pandora_p_for_power/generate.py

from pathlib import Path
import hashlib
import math
import os
import time
import shutil

import cv2
import ox
import requests
import fal_client
from byteplussdkarkruntime import Ark

from django.conf import settings

from item.models import Item
from document.models import Document
from archive.models import File, Stream


os.environ["FAL_KEY"] = settings.FAL_KEY

MAX_DURATION = 12
headers = {
    "Authorization": "Bearer " + settings.BYTEPLUSE_TOKEN,
    "Content-Type": "application/json",
}


def public_url(path):
    return path.replace("/srv/pandora/static/", settings.PUBLIC_URL + "static/")


def public_document_url(document):
    url = "%sdocuments/%s/source.%s?token=%s" % (
        settings.PUBLIC_URL,
        ox.toAZ(document.id),
        document.extension,
        settings.PUBLIC_TOKEN,
    )
    return url


def public_video_url(item):
    url = "%s%s/download/source/?token=%s" % (
        settings.PUBLIC_URL,
        item.public_id,
        settings.PUBLIC_TOKEN,
    )
    return url


def public_frame_url(item, position):
    image = "%s%s/source%s.png?token=%s" % (
        settings.PUBLIC_URL,
        item.public_id,
        position,
        settings.PUBLIC_TOKEN,
    )
    return image


def trim_video(src, dst, frames, start0=False):
    cap = cv2.VideoCapture(src)
    fps = cap.get(cv2.CAP_PROP_FPS)
    frames_src = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    frame_count = 0

    offset = int((frames_src - frames) / 2)
    if start0:
        offset = 0
    print(frames_src, frames, offset)
    fourcc = cv2.VideoWriter_fourcc(*"avc1")
    out = cv2.VideoWriter(dst, fourcc, fps, (width, height))
    written = 0
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        frame_count += 1
        if frame_count < offset:
            continue
        if frame_count >= (frames + offset):
            continue
        out.write(frame)
        written += 1
    out.release()
    cap.release()


def bytedance_task(data):
    url = "https://ark.ap-southeast.bytepluses.com/api/v3/contents/generations/tasks"
    model = "seedance-1-5-pro-251215"
    resolution = "720p"
    defaults = {
        "model": model,
        "generate_audio": False,
        "ratio": "16:9",
        "watermark": False,
        "resolution": resolution,
        "camera_fixed": True,
        "return_last_frame": True,
    }
    for key, value in defaults.items():
        if key not in data:
            data[key] = value
    if data["model"] in EP:
        data["model"] = EP[data["model"]]
    print(data)
    r = requests.post(url, headers=headers, json=data).json()
    print(r)
    task_id = r["id"]
    status = requests.get(url + "/" + task_id, headers=headers).json()
    while status["status"] in ("queued", "running", "cancelled"):
        time.sleep(10)
        status = requests.get(url + "/" + task_id, headers=headers).json()
    print(status)
    return status


def bytedance_response(data):
    url = "https://ark.ap-southeast.bytepluses.com/api/v3/responses"
    defaults = {"model": "seed-1-8-251228"}
    for key, value in defaults.items():
        if key not in data:
            data[key] = value
    print(data)
    if data["model"] in EP:
        data["model"] = EP[data["model"]]
    response = requests.post(url, headers=headers, json=data).json()
    print(response)
    return response


def t2v_bytedance(prompt, duration, output):
    nduration = max(4, int(math.ceil(duration)))
    data = {
        "duration": nduration,
        "content": [{"type": "text", "text": prompt}],
    }
    status = bytedance_task(data)
    output_url = status["content"]["video_url"]
    ox.net.save_url(output_url, output, overwrite=True)
    if "last_frame_url" in status["content"]:
        ox.net.save_url(
            status["content"]["last_frame_url"],
            output + ".last_frame.png",
            overwrite=True,
        )
    return status


def i2v_bytedance(first_frame, prompt, duration, output, last_frame=None, seed=None):
    nduration = max(4, int(math.ceil(duration)))
    data = {
        "duration": nduration,
        "content": [
            {
                "type": "text",
                "text": prompt,
            },
            {
                "type": "image_url",
                "role": "first_frame",
                "image_url": {"url": first_frame},
            },
        ],
    }
    if last_frame:
        data["content"].append(
            {
                "type": "image_url",
                "role": "last_frame",
                "image_url": {"url": last_frame},
            }
        )
    if seed:
        data["seed"] = seed
    status = bytedance_task(data)
    output_url = status["content"]["video_url"]
    ox.net.save_url(output_url, output, overwrite=True)
    if "last_frame_url" in status["content"]:
        ox.net.save_url(
            status["content"]["last_frame_url"],
            output + ".last_frame.png",
            overwrite=True,
        )
    return status


