app.py

import os, io, time, base64, random, subprocess
from typing import Optional, List
from urllib.parse import quote

import requests
from PIL import Image
import gradio as gr

# -------- Modal inference endpoint (dev) --------
INFERENCE_URL = "https://moonmath-ai-dev--moonmath-i2v-backend-moonmathinference-run.modal.run"

# -------- settings --------
MAX_SLOTS = 12          # max image slots user can reveal

# -------- small helpers --------
def _save_video_bytes(data: bytes, tag: str) -> str:
    os.makedirs("/tmp", exist_ok=True)
    path = f"/tmp/{tag}_{int(time.time())}.mp4"
    with open(path, "wb") as f:
        f.write(data)
    return path

def _png_bytes(img: Image.Image) -> bytes:
    buf = io.BytesIO()
    img.save(buf, format="PNG")
    return buf.getvalue()

def _download_to_bytes(url: str) -> bytes:
    r = requests.get(url, timeout=180)
    r.raise_for_status()
    return r.content

def stitch_call(start_img: Image.Image, end_img: Image.Image, prompt: str, seed: Optional[int]) -> Optional[str]:
    """
    Calls your Modal backend with two images + prompt + seed and returns a local /tmp video path.
    """
    if start_img is None or end_img is None:
        return None

    if seed in (None, 0, -1):
        seed = random.randint(1, 2**31 - 1)

    url = f"{INFERENCE_URL}?prompt={quote(prompt or '')}&seed={seed}"
    files = {
        "image_bytes": ("start.png", _png_bytes(start_img), "image/png"),
        "image_bytes_end": ("end.png", _png_bytes(end_img), "image/png"),
    }
    headers = {"accept": "application/json"}

    try:
        resp = requests.post(url, files=files, headers=headers, timeout=600)
        ctype = (resp.headers.get("content-type") or "").lower()

        # Raw video bytes
        if "application/json" not in ctype:
            resp.raise_for_status()
            return _save_video_bytes(resp.content, "stitch")

        # JSON with url or base64
        data = resp.json()
        video_url = data.get("video_url") or data.get("url") or data.get("result") or data.get("output")
        if isinstance(video_url, str) and video_url.startswith(("http://", "https://")):
            return _save_video_bytes(_download_to_bytes(video_url), "stitch")

        video_b64 = data.get("video_b64") or data.get("videoBase64")
        if isinstance(video_b64, str):
            pad = (-len(video_b64)) % 4
            if pad:
                video_b64 += "=" * pad
            return _save_video_bytes(base64.b64decode(video_b64), "stitch")

    except Exception as e:
        print("stitch_call error:", e)

    return None

# -------- FFmpeg-based concatenation (N clips) --------
def concat_many(videos: List[str]) -> Optional[str]:
    vids = [v for v in videos if v]
    if len(vids) < 2:
        return None
    try:
        os.makedirs("/tmp", exist_ok=True)
        out_path = f"/tmp/final_{int(time.time())}.mp4"
        list_file = f"/tmp/list_{int(time.time())}.txt"
        with open(list_file, "w") as f:
            for v in vids:
                f.write(f"file '{v}'\n")
        subprocess.run(
            ["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", list_file, "-c", "copy", out_path],
            check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE
        )
        return out_path
    except Exception as e:
        print("concat_many error:", e)
        return None

# -------- Timeline HTML renderer --------
def render_timeline_html(paths: List[str]):
    vids = [p for p in (paths or []) if p]
    if not vids:
        return "<div class='tl-grid tl-empty'>No clips yet. Generate and click ‘Add to timeline’.</div>"
    items = []
    for i, p in enumerate(vids, 1):
        items.append(
            f"""
            <div class="tl-item">
              <video src="{p}" controls playsinline></video>
              <div class="tl-label">Clip {i}</div>
            </div>
            """
        )
    return f"<div class='tl-grid'>{''.join(items)}</div>"

# =========================
# Gradio callbacks / state ops
# =========================
def add_image_slot(visible_slots: int):
    """Reveal one more upload slot (up to MAX_SLOTS)."""
    return min(MAX_SLOTS, int(visible_slots) + 1)

def _reveal_slots(n, *imgs):
    """Update visibility of image upload components based on visible_slots state."""
    n = int(n)
    updates = []
    for i in range(MAX_SLOTS):
        updates.append(gr.update(visible=(i < n)))
    return updates

def collect_choices(*imgs):
    """Build dropdown choices of available indices (1-based labels) based on non-empty slots."""
    choices = []
    for i, img in enumerate(imgs, start=1):
        if img is not None:
            choices.append(str(i))
    return gr.update(choices=choices), gr.update(choices=choices)

def stitch_selected(prompt, seed, start_idx_str, end_idx_str, *imgs):
    """Run inference for selected start/end indices (1-based strings)."""
    if not start_idx_str or not end_idx_str:
        gr.Warning("Please select Start and End frames.")
        return None
    try:
        s = int(start_idx_str) - 1
        e = int(end_idx_str) - 1
    except Exception:
        gr.Warning("Invalid Start/End selection.")
        return None

    if s < 0 or e < 0 or s >= len(imgs) or e >= len(imgs):
        gr.Warning("Start/End out of range.")
        return None

