app.py

# app.py — FLUX-only with temporal chaining (5s later by default) + Aggressive follow option
import os, json, uuid, re
from datetime import datetime
import gradio as gr
import spaces
import torch
from PIL import Image
import pandas as pd

# =========================
# Storage helpers
# =========================
ROOT = "outputs"
os.makedirs(ROOT, exist_ok=True)

def now_iso(): return datetime.utcnow().replace(microsecond=0).isoformat() + "Z"
def new_id(): return uuid.uuid4().hex[:8]

def project_dir(pid):
    path = os.path.join(ROOT, pid)
    os.makedirs(path, exist_ok=True)
    os.makedirs(os.path.join(path, "keyframes"), exist_ok=True)
    os.makedirs(os.path.join(path, "clips"), exist_ok=True)
    return path

def save_project(proj):
    pid = proj["meta"]["id"]
    path = os.path.join(project_dir(pid), "project.json")
    with open(path, "w") as f: json.dump(proj, f, indent=2)
    return path

def load_project_file(file_obj):
    with open(file_obj.name, "r") as f:
        proj = json.load(f)
    project_dir(proj["meta"]["id"])
    return proj

def ensure_project(p, suggested_name="Project"):
    if p is not None:
        return p
    pid = new_id()
    name = f"{suggested_name}-{pid[:4]}"
    proj = {
        "meta": {"id": pid, "name": name, "created": now_iso(), "updated": now_iso()},
        "shots": [],    # each shot: id,title,description,duration,fps,steps,seed,negative,image_path
        "clips": [],
    }
    save_project(proj)
    return proj

# =========================
# LLM (ZeroGPU) — Storyboard generator (robust)
# =========================
from transformers import AutoTokenizer, AutoModelForCausalLM

STORYBOARD_MODEL = os.getenv("STORYBOARD_MODEL", "Qwen/Qwen2.5-1.5B-Instruct")
HF_TASK_MAX_TOKENS = int(os.getenv("HF_TASK_MAX_TOKENS", "1200"))

_tokenizer = None
_model = None

def _lazy_model_tok():
    global _tokenizer, _model
    if _tokenizer is not None and _model is not None:
        return _model, _tokenizer

    _tokenizer = AutoTokenizer.from_pretrained(STORYBOARD_MODEL, trust_remote_code=True)

    use_cuda = torch.cuda.is_available()
    preferred_dtype = torch.float16 if use_cuda else torch.float32

    _model = AutoModelForCausalLM.from_pretrained(
        STORYBOARD_MODEL,
        device_map="auto",
        torch_dtype=preferred_dtype,
        trust_remote_code=True,
        use_safetensors=True
    )

    if _tokenizer.pad_token_id is None and _tokenizer.eos_token_id is not None:
        _tokenizer.pad_token_id = _tokenizer.eos_token_id

    return _model, _tokenizer

def _prompt_with_tags(user_prompt: str, n_shots: int, default_fps: int, default_len: int) -> str:
    return (
        "You are a cinematographer and storyboard artist. "
        "Given a story idea, break it into a sequence of visually DISTINCT, DETAILED shots. "
        "For each shot, provide the objects in the scene, very specific camera placement, angle, subject position, lighting, and background details. "
        "Imagine you're describing frames for a film storyboard, not vague events.\n\n"
        "Return ONLY a JSON array enclosed between <JSON> and </JSON> tags.\n"
        f"Create a storyboard of {n_shots} shots for this idea:\n\n"
        f"'''{user_prompt}'''\n\n"
        "Each item schema:\n"
        "{\n"
        '  "id": <int starting at 1>,\n'
        '  "title": "Short shot title",\n'
        '  "description": "Highly specific visual description for image generation. Include camera angle, framing, time of day, subject position, lighting, mood, and background details.",\n'
        f'  "duration": {default_len},\n'
        f'  "fps": {default_fps},\n'
        '  "steps": 30,\n'
        '  "seed": null,\n'
        '  "negative": ""\n'
        "}\n\n"
        "Output must start with <JSON> and end with </JSON>.\n"
    )