def first_last(first_frame, last_frame, prompt, duration, output):
    nduration = max(4, int(math.ceil(duration)))
    data = {
        "duration": nduration,
        "content": [
            {
                "type": "text",
                "text": prompt,
            },
            {
                "type": "image_url",
                "role": "first_frame",
                "image_url": {"url": first_frame},
            },
            {
                "type": "image_url",
                "role": "last_frame",
                "image_url": {"url": last_frame},
            },
        ],
    }
    status = bytedance_task(data)
    output_url = status["content"]["video_url"]
    ox.net.save_url(output_url, output, overwrite=True)
    if "last_frame_url" in status["content"]:
        ox.net.save_url(
            status["content"]["last_frame_url"],
            output + ".last_frame.png",
            overwrite=True,
        )
    return status


def get_item_segments(item, max_duration=MAX_DURATION):
    cuts = item.get("cuts")
    filename = item.files.all()[0].data.path
    input_info = ox.avinfo(filename)
    p = 0
    nc = []
    for c in cuts:
        d = c - p
        if d < 0.5:
            continue
        p = c
        nc.append(c)
    nc = nc + [input_info["duration"]]
    if len(nc) > 3:
        if nc[-1] - nc[-2] < 0.5:
            nc = nc[:-2] + nc[-1:]
    segments = []
    position = 0
    for out in nc:
        duration = out - position
        while duration > max_duration:
            position += max_duration
            if len(segments):
                segments.append(["c", position])
            else:
                segments.append(position)
            duration = out - position
        else:
            segments.append(out)
        position = out
    return segments


def join_segments(processed, joined_output):
    out = None
    for filename in processed:
        cap = cv2.VideoCapture(filename)
        if out is None:
            fps = cap.get(cv2.CAP_PROP_FPS)
            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
            fourcc = cv2.VideoWriter_fourcc(*"avc1")
            out = cv2.VideoWriter(joined_output, fourcc, fps, (width, height))
        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            out.write(frame)
        cap.release()
    if out is not None:
        out.release()


def remake_video(item_id, prompt):
    item = Item.objects.get(public_id=item_id)
    segments = get_item_segments(item)
    print(segments)
    prompt_hash = hashlib.sha1(prompt.encode()).hexdigest()
    position = n = 0
    processed = []
    prefix = "/srv/pandora/static/power/cache/%s_%s" % (item.public_id, prompt_hash)
    for segment in segments:
        if isinstance(segment, list):
            stype, segment = segment
        else:
            stype = "n"
        duration = segment - position

        if stype == "c":
            first_frame_path = "%s/%06d.mp4.last_frame.png" % (prefix, n - 1)
            first_frame = public_url(first_frame_path)
        else:
            first_frame = "%s%s/source%s.png?token=%s" % (
                settings.PUBLIC_URL,
                item.public_id,
                position,
                settings.PUBLIC_TOKEN,
            )
        last_frame_position = segment - 2 / 24
        last_frame = "%s%s/source%0.3f.png?token=%s" % (
            settings.PUBLIC_URL,
            item.public_id,
            last_frame_position,
            settings.PUBLIC_TOKEN,
        )
        output = "%s/%06d.mp4" % (prefix, n)
        if not os.path.exists(output):
            first_last(first_frame, last_frame, prompt, duration, output)
        trimmed = "%s/%06d_trimmed.mp4" % (prefix, n)
        frames = int(duration * 24)
        if not os.path.exists(trimmed):
            trim_video(output, trimmed, frames, stype == "c")
        processed.append(trimmed)
        position = segment
        n += 1

    joined_output = "%s/joined.mp4" % (prefix,)
    join_segments(processed, joined_output)
    return joined_output


EP = {
    "seedream-4-5-251128": "ep-20260122071519-pbf7l",
    "seed-1-8-251228": "ep-20260122071243-8qfrk",
    "seedance-1-5-pro-251215": "ep-20260122071613-blmsd",
}

def bytedance_image_generation(data):
    model = "seedream-4-5-251128"
    url = "https://ark.ap-southeast.bytepluses.com/api/v3/images/generations"
    defaults = {
        "model": model,
        "size": "2560x1440",
        "watermark": False,
    }
    for key in defaults:
        if key not in data:
            data[key] = defaults[key]
    if data["model"] in EP:
        data["model"] = EP[data["model"]]
    print("prepare_image", data)
    response = requests.post(url, headers=headers, json=data).json()
    print(response)
    return response["data"][0]["url"]


def prepare_image(image, prompt, out=None):
    if not image.startswith("http:"):
        image = public_url(image)
    data = {
        "prompt": prompt,
        "image": image,
        "size": "2560x1440",
    }
    output_url = bytedance_image_generation(data)
    if out is None:
        out = image + ".ai.png"
    ox.net.save_url(output_url, out, overwrite=True)
    return r

