forward(st, who):
    if st["caught"]:
        return
    (x, y), ori = _agent_pos_ori(st, who)
    dx, dy = DIRS[ori]
    nx, ny = x + dx * MOVE_STEP, y + dy * MOVE_STEP
    if st["grid"][ny, nx] == 0:
        _set_agent_pos_ori(st, who, pos=(nx, ny))
        _add_impulse(st, 0.25)
    else:
        st["log"].append(f"{'Prey' if who=='prey' else 'Predator'} bumped wall.")
        _add_impulse(st, 0.7)

def _check_catch_and_unlock(st):
    if st["pred"] == st["prey"] and not st["caught"]:
        st["caught"] = True
        st["log"].append("CAUGHT the prey.")
        st["progress"]["catches"] += 1
        st["progress"]["unlocked"] = compute_unlocks(st["progress"]["catches"])
        st["log"].append(f"Catches now {st['progress']['catches']}. Unlocks updated.")
        _add_impulse(st, 1.2)
        return True
    return False

def prey_flee_step(st):
    if st["caught"]:
        return
    rng = seeded_rng(st["seed"] + 1337 + st["step"] * 19)
    px, py = st["prey"]
    ax, ay = st["pred"]

    options = [(0,0),(1,0),(-1,0),(0,1),(0,-1)]
    scored = []
    for dx, dy in options:
        nx, ny = px + dx, py + dy
        if st["grid"][ny, nx] == 1:
            continue
        dist = (nx-ax)**2 + (ny-ay)**2
        scored.append((dist + rng.random()*0.1, (nx, ny), (dx, dy)))

    if scored:
        scored.sort(reverse=True)
        pick = scored[0] if rng.random() < 0.78 else rng.choice(scored)
        _, (nx, ny), (dx, dy) = pick
        st["prey"] = (nx, ny)
        if (dx, dy) in DIR_TO_ORI and (dx, dy) != (0,0):
            st["prey_ori"] = DIR_TO_ORI[(dx, dy)]

def predator_wander_step(st):
    if st["caught"]:
        return
    rng = seeded_rng(st["seed"] + 4242 + st["step"] * 23)
    (x, y) = st["pred"]
    ori = st["ori"]
    dx, dy = DIRS[ori]
    front_blocked = (st["grid"][y+dy, x+dx] == 1)

    r = rng.random()
    if front_blocked:
        if r < 0.5:
            _turn(st, "pred", -1); st["log"].append("AutoWander: avoid left.")
        else:
            _turn(st, "pred", +1); st["log"].append("AutoWander: avoid right.")
    else:
        if r < 0.72:
            _forward(st, "pred"); st["log"].append("AutoWander: forward.")
        elif r < 0.86:
            _turn(st, "pred", -1); st["log"].append("AutoWander: turn left.")
        else:
            _turn(st, "pred", +1); st["log"].append("AutoWander: turn right.")

def predator_chase_step(st):
    if st["caught"]:
        return
    grid = st["grid"]
    px = st["pred"][0] + 0.5
    py = st["pred"][1] + 0.5
    base = math.radians(ORI_DEG[st["ori"]])
    fov = math.radians(FOV_DEG)
    prey = st["prey"]

    if los_clear(grid, st["pred"], prey):
        vx = (prey[0] + 0.5) - px
        vy = (prey[1] + 0.5) - py
        ang = math.atan2(vy, vx)
        rel = angle_diff_rad(ang, base)
        if abs(rel) <= fov * 0.5:
            if rel < -0.10:
                _turn(st, "pred", -1); st["log"].append("AutoChase: turn left.")
            elif rel > 0.10:
                _turn(st, "pred", +1); st["log"].append("AutoChase: turn right.")
            else:
                _forward(st, "pred"); st["log"].append("AutoChase: forward.")
            return
    predator_wander_step(st)

def prey_autopilot_step(st):
    # if user is viewing prey and AutoRun is on, prey should still behave like prey (flee)
    prey_flee_step(st)
    st["log"].append("AutoPrey: flee.")