def _prompt_minimal(user_prompt: str, n_shots: int, default_fps: int, default_len: int) -> str:
    return (
        "Reply ONLY with a JSON array starting with '[' and ending with ']'. No extra text.\n"
        f"Storyboard: {n_shots} shots for:\n'''{user_prompt}'''\n"
        "Item schema:\n"
        "{\n"
        '  "id": <int starting at 1>,\n'
        '  "title": "Short title",\n'
        '  "description": "Visual description",\n'
        f'  "duration": {default_len},\n'
        f'  "fps": {default_fps},\n'
        '  "steps": 30,\n'
        '  "seed": null,\n'
        '  "negative": ""\n'
        "}\n"
    )
    
def _apply_chat(tok, system_msg: str, user_msg: str) -> str:
    if hasattr(tok, "apply_chat_template"):
        return tok.apply_chat_template(
            [{"role": "system", "content": system_msg},
             {"role": "user", "content": user_msg}],
            tokenize=False,
            add_generation_prompt=True
        )
    return system_msg + "\n\n" + user_msg

def _generate_text(model, tok, prompt_text: str) -> str:
    inputs = tok(prompt_text, return_tensors="pt")
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    eos_id = tok.eos_token_id or tok.pad_token_id

    gen = model.generate(
        **inputs,
        max_new_tokens=HF_TASK_MAX_TOKENS,
        do_sample=False,
        temperature=0.0,
        repetition_penalty=1.05,
        eos_token_id=eos_id,
        pad_token_id=eos_id,
    )
    prompt_len = inputs["input_ids"].shape[1]
    continuation_ids = gen[0][prompt_len:]
    text = tok.decode(continuation_ids, skip_special_tokens=True).strip()
    if text.startswith("```"):
        text = re.sub(r"^```(?:json)?\s*|\s*```$", "", text, flags=re.IGNORECASE|re.DOTALL).strip()
    return text

def _extract_json_array(text: str) -> str:
    m = re.search(r"<JSON>(.*?)</JSON>", text, flags=re.DOTALL | re.IGNORECASE)
    if m:
        inner = m.group(1).strip()
        if inner:
            return inner
    start = text.find("[")
    if start == -1:
        return ""
    depth = 0
    in_str = False
    prev = ""
    for i in range(start, len(text)):
        ch = text[i]
        if ch == '"' and prev != '\\':
            in_str = not in_str
        if not in_str:
            if ch == "[":
                depth += 1
            elif ch == "]":
                depth -= 1
                if depth == 0:
                    return text[start:i+1].strip()
        prev = ch
    return ""

def _normalize_shots(shots_raw, default_fps: int, default_len: int):
    norm = []
    for i, s in enumerate(shots_raw, start=1):
        norm.append({
            "id": int(s.get("id", i)),
            "title": s.get("title", f"Shot {i}"),
            "description": s.get("description", ""),
            "duration": int(s.get("duration", default_len)),
            "fps": int(s.get("fps", default_fps)),
            "steps": int(s.get("steps", 30)),
            "seed": s.get("seed", None),
            "negative": s.get("negative", ""),
            "image_path": s.get("image_path", None)
        })
    return norm

@spaces.GPU(duration=180)
def generate_storyboard_with_llm(user_prompt: str, n_shots: int, default_fps: int, default_len: int):
    model, tok = _lazy_model_tok()
    system = "You are a film previsualization assistant. Output must be valid JSON."