def process_frame(item, prompt, character=None, position=0, seed=None):
    model = "seedream-4-5-251128"
    if isinstance(item, str):
        item = Item.objects.get(public_id=item)
    if isinstance(character, Document):
        character = public_document_url(character)
    image = public_frame_url(item, position)
    if character is not None:
        image = [image, character]
    data = {
        "model": model,
        "prompt": prompt,
        "image": image,
        "size": "2560x1440",
    }
    if seed:
        data["seed"] = seed
    url = bytedance_image_generation(data)
    img = add_ai_image(item, position, url)
    img.refresh_from_db()
    img.data["model"] = model
    img.data["prompt"] = prompt
    img.data["source"] = item.public_id
    if character:
        img.data["source"] += " " + character.split("?")[0]
    print(img, img.data)
    img.save()
    img.update_sort()
    img.update_find()
    return img


"""
REPLACE_CHARACTER_PROMPT = "Replace the foreground character in image 1 with the character in image 2, keep the posture, clothing, background, light, atmosphere from image 1, but take the facial features and personality from image 2. Make sure the size of the character is adjusted since the new character is a child and make sure the size of the head matches the body. The quality of the image should be the same between foreground and background, adjust the quality of the character to match the background. Use the style of image 1 for the character: if image 1 is a photo make the character a real person, if image 1 is a drawing make the character a drawn character, if image 1 is a comic use a comic character and so on"
"""

REPLACE_CHARACTER_PROMPT = "Replace the foreground character in image 1 with the character in image 2, keep the posture, clothing, background, light, atmosphere from image 1, but take the facial features and personality from image 2. Make sure the size of the character is adjusted since the new character is a child and make sure the size of the head matches the body. The quality of the image should be the same between foreground and background, adjust the quality of the character to match the background. Use the style of image 1 for the character: if image 1 is a photo make the character a real person, if image 1 is a drawing make the character a drawn character, if image 1 is a comic use a comic character"


def fal_replace_character(item, character, position=0):
    if isinstance(item, str):
        item = Item.objects.get(public_id=item)
    prompt = REPLACE_CHARACTER_PROMPT.replace("image 1", "@image 1").replace(
        "image 2", "@image 2"
    )
    if character == "P5":
        prompt = prompt.replace("child", "teenager")
    if character in ("P1", "P2", "P3", "P4", "P5"):
        character = Document.objects.get(data__title="Character " + character)
    if isinstance(character, Document):
        character = public_document_url(character)
    image = public_frame_url(item, position)
    image = [image, character]
    url = flux_edit_image(image, prompt)
    img = add_ai_image(item, position, url)
    img.refresh_from_db()
    img.data["model"] = "flux-2-pro"
    img.data["prompt"] = prompt
    img.data["source"] = item.public_id
    img.data["source"] += " " + character.split("?")[0]
    print(img, img.data)
    img.save()
    img.update_sort()
    img.update_find()
    return img


def replace_character(item, character, position=0, seed=None):
    prompt = REPLACE_CHARACTER_PROMPT
    if character == "P5":
        prompt = prompt.replace("child", "teenager")
    if character in ("P1", "P2", "P3", "P4", "P5"):
        character = public_document_url(
            Document.objects.get(data__title="Character " + character)
        )
    return process_frame(item, prompt, character, position, seed=seed)


def kling_lipsync(audio_item, video_item):
    video_url = public_video_url(video_item)
    audio_url = public_video_url(audio_item)
    model = "fal-ai/kling-video/lipsync/audio-to-video"
    data = {
        "video_url": video_url,
        "audio_url": audio_url
    }
    print(data)
    handler = fal_client.submit(model, arguments=data)
    request_id = handler.request_id
    print(request_id)
    result = fal_wait_for(model, request_id)
    print(result)
    output_url = result["video"]["url"]
    ox.net.save_url(output_url, output, overwrite=True)
    ai = add_ai_variant(item, output, "ai:lipsync")
    ai.data["model"] = model
    ai.save()
    if not keep:
        shutil.rmtree(os.path.dirname(output))
    img.add(ai)
    return ai

def kling_v2v_reference(item, character, keep=False):
    # https://fal.ai/models/fal-ai/kling-video/o1/video-to-video/reference/api
    if isinstance(item, str):
        item = Item.objects.get(public_id=item)