def tick(st):
    if st["caught"]:
        return False  # unlock did not change

    st["step"] += 1
    unlock_changed = False

    # AutoRun = autopilot for the currently viewed observer
    if st["auto_run"]:
        if st["control"] == "pred":
            if st["auto_chase"]:
                predator_chase_step(st)
            else:
                predator_wander_step(st)
        else:
            prey_autopilot_step(st)

    # The non-controlled agent still runs its own policy each step
    if st["control"] != "pred":
        if st["auto_chase"]:
            predator_chase_step(st)
        else:
            predator_wander_step(st)

    if st["control"] != "prey":
        prey_flee_step(st)

    # capture + unlock
    unlock_changed = _check_catch_and_unlock(st)

    _step_disturbance(st)

    if st["step"] >= 600:
        st["caught"] = True
        st["log"].append("Max steps reached (freeze).")

    return unlock_changed

# -----------------------------
# Gradio handlers
# -----------------------------
def ui_refresh_slots(current_value=None):
    choices = list_save_slots()
    if current_value and current_value in choices:
        value = current_value
    else:
        value = choices[0] if choices else "slot1.json"
    return gr.Dropdown(choices=choices if choices else ["slot1.json"], value=value)

def ui_reset(seed, map_choice, st=None):
    seed = int(seed)
    progress = st["progress"] if st else {"catches": 0, "unlocked": compute_unlocks(0)}
    if map_choice not in progress["unlocked"]:
        map_choice = "Training Bay"
    new_st = build_state(seed, map_choice, progress=progress)
    if st:
        new_st["control"] = st.get("control", "pred")
        new_st["overlay"] = st.get("overlay", False)
    return new_st, render_first_person(new_st), render_minimap(new_st), status(new_st), unlock_summary(new_st)

def ui_toggle_control(st):
    st["control"] = "prey" if st["control"] == "pred" else "pred"
    st["log"].append(f"Control switched to: {'Prey' if st['control']=='prey' else 'Predator'}.")
    _add_impulse(st, 0.15)
    return st, render_first_person(st), render_minimap(st), status(st), unlock_summary(st)

def ui_turn_left(st):
    who = st["control"]
    _turn(st, who, -1)
    st["log"].append(f"{'Prey' if who=='prey' else 'Predator'} turn left.")
    unlock_changed = tick(st)
    if unlock_changed:
        return st, render_first_person(st), render_minimap(st), status(st), unlock_summary(st)
    return st, render_first_person(st), render_minimap(st), status(st), gr.update()  # avoid re-render if unchanged

def ui_turn_right(st):
    who = st["control"]
    _turn(st, who, +1)
    st["log"].append(f"{'Prey' if who=='prey' else 'Predator'} turn right.")
    unlock_changed = tick(st)
    if unlock_changed:
        return st, render_first_person(st), render_minimap(st), status(st), unlock_summary(st)
    return st, render_first_person(st), render_minimap(st), status(st), gr.update()

def ui_forward(st):
    who = st["control"]
    _forward(st, who)
    st["log"].append(f"{'Prey' if who=='prey' else 'Predator'} forward.")
    unlock_changed = tick(st)
    if unlock_changed:
        return st, render_first_person(st), render_minimap(st), status(st), unlock_summary(st)
    return st, render_first_person(st), render_minimap(st), status(st), gr.update()

def ui_toggle_chase(st):
    st["auto_chase"] = not st["auto_chase"]
    st["log"].append(f"AutoChase set to {st['auto_chase']}.")
    _add_impulse(st, 0.10)
    return st, render_first_person(st), render_minimap(st), status(st), gr.update()

def ui_toggle_run(st):
    st["auto_run"] = not st["auto_run"]
    st["log"].append(f"AutoRun set to {st['auto_run']}.")
    _add_impulse(st, 0.10)
    return st, render_first_person(st), render_minimap(st), status(st), gr.update()

def ui_toggle_overlay(st):
    st["overlay"] = not st.get("overlay", False)
    st["log"].append(f"Overlay set to {st['overlay']}.")
    return st, render_first_person(st), render_minimap(st), status(st), gr.update()