    p1 = _apply_chat(tok, system + " Return ONLY JSON inside <JSON> tags.",
                     _prompt_with_tags(user_prompt, n_shots, default_fps, default_len))
    out1 = _generate_text(model, tok, p1)
    json_text = _extract_json_array(out1)

    if not json_text:
        p2 = _apply_chat(tok, system + " Reply ONLY with a JSON array.",
                         _prompt_minimal(user_prompt, n_shots, default_fps, default_len))
        out2 = _generate_text(model, tok, p2)
        json_text = _extract_json_array(out2)
        if not json_text and "[" in out2 and "]" in out2:
            start = out2.find("["); end = out2.rfind("]")
            if start != -1 and end != -1 and end > start:
                json_text = out2[start:end+1].strip()

    if not json_text or not json_text.strip():
        fallback = []
        for i in range(1, int(n_shots) + 1):
            fallback.append({
                "id": i,
                "title": f"Shot {i}",
                "description": f"Simple placeholder for: {user_prompt[:80]}",
                "duration": default_len,
                "fps": default_fps,
                "steps": 30,
                "seed": None,
                "negative": "",
                "image_path": None
            })
        return fallback

    try:
        shots_raw = json.loads(json_text)
    except Exception:
        json_text_clean = re.sub(r",\s*([\]\}])", r"\1", json_text)
        shots_raw = json.loads(json_text_clean)

    return _normalize_shots(shots_raw, default_fps, default_len)

# =========================
# IMAGE GEN — FLUX only (no fallback) + Temporal chaining
# =========================
USE_CUDA = torch.cuda.is_available()
DTYPE = torch.float16 if USE_CUDA else torch.float32

# Correct, gated repo; accept access and set HF_TOKEN
FLUX_MODEL = os.getenv("FLUX_MODEL", "black-forest-labs/FLUX.1-schnell")
HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN")

_flux_t2i = None
_flux_i2i = None

def _lazy_flux_pipes():
    from diffusers import FluxPipeline, FluxImg2ImgPipeline
    global _flux_t2i, _flux_i2i
    if _flux_t2i is not None and _flux_i2i is not None:
        return _flux_t2i, _flux_i2i
    _flux_t2i = FluxPipeline.from_pretrained(
        FLUX_MODEL, torch_dtype=DTYPE, use_safetensors=True, token=HF_TOKEN
    )
    if USE_CUDA: _flux_t2i = _flux_t2i.to("cuda")
    _flux_i2i = FluxImg2ImgPipeline.from_pretrained(
        FLUX_MODEL, torch_dtype=DTYPE, use_safetensors=True, token=HF_TOKEN
    )
    if USE_CUDA: _flux_i2i = _flux_i2i.to("cuda")
    return _flux_t2i, _flux_i2i

def _flux_healthcheck():
    if not HF_TOKEN:
        raise RuntimeError(
            "HF_TOKEN is not set. FLUX models are gated; set a Hugging Face READ token "
            "and accept the model terms on the repo page."
        )
    _lazy_flux_pipes()

def _save_keyframe(pid: str, shot_id: int, img: Image.Image) -> str:
    pdir = project_dir(pid)
    out = os.path.join(pdir, "keyframes", f"shot_{shot_id:02d}.png")
    img.save(out)
    return out

# ---- Temporal prompt composer (PRIORITIZE the new shot) ----
def _compose_temporal_prompt(shots: list, idx: int, seconds_forward: int = 5) -> tuple[str, str]:
    """
    Build a prompt that continues the scene N seconds later,
    prioritizing the NEW shot description (composition/action),
    while keeping only identity/lighting/environment continuity.
    Returns (composed_prompt, composed_negative).
    """
    curr = shots[idx]
    curr_desc = (curr.get("description") or "").strip()
    curr_neg  = (curr.get("negative") or "").strip()

    if idx == 0:
        return curr_desc, curr_neg

    prev = shots[idx - 1]
    prev_desc = (prev.get("description") or "").strip()