    if character in ("P1", "P2", "P3", "P4", "P5"):
        character = public_document_url(
            Document.objects.get(data__title="Character " + character)
        )
    video_url = public_video_url(item)
    prompt = "Replace the main character in @Video1 with the character from the reference images, adjust the style of the character to match the style of the video"
    model = "fal-ai/kling-video/o1/video-to-video/reference"
    prompt_hash = hashlib.sha1((prompt + character).encode()).hexdigest()
    output = "/srv/pandora/static/power/cache/%s_%s/ai.mp4" % (
        item.public_id,
        prompt_hash,
    )
    for d in [3, 4, 5, 6, 7, 8, 9, 10]:
        if d > item.sort.duration:
            break
    duration = d

    data = {
        "prompt": prompt,
        "image_url": image_url,
        "video_url": video_url,
        "keep_original_sound": False,
        "character_orientation": "video",
    }
    data = {
      "prompt": prompt,
      "keep_audio": False,
      "aspect_ratio": "16:9",
      "video_url": video_url,
      "image_urls": [image_url],
      "duration": str(duration)
    }
    '''
    data["elements"] = [
        {
        "reference_image_urls": [
            image_url,
            image_url,
        ],
        "frontal_image_url": image_url
        }
    ]
    '''
    print(data)
    handler = fal_client.submit(model, arguments=data)
    request_id = handler.request_id
    print(request_id)
    result = fal_wait_for(model, request_id)
    print(result)
    output_url = result["video"]["url"]
    ox.net.save_url(output_url, output, overwrite=True)
    ai = add_ai_variant(item, output, "ai:v2v-replace")
    ai.data["prompt"] = ox.escape_html(prompt)
    ai.data["firstframe"] = image_url.split("?")[0]
    ai.data["model"] = model
    ai.save()
    if not keep:
        shutil.rmtree(os.path.dirname(output))
    img.add(ai)
    return ai

def replace_character_motion_control(item, character, keep=False):
    if isinstance(item, str):
        item = Item.objects.get(public_id=item)

    # FIXME get character from documents
    if isinstance(character, str):
        img = replace_character(item, character, 0)
    else:
        img = character
    image_url = public_document_url(img)
    video_url = public_video_url(item)
    prompt = ""
    model = "fal-ai/kling-video/v2.6/pro/motion-control"
    prompt_hash = hashlib.sha1((prompt + image_url).encode()).hexdigest()
    output = "/srv/pandora/static/power/cache/%s_%s/ai.mp4" % (
        item.public_id,
        prompt_hash,
    )
    data = {
        "prompt": prompt,
        "image_url": image_url,
        "video_url": video_url,
        "keep_original_sound": False,
        "character_orientation": "video",
    }
    print(data)
    handler = fal_client.submit(model, arguments=data)
    request_id = handler.request_id
    print(request_id)
    result = fal_wait_for(model, request_id)
    print(result)
    output_url = result["video"]["url"]
    ox.net.save_url(output_url, output, overwrite=True)
    ai = add_ai_variant(item, output, "ai:replace:p1:motion-control")
    ai.data["prompt"] = ox.escape_html(prompt)
    ai.data["firstframe"] = image_url.split("?")[0]
    ai.data["model"] = model
    ai.save()
    if not keep:
        shutil.rmtree(os.path.dirname(output))
    img.add(ai)
    return ai


def describe_video(url, neutral=False):
    if neutral:
        prompt = (
            "Detect cuts or scene changes and describe each scene, use as much details as you can. "
            "Describe each person incudling detalied apreance, haircut in a gender neutral way, "
            "describe each objects, animal or plant, describe foreground and backgroud, "
            "describe from what angle the scene is filmed, incude details about camera model, lense, depth of field used to film this scene. "
            "Use the format: <description of scene 1>. CAMERA CUT TO <description of scene 2>. CAMERA CUT TO <description of scene 3>. "
            "Don't mention it if you don't find a cut."
        )
    else:
        prompt = (
            "Detect cuts or scene changes and describe each scene, use as much details as you can. "
            "Describe each person incudling detalied apreance, ethnicity, haircolor, haircut, "
            "describe each objects, animal or plant, describe foreground and backgroud, "
            "describe from what angle the scene is filmed, incude details about camera model, lense, depth of field used to film this scene. "
            "Use the format: <description of scene 1>. CAMERA CUT TO <description of scene 2>. CAMERA CUT TO <description of scene 3>. "
            "Don't mention it if you don't find a cut."
        )
    data = {
        "input": [
            {
                "role": "user",
                "content": [
                    {"type": "input_video", "video_url": url, "fps": 1},
                    {"type": "input_text", "text": prompt},
                ],
            }
        ],
    }
    response = bytedance_response(data)
    return response["output"][1]["content"][0]["text"]


def describe_item(item, neutral=False):
    if isinstance(item, str):
        item = Item.objects.get(public_id=item)
    video_url = public_video_url(item)
    return describe_video(video_url, neutral)