def ui_tick(st):
    unlock_changed = tick(st)
    if unlock_changed:
        return st, render_first_person(st), render_minimap(st), status(st), unlock_summary(st)
    return st, render_first_person(st), render_minimap(st), status(st), gr.update()

def ui_timer(st):
    # IMPORTANT: do NOT update unlock markdown here (prevents flashing)
    if st["auto_run"] and not st["caught"]:
        _ = tick(st)
    return st, render_first_person(st), render_minimap(st), status(st)

def ui_swap_roles(st):
    if st["caught"]:
        return st, render_first_person(st), render_minimap(st), status(st), gr.update()
    st["pred"], st["prey"] = st["prey"], st["pred"]
    st["ori"], st["prey_ori"] = st["prey_ori"], st["ori"]
    st["log"].append("Swapped roles (Predator ⇄ Prey).")
    _add_impulse(st, 0.35)
    changed = _check_catch_and_unlock(st)
    if changed:
        return st, render_first_person(st), render_minimap(st), status(st), unlock_summary(st)
    return st, render_first_person(st), render_minimap(st), status(st), gr.update()

# ---- Save/load UI handlers ----
def ui_save_slot(st, slot_name):
    try:
        path = _slot_path(slot_name)
        save_to_path(st, path)
        export_path = path
    except Exception as e:
        st["log"].append(f"Save failed: {e}")
        export_path = None

    dd = ui_refresh_slots(os.path.basename(export_path) if export_path else None)
    return st, render_first_person(st), render_minimap(st), status(st), unlock_summary(st), export_path, dd

def ui_load_slot(st, selected_slot):
    path = os.path.join(SAVE_DIR, selected_slot) if selected_slot else _slot_path("slot1")
    try:
        if not os.path.exists(path):
            st["log"].append(f"No save found at {path}")
            dd = ui_refresh_slots(selected_slot)
            return st, render_first_person(st), render_minimap(st), status(st), gr.update(), None, dd
        loaded = load_from_path(path)
        dd = ui_refresh_slots(os.path.basename(path))
        return loaded, render_first_person(loaded), render_minimap(loaded), status(loaded), unlock_summary(loaded), None, dd
    except Exception as e:
        st["log"].append(f"Load failed: {e}")
        dd = ui_refresh_slots(selected_slot)
        return st, render_first_person(st), render_minimap(st), status(st), gr.update(), None, dd

def ui_import_save(st, uploaded_file):
    try:
        if uploaded_file is None:
            st["log"].append("Import: no file provided.")
            dd = ui_refresh_slots()
            return st, render_first_person(st), render_minimap(st), status(st), gr.update(), None, dd
        loaded = load_from_path(uploaded_file)
        dd = ui_refresh_slots()
        return loaded, render_first_person(loaded), render_minimap(loaded), status(loaded), unlock_summary(loaded), None, dd
    except Exception as e:
        st["log"].append(f"Import failed: {e}")
        dd = ui_refresh_slots()
        return st, render_first_person(st), render_minimap(st), status(st), gr.update(), None, dd

# -----------------------------
# App
# -----------------------------
all_map_names = [name for name, _ in MAP_UNLOCKS]
initial_progress = {"catches": 0, "unlocked": compute_unlocks(0)}
initial_state = build_state(seed=1, map_name="Training Bay", progress=initial_progress)

initial_slots = list_save_slots()
initial_slot_value = initial_slots[0] if initial_slots else "slot1.json"

with gr.Blocks(title="RFT Predator Space — Symmetric Observers") as demo:
    gr.Markdown(
        "## Experience reality through an RFT observer agent’s perspective\n"
        "**Accessibility note:** the progression panel is now event-driven (no flashing).\n\n"
        "**AutoRun:** moves the current POV observer (first-person autopilot)."
    )

    st = gr.State(initial_state)

    with gr.Row():
        seed = gr.Number(label="Seed", value=1, precision=0)
        map_choice = gr.Dropdown(label="Map (locked unless unlocked)", choices=all_map_names, value="Training Bay")
        btn_reset = gr.Button("Reset")
        btn_control = gr.Button("Toggle Control (Pred ↔ Prey)")
        btn_tick = gr.Button("Tick")