    composed = (
        f"Continue the same scene {seconds_forward} seconds later.\n"
        f"PRIORITIZE this new moment and its composition now: \"{curr_desc}\".\n"
        f"Keep continuity ONLY for subject identity, lighting palette, time of day, and general environment style.\n"
        f"Previous frame (context only, do not copy its framing): \"{prev_desc}\".\n"
        f"Avoid replicating the previous composition; allow camera move / subject reposition consistent with {seconds_forward} seconds of natural progression."
    ).strip()

    negative = (
        curr_neg + (
            "; identical composition as previous; exact same framing; rigid pose repeat; freeze frame; "
            "hard scene reset; different subject identity; wildly different art style; unrelated background"
        )
    ).strip("; ")

    return composed, negative

@spaces.GPU(duration=180)
def generate_keyframe_image(
    pid: str,
    shot_idx: int,
    shots: list,
    t2i_steps: int = 18,         # FLUX: 12–22
    i2i_steps: int = 22,         # FLUX: 16–26
    i2i_strength: float = 0.90,  # ↑ more change toward new prompt
    guidance_scale: float = 3.4, # ↑ stronger text pull
    width: int = 640,
    height: int = 640,
    seconds_forward: int = 5,    # temporal step
    aggressive: bool = False     # optional push
):
    """
    Generate image for shots[shot_idx] using FLUX only.
    - Shot 1: text2img
    - Shot k>1: img2img from previous approved frame + temporal prompt ("N seconds later")
    """
    try:
        t2i, i2i = _lazy_flux_pipes()
    except Exception as e:
        raise gr.Error(
            f"FLUX failed to load: {e}\n"
            "Set FLUX_MODEL (e.g., 'black-forest-labs/FLUX.1-schnell') and ensure HF_TOKEN if required."
        )

    # Build temporal prompt
    composed_prompt, composed_negative = _compose_temporal_prompt(shots, shot_idx, seconds_forward=seconds_forward)

    # RNG / seed
    seed = shots[shot_idx].get("seed", None)
    device = "cuda" if USE_CUDA else "cpu"
    gen = torch.Generator(device)
    if isinstance(seed, int):
        gen = gen.manual_seed(int(seed))

    # sizes
    width  = max(256, min(1024, int(width)))
    height = max(256, min(1024, int(height)))

    # chaining
    prev_path = shots[shot_idx - 1].get("image_path") if shot_idx > 0 else None
    use_prev = bool(shot_idx > 0 and prev_path and os.path.exists(prev_path))

    # Aggressive mode bumps
    if aggressive:
        i2i_strength = min(0.98, max(i2i_strength, 0.92))
        guidance_scale = max(guidance_scale, 3.6)
        i2i_steps = max(i2i_steps, 24)

    # generate
    if not use_prev:
        out = t2i(
            prompt=composed_prompt,
            negative_prompt=composed_negative or None,
            num_inference_steps=int(max(10, t2i_steps)),
            guidance_scale=float(max(2.4, guidance_scale)),
            generator=gen,
            width=width, height=height
        ).images[0]
    else:
        init_image = Image.open(prev_path).convert("RGB")  # previous approved frame (the "init_image")
        out = i2i(
            prompt=composed_prompt,
            negative_prompt=composed_negative or None,
            image=init_image,
            strength=float(min(max(i2i_strength, 0.70), 0.98)),
            num_inference_steps=int(max(14, i2i_steps)),
            guidance_scale=float(max(2.4, guidance_scale)),
            generator=gen
        ).images[0]

    saved_path = _save_keyframe(pid, int(shots[shot_idx]["id"]), out)
    return saved_path