def reshoot_item(item, extra_prompt=None, first_frame=None, keep=False):
    if isinstance(item, str):
        item = Item.objects.get(public_id=item)
    if isinstance(first_frame, Document):
        first_frame = public_document_url(first_frame)
    duration = item.sort.duration
    frames = int(duration * 24)
    prompt = describe_item(item, first_frame is not None)

    if extra_prompt:
        prompt += " " + extra_prompt
    prompt_hash = hashlib.sha1((prompt).encode()).hexdigest()
    output = "/srv/pandora/static/power/cache/%s_%s/ai.mp4" % (
        item.public_id,
        prompt_hash,
    )
    if first_frame:
        status = i2v_bytedance(first_frame, prompt, duration, output)
    else:
        status = t2v_bytedance(prompt, duration, output)

    trimmed = "/srv/pandora/static/power/cache/%s_%s/trimmed.mp4" % (
        item.public_id,
        prompt_hash,
    )
    trim_video(output, trimmed, frames)
    variant = "ai:0:reshoot"
    if first_frame:
        variant = "ai:0:reshoot-firstframe"
    ai = add_ai_variant(item, trimmed, variant)
    ai.data["prompt"] = ox.escape_html(prompt)
    ai.data["model"] = status["model"]
    ai.data["seed"] = status["seed"]
    if first_frame:
        ai.data["firstframe"] = first_frame.split("?")[0]
        if isinstance(first_frame, Document):
            first_frame.add(ai)
    ai.save()
    if not keep:
        shutil.rmtree(os.path.dirname(output))
    return ai


def reshoot_item_segments(item, character, keep=False):
    if isinstance(item, str):
        item = Item.objects.get(public_id=item)
    max_duration = 12
    source = item.files.all()[0].data.path
    info = ox.avinfo(source)
    duration = info["duration"]
    if duration < max_duration:
        segments = [duration]
    else:
        segments = get_item_segments(item, max_duration=max_duration)
    print(segments)
    prompt_hash = hashlib.sha1("reshoot_segment".encode()).hexdigest()
    processed = []
    prefix = "/srv/pandora/static/power/cache/%s_%s" % (item.public_id, prompt_hash)
    video_segments = fragment_video(source, prefix, segments)
    prompts = []
    first_frames = []
    seed = None
    n = 0
    position = 0
    for segment in segments:
        if isinstance(segment, list):
            stype, segment = segment
        else:
            stype = "n"
        output = "%s/%06d.mp4" % (prefix, n)
        output_ai = "%s/%06d_ai.mp4" % (prefix, n)
        segment_duration = segment - position
        if os.path.exists(output):
            segment_video_url = public_url(output)
            prompt = describe_video(segment_video_url, neutral=True)
            prompts.append("Segment %s: " % (n + 1) + prompt)
            segment_character = character
            if position:
                segment_character = segment_first_frame_url
            segment_first_frame = replace_character(
                item, segment_character, position, seed=seed
            )
            segment_first_frame_url = public_document_url(segment_first_frame)
            status = i2v_bytedance(
                segment_first_frame_url, prompt, segment_duration, output_ai, seed=seed
            )
            seeed = status["seed"]
            trimmed = "%s/%06d_ai_trimmed.mp4" % (prefix, n)
            frames = int(segment_duration * 24)
            trim_video(output_ai, trimmed, frames, stype == "c")
            processed.append(trimmed)
            first_frames.append(segment_first_frame)
        n += 1
        position = segment
    joined_output = "%s/joined.mp4" % (prefix)
    join_segments(processed, joined_output)
    ai = add_ai_variant(item, joined_output, "ai:0:reshoot-firstframe")
    prompt = "\n\n".join(prompts)
    ai.data["prompt"] = ox.escape_html(prompt)
    ai.data["firstframe"] = " ".join([ox.toAZ(ff.id) for ff in first_frames])
    ai.data["model"] = status["model"]
    ai.data["seed"] = seed
    ai.save()
    if not keep:
        shutil.rmtree(os.path.dirname(joined_output))
    for first_frame in first_frames:
        first_frame.add(ai)
    if not keep:
        shutil.rmtree(prefix)
    return ai


def describe_image(url):
    system_prompt = ""
    system_prompt = "You are an image analyst describing different aspects of an image. You are focused on the form, composition, and task shown in the image."
    prompt = "Please analyze this image according to the specified structure."
    data = {
        "input": [
            {"role": "system", "content": system_prompt},
            {
                "role": "user",
                "content": [
                    {"type": "input_image", "image_url": url},
                    {"type": "input_text", "text": prompt},
                ],
            },
        ],
    }
    response = bytedance_response(data)
    return response["output"][-1]["content"][0]["text"]