    with gr.Row():
        btn_left = gr.Button("Turn Left")
        btn_fwd = gr.Button("Forward")
        btn_right = gr.Button("Turn Right")

    with gr.Row():
        btn_chase = gr.Button("Toggle AutoChase")
        btn_run = gr.Button("Toggle AutoRun")
        btn_overlay = gr.Button("Toggle Overlay (optional)")
        btn_swap = gr.Button("Swap Roles (Pred ⇄ Prey)")

    with gr.Row():
        view = gr.Image(label="First-person observer view", type="numpy")
        mini = gr.Image(label="Minimap (debug)", type="numpy")

    with gr.Row():
        info = gr.Textbox(label="Run log", lines=12)
        unlocks = gr.Markdown(value=unlock_summary(initial_state))

    gr.Markdown("### Save / Load")

    with gr.Row():
        slot_pick = gr.Dropdown(label="Existing saves", choices=initial_slots if initial_slots else ["slot1.json"], value=initial_slot_value)
        slot_name = gr.Textbox(label="New save name (optional)", value="slot1")

    with gr.Row():
        btn_refresh = gr.Button("Refresh Saves List")
        btn_save = gr.Button("Save (to name)")
        btn_load = gr.Button("Load (selected)")

    with gr.Row():
        export_file = gr.File(label="Exported Save File (download this)", interactive=False)
        import_file = gr.File(label="Import Save File (upload)", interactive=True)
        btn_import = gr.Button("Import (Load Uploaded File)")

    demo.load(
        lambda: (st.value, render_first_person(st.value), render_minimap(st.value), status(st.value), unlock_summary(st.value), None, ui_refresh_slots(initial_slot_value)),
        outputs=[st, view, mini, info, unlocks, export_file, slot_pick]
    )

    btn_reset.click(ui_reset, inputs=[seed, map_choice, st], outputs=[st, view, mini, info, unlocks])
    btn_control.click(ui_toggle_control, inputs=[st], outputs=[st, view, mini, info, unlocks])

    btn_left.click(ui_turn_left, inputs=[st], outputs=[st, view, mini, info, unlocks])
    btn_right.click(ui_turn_right, inputs=[st], outputs=[st, view, mini, info, unlocks])
    btn_fwd.click(ui_forward, inputs=[st], outputs=[st, view, mini, info, unlocks])

    btn_chase.click(ui_toggle_chase, inputs=[st], outputs=[st, view, mini, info, unlocks])
    btn_run.click(ui_toggle_run, inputs=[st], outputs=[st, view, mini, info, unlocks])
    btn_overlay.click(ui_toggle_overlay, inputs=[st], outputs=[st, view, mini, info, unlocks])
    btn_swap.click(ui_swap_roles, inputs=[st], outputs=[st, view, mini, info, unlocks])

    btn_tick.click(ui_tick, inputs=[st], outputs=[st, view, mini, info, unlocks])

    btn_refresh.click(lambda cur: ui_refresh_slots(cur), inputs=[slot_pick], outputs=[slot_pick])

    btn_save.click(
        lambda st_, name_, pick_: ui_save_slot(st_, name_ if (name_ and name_.strip()) else pick_),
        inputs=[st, slot_name, slot_pick],
        outputs=[st, view, mini, info, unlocks, export_file, slot_pick]
    )

    btn_load.click(
        ui_load_slot,
        inputs=[st, slot_pick],
        outputs=[st, view, mini, info, unlocks, export_file, slot_pick]
    )

    btn_import.click(
        ui_import_save,
        inputs=[st, import_file],
        outputs=[st, view, mini, info, unlocks, export_file, slot_pick]
    )

    # Timer outputs DO NOT include unlocks (prevents flashing)
    if hasattr(gr, "Timer"):
        gr.Timer(1.0 / AUTO_TICK_HZ).tick(
            ui_timer,
            inputs=[st],
            outputs=[st, view, mini, info]
        )

# queue() helps Timer behave reliably in Spaces
demo.queue().launch()