# =========================
# Shots <-> DataFrame utils
# =========================
SHOT_COLUMNS = ["id", "title", "description", "duration", "fps", "steps", "seed", "negative", "image_path"]

def shots_to_df(shots: list) -> pd.DataFrame:
    rows = [{k: s.get(k, None) for k in SHOT_COLUMNS} for s in shots]
    return pd.DataFrame(rows, columns=SHOT_COLUMNS)

def df_to_shots(df: pd.DataFrame) -> list:
    out = []
    for _, row in df.iterrows():
        out.append({
            "id": int(row["id"]),
            "title": (row["title"] or f"Shot {int(row['id'])}"),
            "description": row["description"] or "",
            "duration": int(row["duration"]) if pd.notna(row["duration"]) else 4,
            "fps": int(row["fps"]) if pd.notna(row["fps"]) else 24,
            "steps": int(row["steps"]) if pd.notna(row["steps"]) else 30,
            "seed": (int(row["seed"]) if pd.notna(row["seed"]) else None),
            "negative": row["negative"] or "",
            "image_path": row["image_path"] if pd.notna(row["image_path"]) else None
        })
    return sorted(out, key=lambda x: x["id"])

# =========================
# Gradio UI
# =========================
with gr.Blocks() as demo:
    gr.Markdown("# 🎬 Storyboard → Keyframes → (Videos soon) → Export")
    gr.Markdown(
        "Edit storyboard prompts, then generate keyframes.\n"
        "**Temporal chaining**: each new shot is generated N seconds later from the previous approved frame, "
        "while the current shot description drives composition & action. **Model**: FLUX-only."
    )

    # State
    project = gr.State(None)
    current_idx = gr.State(0)

    # Header
    with gr.Row():
        with gr.Column(scale=2):
            proj_name = gr.Textbox(label="Project name", placeholder="e.g., Desert Chase")
        with gr.Column(scale=1):
            new_btn = gr.Button("New Project", variant="primary")
        with gr.Column(scale=1):
            save_btn = gr.Button("Save Project")
        with gr.Column(scale=1):
            load_file = gr.File(label="Load Project (project.json)", file_count="single", type="filepath")
            load_btn = gr.Button("Load")
    sb_status = gr.Markdown("")

    # Tabs
    with gr.Tabs():
        with gr.Tab("Storyboard"):
            gr.Markdown("### 1) Storyboard")
            sb_prompt = gr.Textbox(label="High-level prompt", lines=4, placeholder="Describe the story you want to create…")
            with gr.Row():
                sb_target_shots = gr.Slider(1, 12, value=3, step=1, label="Target # of shots")
                sb_default_fps  = gr.Slider(8, 60, value=24, step=1, label="Default FPS")
                sb_default_len  = gr.Slider(1, 12, value=4, step=1, label="Default seconds per shot")
            propose_btn = gr.Button("Propose Storyboard (LLM on ZeroGPU)")
            shots_df    = gr.Dataframe(
                headers=SHOT_COLUMNS,
                datatype=["number","str","str","number","number","number","number","str","str"],
                row_count=(1,"dynamic"), col_count=len(SHOT_COLUMNS),
                label="Edit shots below (prompts & params)", wrap=True
            )
            save_edits_btn = gr.Button("Save Edits ✓", variant="primary", interactive=False)
            with gr.Row():
                proj_seed_box = gr.Number(label="Project Seed (locked across shots)", precision=0)
            to_keyframes_btn = gr.Button("Start Keyframes →", variant="secondary")

        with gr.Tab("Keyframes"):
            gr.Markdown("### 2) Keyframes")
            shot_info_md = gr.Markdown("")
            prompt_box   = gr.Textbox(label="Shot description (editable before generating)", lines=4)
            with gr.Row():
                gen_btn = gr.Button("Generate / Regenerate", variant="primary")
                approve_next_btn = gr.Button("Approve & Next →", variant="secondary")

            with gr.Row():
                img_strength = gr.Slider(0.50, 0.98, value=0.90, step=0.02, label="Change vs Consistency (img2img strength)")
                img_steps    = gr.Slider(12, 28, value=22, step=1, label="Inference Steps (img2img)")
                guidance     = gr.Slider(2.4, 4.0, value=3.4, step=0.1, label="Guidance Scale")
                temporal_secs = gr.Slider(1, 10, value=5, step=1, label="Temporal step (seconds later)")
                aggressive_follow = gr.Checkbox(value=False, label="Aggressive follow prompt (more change)")