def transform_remake_video(item_id, image_prompt, video_prompt):
    item = Item.objects.get(public_id=item_id)
    segments = get_item_segments(item)
    print(segments)
    prompt_hash = hashlib.sha1((image_prompt + video_prompt).encode()).hexdigest()
    position = n = 0
    processed = []
    prefix = "/srv/pandora/static/power/cache/%s_%s" % (item.public_id, prompt_hash)
    for segment in segments:
        if isinstance(segment, list):
            stype, segment = segment
        else:
            stype = "n"
        duration = segment - position
        if stype == "c":
            first_frame_path = "%s/%06d.mp4.last_frame.png" % (prefix, n - 1)
            first_frame = public_url(first_frame_path)
        else:
            first_frame = "%s%s/source%s.png?token=%s" % (
                settings.PUBLIC_URL,
                item.public_id,
                position,
                settings.PUBLIC_TOKEN,
            )
            first_frame_path = "%s/%06d.first_frame.png" % (prefix, n)
            if not os.path.exists(first_frame_path):
                prepare_image(first_frame, image_prompt, first_frame_path)
            first_frame = public_url(first_frame_path)
        last_frame_position = segment - 2 / 24
        last_frame = "%s%s/source%0.3f.png?token=%s" % (
            settings.PUBLIC_URL,
            item.public_id,
            last_frame_position,
            settings.PUBLIC_TOKEN,
        )
        last_frame_path = "%s/%06d.last_frame.png" % (prefix, n)
        if not os.path.exists(last_frame_path):
            prepare_image(last_frame, image_prompt, last_frame_path)
        last_frame = public_url(last_frame_path)

        output = "%s/%06d.mp4" % (prefix, n)
        if not os.path.exists(output):
            first_last(first_frame, last_frame, video_prompt, duration, output)
        trimmed = "%s/%06d_trimmed.mp4" % (prefix, n)
        frames = int(duration * 24)
        if not os.path.exists(trimmed):
            trim_video(output, trimmed, frames, stype == "c")
        processed.append(trimmed)
        position = segment
        n += 1

    joined_output = "%s/joined.mp4" % (prefix,)
    join_segments(processed, joined_output)
    return joined_output


def restyle_video(item_id, prompt):
    item = Item.objects.get(public_id=item_id)
    video_url = public_video_url(item)
    model = "decart/lucy-restyle"
    handler = fal_client.submit(
        model,
        arguments={
            "prompt": prompt,
            "video_url": video_url,
            "resolution": "720p",
            "enhance_prompt": True,
        },
    )
    request_id = handler.request_id
    print(request_id)
    status = fal_client.status(model, request_id, with_logs=True)
    while isinstance(status, fal_client.InProgress):
        time.sleep(10)
        status = fal_client.status(model, request_id, with_logs=True)
    result = fal_client.result(model, request_id)
    print(result)
    output_url = result["video"]["url"]
    prompt_hash = hashlib.sha1((prompt).encode()).hexdigest()
    output_path = "/srv/pandora/static/power/cache/%s_%s.mp4" % (
        item.public_id,
        prompt_hash,
    )
    ox.net.save_url(output_url, output_path, overwrite=True)
    return output_path


def fal_wait_for(model, request_id):
    status = fal_client.status(model, request_id, with_logs=True)
    while isinstance(status, fal_client.InProgress):
        time.sleep(10)
        status = fal_client.status(model, request_id, with_logs=True)
    result = fal_client.result(model, request_id)
    return result


def motion_control_preprocess_image(item_id, image_prompt, video_prompt):
    item = Item.objects.get(public_id=item_id)
    video_url = public_video_url(item)
    model = "fal-ai/kling-video/v2.6/pro/motion-control"
    prompt_hash = hashlib.sha1((image_prompt + video_prompt).encode()).hexdigest()
    output = "/srv/pandora/static/power/cache/%s_%s.mp4" % (item.public_id, prompt_hash)
    first_frame = "%s%s/source%s.png?token=%s" % (
        settings.PUBLIC_URL,
        item.public_id,
        0,
        settings.PUBLIC_TOKEN,
    )
    first_frame_path = "/srv/pandora/static/power/cache/%s_%s/%06d.first_frame.png" % (
        item.public_id,
        prompt_hash,
        0,
    )
    if not os.path.exists(first_frame_path):
        os.makedirs(os.path.dirname(first_frame_path), exist_ok=True)
        prepare_image(first_frame, image_prompt, first_frame_path)
    image_url = public_url(first_frame_path)
    data = {
        "prompt": video_prompt,
        "image_url": image_url,
        "video_url": video_url,
        "keep_original_sound": False,
        "character_orientation": "video",
    }
    print(data)
    handler = fal_client.submit(model, arguments=data)
    request_id = handler.request_id
    print(request_id)
    result = fal_wait_for(model, request_id)
    print(result)
    output_url = result["video"]["url"]
    ox.net.save_url(output_url, output, overwrite=True)
    return output