    start_img = imgs[s]
    end_img = imgs[e]
    if start_img is None or end_img is None:
        gr.Warning("Selected slots are empty.")
        return None

    vid = stitch_call(start_img, end_img, prompt or "", int(seed or 0))
    if not vid:
        gr.Warning("Generation failed.")
        return None
    return vid  # path for preview

def add_to_timeline(preview_path, timeline_paths: List[str]):
    """Append preview to timeline; return updated state and HTML."""
    tl = list(timeline_paths or [])
    if not preview_path:
        gr.Warning("Generate a clip first.")
        return tl, gr.update(value=render_timeline_html(tl))
    tl.append(preview_path)
    return tl, gr.update(value=render_timeline_html(tl))

def stitch_all_from_timeline(timeline_paths: List[str]):
    vids = list(timeline_paths or [])
    if len(vids) < 2:
        gr.Warning("Add at least two clips to the timeline first.")
        return None
    out = concat_many(vids)
    if not out:
        gr.Warning("Failed to concatenate clips.")
    return out

# =========================
# UI
# =========================
CSS = """
.gradio-container { padding: 24px; }
.pill button { border-radius: 999px !important; padding: 10px 18px; }
.rounded textarea { border-radius: 16px !important; }
.gallery-row { display:flex; gap:16px; overflow-x:auto; padding:8px 4px; }
.gallery-row .gradio-image { min-width: 220px; }
.tl-grid {
  display: grid;
  grid-template-columns: repeat(auto-fill, minmax(180px, 1fr));
  gap: 12px;
}
.stitch-box {
  background-color: #f0f4ff;   /* pick any color you like */
  border-radius: 12px;
  padding: 16px;
}
.tl-grid video {
  width: 100%;
  height: 120px;
  object-fit: cover;
  border-radius: 12px;
  display: block;
}
.tl-label {
  font-size: 12px;
  color: #9aa0a6;
  margin-top: 4px;
  text-align: center;
}
.tl-empty { color: #9aa0a6; padding: 8px 4px; }
"""

with gr.Blocks(css=CSS, title="StitchTool") as demo:
    gr.Markdown("## StitchTool")

    # --- State ---
    visible_slots = gr.State(value=3)      # number of visible image slots
    timeline_state = gr.State(value=[])    # list[str] of video file paths (timeline)

    # --- Image gallery (horizontal, grows on demand) ---
    with gr.Row(elem_classes=["gallery-row"]):
        img_comps = []
        for i in range(MAX_SLOTS):
            comp = gr.Image(label=f"Image {i+1} upload", type="pil", visible=(i < 3))
            img_comps.append(comp)
        add_btn = gr.Button("＋ Add image")

    # clicking add → reveal one more slot
    add_btn.click(
        fn=add_image_slot,
        inputs=[visible_slots],
        outputs=[visible_slots],
    )

    # reflect visibility changes whenever visible_slots changes
    visible_slots.change(
        fn=_reveal_slots,
        inputs=[visible_slots] + img_comps,
        outputs=img_comps
    )

    # Seed + Start/End selection + Prompt + Stitch + Preview
    seed = gr.Number(value=0, precision=0, label="Seed (0 = random)")

    with gr.Row():
        # Left column: controls (with colored background via .stitch-box)
        with gr.Column(scale=1, min_width=420, elem_classes=["stitch-box"]):
            start_dd = gr.Dropdown(label="Start frame", choices=[], interactive=True)
            end_dd = gr.Dropdown(label="End frame", choices=[], interactive=True)
            prompt = gr.Textbox(
                placeholder="Describe the transition between the selected start and end frames…",
                lines=3,
                label="Prompt",
                elem_classes=["rounded"]
            )
            run_btn = gr.Button("Generate", elem_classes=["pill"])
            add_tl_btn = gr.Button("Add to timeline", elem_classes=["pill"])

        # Right column: preview video
        with gr.Column(scale=1, min_width=420):
            preview = gr.Video(label="Video output", interactive=False)

    # keep start/end dropdowns up to date based on which slots have images
    for comp in img_comps:
        comp.change(
            fn=collect_choices,
            inputs=img_comps,
            outputs=[start_dd, end_dd]
        )

    # stitch action → preview
    run_btn.click(
        fn=stitch_selected,
        inputs=[prompt, seed, start_dd, end_dd] + img_comps,
        outputs=[preview]
    )

    # --- Dynamic timeline (no placeholders) ---
    with gr.Row():
        timeline_html = gr.HTML(value=render_timeline_html([]))

    add_tl_btn.click(
        fn=add_to_timeline,
        inputs=[preview, timeline_state],
        outputs=[timeline_state, timeline_html]
    )

    # final stitch all (concatenate in order)
    with gr.Row():
        with gr.Column(scale=1, min_width=420):
            stitch_all_btn = gr.Button("Stitch All", elem_classes=["pill"])
        with gr.Column(scale=1, min_width=420):
            final_vid = gr.Video(label="Stitched Video Output", interactive=False)

    stitch_all_btn.click(
        fn=stitch_all_from_timeline,
        inputs=[timeline_state],
        outputs=[final_vid]
    )

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