app.py

import os, json, uuid, re
from datetime import datetime
import gradio as gr
import spaces  # ZeroGPU decorator
import torch

# =========================
# 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"])  # ensure dirs
    return proj

# =========================
# LLM (ZeroGPU) — Storyboard generator (robust, two-pass + empty fallback)
# =========================
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"))  # give a bit more room

_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,
    )
    # Ensure pad token exists to avoid warnings
    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"
        f"  \"video_length\": {default_len},\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"
        f"  \"video_length\": {default_len},\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
    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=tok.pad_token_id if tok.pad_token_id is not None else eos_id,
    )
    text = tok.decode(gen[0], skip_special_tokens=True)
    if text.startswith(prompt_text):
        text = text[len(prompt_text):]
    # strip code fences if present
    text = text.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
    # Fallback: first balanced array
    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)),
            "video_length": int(s.get("video_length", default_len)),
            "steps": int(s.get("steps", 30)),
            "seed": s.get("seed", None),
            "negative": s.get("negative", ""),
            "keyframe_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):
    """
    Two-pass generation with robust parsing and empty-output fallback.
    """
    model, tok = _lazy_model_tok()
    system = "You are a film previsualization assistant. Output must be valid JSON."

    # PASS 1: with <JSON> tags
    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)
    print(f"[DEBUG] LLM raw out1 (first 240 chars): {out1[:240]}")
    json_text = _extract_json_array(out1)

    # PASS 2: strict array fallback
    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)
        print(f"[DEBUG] LLM raw out2 (first 240 chars): {out2[:240]}")
        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 → return a single stub so the app does not crash
    if not json_text or not json_text.strip():
        print("⚠️ LLM returned empty or unparsable JSON. Using fallback storyboard.")
        return [{
            "id": 1,
            "title": "Shot 1",
            "description": f"Fallback shot for: {user_prompt[:80]}",
            "duration": default_len,
            "fps": default_fps,
            "video_length": default_len,
            "steps": 30,
            "seed": None,
            "negative": "",
            "keyframe_path": None
        }]

    # Parse & normalize (with tiny trailing-comma cleanup)
    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)

# =========================
# Gradio UI
# =========================
with gr.Blocks() as demo:
    gr.Markdown("# 🎬 Storyboard → Keyframes → Videos → Export")
    gr.Markdown("**Step 2**: Real storyboard generation on **ZeroGPU**. Next steps will add keyframes (img2img) and your Modal videos.")

    # Global state
    project = gr.State(None)        # dict with meta/shots/clips
    current_tab = gr.State("Storyboard")

    # Header row
    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")

    # 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_json   = gr.JSON(label="Storyboard JSON (editable in next step)")
            confirm_btn  = gr.Button("Confirm Storyboard ✓", variant="primary")
            sb_status    = gr.Markdown("")

        with gr.Tab("Keyframes"):
            gr.Markdown("### 2) Keyframes (coming next)")
            kf_table = gr.JSON(label="Shots (read-only for now)")
            to_videos_btn = gr.Button("Continue to Videos →", interactive=False)

        with gr.Tab("Videos"):
            gr.Markdown("### 3) Videos (coming next)")
            vd_table = gr.JSON(label="Planned clip edges (read-only for now)")
            to_export_btn = gr.Button("Continue to Export →", interactive=False)

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

    # -------- Handlers --------
    def on_new(name):
        name = (name or "").strip() or f"Project-{new_id()}"
        pid = new_id()
        p = {
            "meta": {"id": pid, "name": name, "created": now_iso(), "updated": now_iso()},
            "shots": [],
            "clips": []
        }
        save_project(p)
        return p, gr.update(value=f"**New project created** `{name}` (id: `{pid}`)")

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

    def on_propose(p, prompt, target_shots, fps, vlen):
        if p is None:
            raise gr.Error("Create a project first (New 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, gr.update(value="Storyboard generated by LLM (ZeroGPU).")

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

    def on_confirm(p):
        if p is None or not p.get("shots"):
            raise gr.Error("No storyboard yet.")
        edges = []
        for i in range(len(p["shots"]) - 1):
            a = p["shots"][i]["id"]
            b = p["shots"][i+1]["id"]
            edges.append({"from": a, "to": b, "prompt": f"Transition from shot {a} to {b}"})
        p = dict(p)
        p["clips"] = edges
        p["meta"]["updated"] = now_iso()
        save_project(p)
        return (
            p,
            gr.update(value=p["shots"]),
            gr.update(value=p["clips"]),
            gr.update(value="Storyboard confirmed. Proceed to Keyframes."),
            gr.update(interactive=True)
        )

    confirm_btn.click(
        on_confirm,
        inputs=[project],
        outputs=[project, kf_table, vd_table, sb_status, to_videos_btn]
    )

    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']}`)"),
            gr.update(value=p["shots"]),
            gr.update(value=p["clips"]),
            gr.update(interactive=bool(p.get("shots")))
        )

    load_btn.click(
        on_load,
        inputs=[load_file],
        outputs=[project, sb_status, kf_table, vd_table, to_videos_btn]
    )

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