app.py

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)
    _model = AutoModelForCausalLM.from_pretrained(
        STORYBOARD_MODEL,
        device_map="auto",
        dtype="auto",
        trust_remote_code=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 (
        "Return ONLY a JSON array, enclosed between <JSON> and </JSON>.\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 title\",\n'
        '  \"description\": \"Visual description for keyframe generation\",\n'
        f"  \"duration\": {default_len},\n"
        f"  \"fps\": {default_fps},\n"
        "  \"steps\": 30,\n"
        "  \"seed\": null,\n"
        '  \"negative\": \"\"\n'
        "}\n\n"
        "Output:\n<JSON>\n[ { ... }, ... ]\n</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:
    """Decode only the continuation (avoid prompt echo)."""
    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
    for i in range(start, len(text)):
        ch = text[i]
        if ch == "[":
            depth += 1
        elif ch == "]":
            depth -= 1
            if depth == 0:
                return text[start:i+1].strip()
    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."

    # Pass 1
    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)

    # Pass 2
    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()

    # Empty fallback
    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 (ZeroGPU) — SD1.5 text2img + img2img chaining
# =========================

from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline

SD_MODEL = os.getenv("SD_MODEL", "stabilityai/sd-turbo")
_sd_t2i = None
_sd_i2i = None

def _lazy_sd_pipes():
    """
    Load SD once in a version-safe way:
      - torch_dtype (not dtype)
      - low_cpu_mem_usage=False to avoid offload_state_dict kwarg
      - no revision pin (some repos don't have 'fp16' branch)
      - optional HF token if set (for gated models)
    """
    global _sd_t2i, _sd_i2i
    if _sd_t2i is not None and _sd_i2i is not None:
        return _sd_t2i, _sd_i2i

    use_cuda = torch.cuda.is_available()
    dtype = torch.float16 if use_cuda else torch.float32
    hf_token = os.getenv("HF_TOKEN", None)  # add this in Space Secrets only if needed

    _sd_t2i = StableDiffusionPipeline.from_pretrained(
        SD_MODEL,
        torch_dtype=dtype,
        safety_checker=None,
        feature_extractor=None,
        use_safetensors=True,
        low_cpu_mem_usage=False,
        token=hf_token
    )
    if use_cuda:
        _sd_t2i = _sd_t2i.to("cuda")

    _sd_i2i = StableDiffusionImg2ImgPipeline(
        vae=_sd_t2i.vae,
        text_encoder=_sd_t2i.text_encoder,
        tokenizer=_sd_t2i.tokenizer,
        unet=_sd_t2i.unet,
        scheduler=_sd_t2i.scheduler,
        safety_checker=None,
        feature_extractor=None
    )
    if use_cuda:
        _sd_i2i = _sd_i2i.to("cuda")

    return _sd_t2i, _sd_i2i

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

@spaces.GPU(duration=180)
def generate_keyframe_image(
    pid: str,
    shot_idx: int,
    shots: list,
    guidance_scale: float = 7.5,
    strength: float = 0.35
):
    """
    Generate image for shots[shot_idx].
    - shot 0: text2img
    - shot k>0: img2img using previous approved image as conditioning (if available)
    """
    t2i, i2i = _lazy_sd_pipes()
    shot = shots[shot_idx]
    prompt = shot.get("description", "")
    negative = shot.get("negative") or ""
    steps = int(shot.get("steps", 30))
    seed = shot.get("seed", None)

    gen = torch.Generator("cuda" if torch.cuda.is_available() else "cpu")
    if isinstance(seed, int):
        gen = gen.manual_seed(seed)

    if shot_idx == 0 or not shots[shot_idx - 1].get("image_path"):
        out = t2i(
            prompt=prompt,
            negative_prompt=negative,
            guidance_scale=guidance_scale,
            num_inference_steps=steps,
            generator=gen
        ).images[0]
    else:
        prev_path = shots[shot_idx - 1]["image_path"]
        init_image = Image.open(prev_path).convert("RGB")
        out = i2i(
            prompt=prompt,
            negative_prompt=negative,
            image=init_image,
            guidance_scale=guidance_scale,
            strength=strength,
            num_inference_steps=steps,
            generator=gen
        ).images[0]

    saved_path = _save_keyframe(pid, int(shot["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 → Export")
    gr.Markdown("**Edit storyboard prompts**, then generate keyframes. Each next shot uses the **previous approved image** as reference.")

    # 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)
            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():
                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)
        # Enable Save Edits after storyboard exists
        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]
    )

    # Defensive save handler (works even if user clicks too early)
    def on_save_edits(*args):
        p = args[0] if len(args) > 0 else None
        df = args[1] if len(args) > 1 else None
        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):
        if p is None: raise gr.Error("No project.")
        shots = df_to_shots(df)
        if not shots: raise gr.Error("Storyboard is empty.")
        p = dict(p); p["shots"] = shots; p["meta"]["updated"] = now_iso(); save_project(p)
        idx = 0
        prev_path = None
        info = f"**Shot {shots[idx]['id']} — {shots[idx]['title']}**  \nDuration: {shots[idx]['duration']}s @ {shots[idx]['fps']} fps"
        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="Ready to generate shot 1.")

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

    def on_generate_img(p, idx, current_prompt):
        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 before generation
        prev_path = shots[idx-1]["image_path"] if idx > 0 else None
        img_path = generate_keyframe_image(p["meta"]["id"], int(idx), shots)
        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], 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']}**  \nDuration: {shots[ni]['duration']}s @ {shots[ni]['fps']} fps"
            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)
        return (
            p,
            gr.update(value=f"Loaded project `{p['meta']['name']}` (id: `{p['meta']['id']}`)"),
            shots_to_df(p.get("shots", [])),
        )

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

if __name__ == "__main__":
    demo.launch()