# Search all video models
modelslab models search --feature video_fusion

# Search by name
modelslab models search --search "seedance"
modelslab models search --search "wan"
modelslab models search --search "veo"

# Get model details
modelslab models detail --id seedance-t2v

import requests
import time

def generate_video(prompt, api_key, model_id="seedance-t2v"):
    """Generate a video from a text prompt.

    Args:
        prompt: Text description of the video
        api_key: Your ModelsLab API key
        model_id: Video model to use
    """
    response = requests.post(
        "https://modelslab.com/api/v7/video-fusion/text-to-video",
        json={
            "key": api_key,
            "model_id": model_id,
            "prompt": prompt,
            "negative_prompt": "low quality, blurry, static, distorted"
        }
    )

    data = response.json()

    if data["status"] == "error":
        raise Exception(f"Error: {data['message']}")

    if data["status"] == "success":
        return data["output"][0]

    # Video generation is async - poll for results
    request_id = data["id"]
    print(f"Video processing... Request ID: {request_id}")
    print(f"Estimated time: {data.get('eta', 'unknown')} seconds")

    return poll_video_result(request_id, api_key)

def poll_video_result(request_id, api_key, timeout=600):
    """Poll for video generation results."""
    start_time = time.time()

    while time.time() - start_time < timeout:
        fetch = requests.post(
            f"https://modelslab.com/api/v7/video-fusion/fetch/{request_id}",
            json={"key": api_key}
        )
        result = fetch.json()

        if result["status"] == "success":
            return result["output"][0]
        elif result["status"] == "failed":
            raise Exception(result.get("message", "Generation failed"))

        print(f"Status: processing... ({int(time.time() - start_time)}s elapsed)")
        time.sleep(10)

    raise Exception("Timeout waiting for video generation")

# Usage
video_url = generate_video(
    "A spaceship flying through an asteroid field, cinematic, 4K",
    "your_api_key",
    model_id="seedance-t2v"
)
print(f"Video ready: {video_url}")

def animate_image(image_url, prompt, api_key, model_id="seedance-i2v"):
    """Animate a static image based on a motion prompt.

    Args:
        image_url: URL of the image to animate
        prompt: Description of desired motion/animation
        model_id: Video model for image-to-video
    """
    response = requests.post(
        "https://modelslab.com/api/v7/video-fusion/image-to-video",
        json={
            "key": api_key,
            "model_id": model_id,
            "init_image": [image_url],  # v7 expects array
            "prompt": prompt,
            "negative_prompt": "static, still, low quality, blurry"
        }
    )

    data = response.json()

    if data["status"] == "success":
        return data["output"][0]
    elif data["status"] == "processing":
        return poll_video_result(data["id"], api_key)
    else:
        raise Exception(data.get("message", "Unknown error"))

# Animate a landscape
video = animate_image(
    "https://example.com/landscape.jpg",
    "The clouds moving slowly across the sky, birds flying in the distance",
    "your_api_key",
    model_id="seedance-i2v"
)
print(f"Animated video: {video}")

def transform_video(video_url, prompt, api_key, model_id="wan2.1"):
    """Transform an existing video with a new style or content.

    Args:
        video_url: URL of the source video
        prompt: Description of desired transformation
    """
    response = requests.post(
        "https://modelslab.com/api/v7/video-fusion/video-to-video",
        json={
            "key": api_key,
            "model_id": model_id,
            "init_video": [video_url],  # v7 expects array
            "prompt": prompt
        }
    )

    data = response.json()
    if data["status"] == "processing":
        return poll_video_result(data["id"], api_key)
    elif data["status"] == "success":
        return data["output"][0]

def lip_sync(video_url, audio_url, api_key, model_id="lipsync-2"):
    """Sync lip movements to audio.

    Args:
        video_url: URL of the video with a face
        audio_url: URL of the audio to sync to
    """
    response = requests.post(
        "https://modelslab.com/api/v7/video-fusion/lip-sync",
        json={
            "key": api_key,
            "model_id": model_id,
            "init_video": video_url,
            "init_audio": audio_url
        }
    )

    data = response.json()
    if data["status"] == "processing":
        return poll_video_result(data["id"], api_key)
    elif data["status"] == "success":
        return data["output"][0]

Bad: "A cat"
Good: "A cat walking through a garden, looking around curiously, sunlight filtering through trees"

Include: Action, movement, camera motion, atmosphere

# Video generation is ALWAYS async
# Always implement polling or use webhooks
if data["status"] == "processing":
    video = poll_video_result(data["id"], api_key)

payload = {
    "key": api_key,
    "model_id": "seedance-t2v",
    "prompt": "...",
    "webhook": "https://yourserver.com/webhook/video",
    "track_id": "video_001"
}

try:
    video = generate_video(prompt, api_key, model_id="seedance-t2v")
    print(f"Video generated: {video}")
except Exception as e:
    print(f"Video generation failed: {e}")