            with gr.Row():
                prev_img = gr.Image(label="Previous approved image (conditioning)", type="filepath")
                out_img  = gr.Image(label="Generated image", type="filepath")
            kf_status = gr.Markdown("")

        with gr.Tab("Videos"):
            gr.Markdown("### 3) Videos (coming next)")
            vd_table = gr.JSON(label="Planned clip edges (read-only for now)")

        with gr.Tab("Export"):
            gr.Markdown("### 4) Export (coming next)")
            export_info = gr.Markdown("Nothing to export yet.")

    # ---------- Handlers ----------
    def on_new(name):
        p = ensure_project(None, suggested_name=(name or "Project"))
        return p, gr.update(value=f"**New project created** `{p['meta']['name']}` (id: `{p['meta']['id']}`)")

    new_btn.click(on_new, inputs=[proj_name], outputs=[project, sb_status])

    def on_propose(p, prompt, target_shots, fps, vlen):
        p = ensure_project(p, suggested_name=(proj_name.value if hasattr(proj_name, "value") else "Project"))
        if not prompt or not str(prompt).strip():
            raise gr.Error("Please enter a high-level prompt.")
        shots = generate_storyboard_with_llm(str(prompt).strip(), int(target_shots), int(fps), int(vlen))
        p = dict(p)
        p["shots"] = shots
        p["meta"]["updated"] = now_iso()
        save_project(p)
        return p, shots_to_df(shots), gr.update(value="Storyboard generated (editable)."), gr.update(interactive=True)

    propose_btn.click(
        on_propose,
        inputs=[project, sb_prompt, sb_target_shots, sb_default_fps, sb_default_len],
        outputs=[project, shots_df, sb_status, save_edits_btn]
    )

    def on_save_edits(p, df):
        if p is None:
            raise gr.Error("No project in memory. Click New Project, then generate a storyboard.")
        if df is None:
            raise gr.Error("No storyboard table to save. Generate a storyboard first, then edit it.")
        shots = df_to_shots(df)
        p = dict(p)
        p["shots"] = shots
        p["meta"]["updated"] = now_iso()
        save_project(p)
        return p, gr.update(value="Edits saved.")

    save_edits_btn.click(on_save_edits, inputs=[project, shots_df], outputs=[project, sb_status])

    def on_start_keyframes(p, df, proj_seed_override):
        if p is None: raise gr.Error("No project.")
        shots = df_to_shots(df)
        if not shots: raise gr.Error("Storyboard is empty.")

        # lock a single seed for the project:
        proj_seed = None
        if proj_seed_override not in [None, ""] and str(proj_seed_override).isdigit():
            proj_seed = int(proj_seed_override)
        if proj_seed is None:
            proj_seed = p.get("meta", {}).get("seed", None)
        if proj_seed is None:
            for s in shots:
                if isinstance(s.get("seed"), int):
                    proj_seed = int(s["seed"])
                    break
        if proj_seed is None:
            proj_seed = int(torch.randint(0, 2**31 - 1, (1,)).item())

        for s in shots:
            if not isinstance(s.get("seed"), int):
                s["seed"] = proj_seed

        p = dict(p)
        p["shots"] = shots
        p["meta"]["seed"] = proj_seed
        p["meta"]["updated"] = now_iso()
        save_project(p)

        idx = 0
        prev_path = None
        info = (
            f"**Shot {shots[idx]['id']} — {shots[idx]['title']}**  \n"
            f"Duration: {shots[idx]['duration']}s @ {shots[idx]['fps']} fps  \n"
            f"Locked project seed: `{proj_seed}`"
        )
        return p, 0, gr.update(value=info), gr.update(value=shots[idx]["description"]), gr.update(value=prev_path), gr.update(value=None), gr.update(value=f"Ready to generate shot 1."), gr.update(value=proj_seed)