def luma_wait_for(id):
    url = "https://api.lumalabs.ai/dream-machine/v1/generations/%s" % id
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
        "authorization": "Bearer " + settings.LUMA_TOKEN,
    }
    status = requests.get(url, headers=headers).json()
    while status["state"] in ("queued", "dreaming"):
        time.sleep(10)
        status = requests.get(url, headers=headers).json()
    return status


def luma_modify_segment(video_url, prompt, first_frame=None, mode="flex_2"):
    # also got that at fal-ai/luma-dream-machine/ray-2/modify
    url = "https://api.lumalabs.ai/dream-machine/v1/generations/video/modify"
    payload = {
        "generation_type": "modify_video",
        "model": "ray-2",
        "mode": mode,
        "prompt": prompt,
        "media": {"url": video_url},
    }
    if first_frame:
        payload["first_frame"] = {"url": first_frame}
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
        "authorization": "Bearer " + settings.LUMA_TOKEN,
    }
    response = requests.post(url, json=payload, headers=headers).json()
    print(response)
    status = luma_wait_for(response["id"])
    return status["assets"]["video"]


def fragment_video(filename, segmentdir, segments):
    filename = str(filename)
    input_info = ox.avinfo(filename)

    segments_ = []
    for segment in segments:
        if isinstance(segment, list):
            stype, segment = segment
        else:
            stype = "n"
        segments_.append(segment)
    segments = segments_

    position = 0
    segment = 0

    cap = cv2.VideoCapture(filename)
    fps = cap.get(cv2.CAP_PROP_FPS)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    fourcc = cv2.VideoWriter_fourcc(*"avc1")
    frame_count = 0
    next_cut = int(segments.pop(0) * fps)

    last = None
    os.makedirs(segmentdir, exist_ok=True)

    while cap.isOpened():
        if frame_count == 0:
            output_path = segmentdir + "/%06d.mp4" % segment
            out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        elif next_cut and frame_count >= next_cut and segments:
            print(frame_count, output_path)
            cv2.imwrite(output_path.replace(".mp4", "_last.jpg"), frame)
            out.release()
            segment += 1
            output_path = segmentdir + "/%06d.mp4" % segment
            out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
            if segments:
                next_cut = int(segments.pop(0) * fps)
            else:
                next_cut = None
            last = None
        ret, frame = cap.read()
        if not ret:
            break
        out.write(frame)
        if last is None:
            cv2.imwrite(output_path.replace(".mp4", "_first.jpg"), frame)
        last = frame
        frame_count += 1

    out.release()
    cap.release()

    if last is None:
        os.unlink(output_path)
    else:
        cv2.imwrite(output_path.replace(".mp4", "_last.jpg"), last)


def flux_edit_image(image, prompt):
    if isinstance(image, str):
        image = [image]
    data = {
        "prompt": prompt,
        "safety_tolerance": "5",
        "enable_safety_checker": False,
        "output_format": "jpeg",
        "image_urls": image,
    }
    print(data)
    result = fal_client.subscribe("fal-ai/flux-2-pro/edit", arguments=data)
    print(result)
    return result["images"][0]["url"]


def in_the_style_of_fal(image, style):
    prompt = "apply style from @image 2 to @image 1 keep the position of the person in @image 1 but take light, colors, clothing from @image 2"
    return flux_edit_image([image, style], prompt)


def in_the_style_of_byte(image, style):
    prompt = "apply style from image 2 to image 1 keep the position of the person in image 1 but take light, colors, clothing from image 2"
    image_model_name = "seedream-4-5-251128"
    ark_client = Ark(
        base_url="https://ark.ap-southeast.bytepluses.com/api/v3",
        api_key=settings.BYTEPLUSE_TOKEN,
    )
    create_result = ark_client.images.generate(
        model=image_model_name,
        image=[image, style],
        prompt=prompt,
        sequential_image_generation="disabled",
        response_format="url",
        size="2560x1440",
        stream=False,
        watermark=False,
    )
    print(create_result)
    return create_result.data[0].url


def luma_modify_item(item, prompt="", image_prompt=None, first_frame=None, keep=False):
    mode = "flex_2"
    if isinstance(item, str):
        item = Item.objects.get(public_id=item)
    source = item.files.all()[0].data.path
    info = ox.avinfo(source)
    duration = info["duration"]
    max_duration = 10
    if duration < max_duration:
        segments = [duration]
    else:
        segments = get_item_segments(item, max_duration=max_duration)
    print(segments)
    prompt_hash = hashlib.sha1((prompt + (image_prompt or "")).encode()).hexdigest()
    processed = []
    prefix = "/srv/pandora/static/power/cache/%s_%s" % (item.public_id, prompt_hash)
    video_segments = fragment_video(source, prefix, segments)
    n = 0
    if isinstance(first_frame, Document):
        first_frame_url = public_document_url(first_frame)
    else:
        first_frame_url = first_frame
    for segment in segments:
        if isinstance(segment, list):
            stype, segment = segment
        else:
            stype = "n"
        output = "%s/%06d.mp4" % (prefix, n)
        output_ai = "%s/%06d_ai.mp4" % (prefix, n)
        if os.path.exists(output):
            video_url = luma_modify_segment(
                public_url(output), prompt, first_frame=first_frame_url, mode=mode
            )
            ox.net.save_url(video_url, output_ai, overwrite=True)
            processed.append(output_ai)
        n += 1
    joined_output = "%s/joined.mp4" % (prefix,)
    join_segments(processed, joined_output)
    ai = add_ai_variant(item, joined_output, "ai:replace:p1:luma")
    ai.data["prompt"] = ox.escape_html(prompt)
    if first_frame:
        ai.data["firstframe"] = first_frame_url.split("?")[0]
    ai.data["model"] = "ray-2:%s" % mode
    ai.save()
    if not keep:
        shutil.rmtree(os.path.dirname(joined_output))
    if isinstance(first_frame, Document):
        first_frame.add(ai)
    return ai


def add_ai_variant(item, video_path, type):
    if isinstance(item, str):
        item = Item.objects.get(public_id=item)

    ai = Item()
    ai.user = item.user
    ai.data["type"] = [type]
    ai.data["title"] = item.data["title"]
    ai.data["chapter"] = item.data.get("chapter", "")
    ai.save()
    file = File()
    file.oshash = ox.oshash(video_path)
    file.item = ai
    file.path = "%s.mp4" % type
    file.info = ox.avinfo(video_path)
    del file.info["path"]
    file.parse_info()
    file.data.name = file.get_path("data." + video_path.split(".")[-1])
    os.makedirs(os.path.dirname(file.data.path), exist_ok=True)
    shutil.copy(video_path, file.data.path)
    file.available = True
    file.selected = True
    file.queued = True
    file.wanted = False
    file.save()
    file.extract_stream()
    return ai


def add_ai_image(item, position, url, extension=None):
    if extension is None:
        extension = url.split(".")[-1].split("?")[0]
        if extension == "jpeg":
            extension = "jpg"
    file = Document(user=item.user)
    file.data["title"] = "%s at %s" % (item.get("title"), position)
    file.data["position"] = position
    file.extension = extension
    file.width = -1
    file.pages = -1
    file.uploading = True
    file.save()
    file.uploading = True
    name = "data.%s" % file.extension
    file.file.name = file.path(name)
    ox.net.save_url(url, file.file.path, overwrite=True)
    file.get_info()
    file.get_ratio()
    file.oshash = ox.oshash(file.file.path)
    file.save()
    file.update_sort()
    file.add(item)
    return file

def add_tag(item, tag):
    if 'tags' not in item.data:
        item.data['tags'] = []
    item.data['tags'].append(tag)
    item.save()

def process_motion_firstframe(character="P1", keep=False):
    l = itemlist.models.List.objects.get(name='Motion-Firstframe')
    for i in l.items.all():
            ai = Item.objects.filter(data__type__icontains='ai').filter(data__title=i.data['title'])
            if ai.exists() or 'ai-failed' in i.data.get('tags', []):
                print('>> skip', i)
                continue
            print(i)
            try:
                replace_character_motion_control(i, character, keep=keep)
            except:
                add_tag(i, 'ai-failed')
                print('>> failed', i)


def extract_firstframe(character='P1'):
    for item in Item.objects.filter(data__type__icontains="source"):
        if 'ai-failed' in item.data.get('tags', []):
            continue
        if not item.documents.all().exists():
            print(item)
            try:
                first_frame = replace_character(item, character, 0)
            except:
                add_tag(item, 'ai-failed')

def process_reshoot_firstframe():
    l = itemlist.models.List.objects.get(name='Reshoot-Firstframe')
    for i in l.items.all():
        if i.sort.duration > 30: continue
        if i.public_id == 'HZ': continue
        if i.documents.all().count():
            ai = Item.objects.filter(data__type__icontains='ai').filter(data__title=i.data['title'])
            if ai.exists() or 'ai-failed' in i.data.get('tags', []):
                print('>> skip', i)
                continue
            first_frame = i.documents.all().order_by('-created').first()
            if not first_frame:
                first_frame = replace_character(i, 'P1', 0)
            print(i, first_frame, i.documents.all().count())
            try:
                replace_character_motion_control(i, first_frame)
            except:
                add_tag(i, 'ai-failed')
                print('>> failed', i)