    to_keyframes_btn.click(
        on_start_keyframes,
        inputs=[project, shots_df, proj_seed_box],
        outputs=[project, current_idx, shot_info_md, prompt_box, prev_img, out_img, kf_status, proj_seed_box]
    )

    def on_generate_img(p, idx, current_prompt, i2i_strength_val, i2i_steps_val, guidance_val, seconds_forward_val, aggressive_val):
        if p is None: raise gr.Error("No project.")
        shots = p["shots"]
        if idx < 0 or idx >= len(shots): raise gr.Error("Invalid shot index.")
        shots[idx]["description"] = current_prompt  # allow tweaking

        img_path = generate_keyframe_image(
            p["meta"]["id"],
            int(idx),
            shots,
            t2i_steps=18,
            i2i_steps=int(i2i_steps_val),
            i2i_strength=float(i2i_strength_val),
            guidance_scale=float(guidance_val),
            width=640,
            height=640,
            seconds_forward=int(seconds_forward_val),
            aggressive=bool(aggressive_val)
        )
        prev_path = shots[idx-1]["image_path"] if idx > 0 else None
        return img_path, (prev_path or None), gr.update(value=f"Generated candidate for shot {shots[idx]['id']}.")

    gen_btn.click(
        on_generate_img,
        inputs=[project, current_idx, prompt_box, img_strength, img_steps, guidance, temporal_secs, aggressive_follow],
        outputs=[out_img, prev_img, kf_status]
    )

    def on_approve_next(p, idx, current_prompt, latest_img_path):
        if p is None: raise gr.Error("No project.")
        shots = p["shots"]
        i = int(idx)
        if i < 0 or i >= len(shots): raise gr.Error("Invalid shot index.")
        if not latest_img_path: raise gr.Error("Generate an image first.")

        # commit
        shots[i]["description"] = current_prompt
        shots[i]["image_path"] = latest_img_path
        p["shots"] = shots
        p["meta"]["updated"] = now_iso()
        save_project(p)

        # next
        if i + 1 < len(shots):
            ni = i + 1
            info = (
                f"**Shot {shots[ni]['id']} — {shots[ni]['title']}**  \n"
                f"Duration: {shots[ni]['duration']}s @ {shots[ni]['fps']} fps  \n"
                f"Locked project seed: `{p['meta'].get('seed')}`"
            )
            prev_path = shots[ni-1]["image_path"]
            return p, ni, gr.update(value=info), gr.update(value=shots[ni]["description"]), gr.update(value=prev_path), gr.update(value=None), gr.update(value=f"Approved shot {shots[i]['id']}. On to shot {shots[ni]['id']}.")
        else:
            return p, i, gr.update(value="**All keyframes approved.** Proceed to Videos tab."), gr.update(value=""), gr.update(value=shots[i]["image_path"]), gr.update(value=None), gr.update(value="All shots approved ✅")

    approve_next_btn.click(on_approve_next, inputs=[project, current_idx, prompt_box, out_img], outputs=[project, current_idx, shot_info_md, prompt_box, prev_img, out_img, kf_status])

    def on_save(p):
        if p is None:
            raise gr.Error("No project in memory.")
        path = save_project(p)
        return gr.update(value=f"Saved to `{path}`")

    save_btn.click(on_save, inputs=[project], outputs=[sb_status])

    def on_load(file_obj):
        p = load_project_file(file_obj)
        seed_val = p.get("meta", {}).get("seed", None)
        return (
            p,
            gr.update(value=f"Loaded project `{p['meta']['name']}` (id: `{p['meta']['id']}`)"),
            shots_to_df(p.get("shots", [])),
            gr.update(value=seed_val)
        )

    load_btn.click(on_load, inputs=[load_file], outputs=[project, sb_status, shots_df, proj_seed_box])

if __name__ == "__main__":
    _flux_healthcheck()  # fail fast with clear error if FLUX isn't available
    demo.launch()