Sora Official Forward Plan

curl -X POST "https://api.yelinai.com/v1/videos" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F model="sora-2" \
  -F prompt="A golden retriever playing fetch on a sunny beach, waves gently rolling in the background, cinematic lighting" \
  -F size="1280x720" \
  -F seconds="8"

{
  "id": "video_69788dc4a1c8819887468fbeffdda023",
  "object": "video",
  "model": "sora-2",
  "status": "queued",
  "progress": 0,
  "created_at": 1769508292,
  "size": "1280x720",
  "seconds": "8"
}

curl -X POST "https://api.yelinai.com/v1/videos" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F model="sora-2" \
  -F prompt="The camera slowly zooms in, the scene comes alive with gentle movement, cinematic atmosphere" \
  -F size="1280x720" \
  -F seconds="4" \
  -F input_reference="@your_image.jpeg;type=image/jpeg"

curl "https://api.yelinai.com/v1/videos/video_69788dc4a1c8819887468fbeffdda023" \
  -H "Authorization: Bearer YOUR_API_KEY"

{
  "id": "video_69788dc4a1c8819887468fbeffdda023",
  "object": "video",
  "status": "in_progress",
  "progress": 45,
  "created_at": 1769508292
}

{
  "id": "video_69788dc4a1c8819887468fbeffdda023",
  "object": "video",
  "status": "completed",
  "progress": 100,
  "created_at": 1769508292,
  "completed_at": 1769508592
}

curl "https://api.yelinai.com/v1/videos/video_69788dc4a1c8819887468fbeffdda023/content" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -o output.mp4

import requests
import time

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.yelinai.com"

headers = {
    "Authorization": f"Bearer {API_KEY}"
}

# Text-to-Video
def create_video(prompt, model="sora-2", seconds="8", size="1280x720"):
    response = requests.post(
        f"{BASE_URL}/v1/videos",
        headers=headers,
        data={
            "model": model,
            "prompt": prompt,
            "seconds": seconds,
            "size": size
        }
    )
    response.raise_for_status()
    return response.json()

# Image-to-Video
def create_video_from_image(prompt, image_path, model="sora-2", seconds="4", size="1280x720"):
    with open(image_path, "rb") as f:
        response = requests.post(
            f"{BASE_URL}/v1/videos",
            headers=headers,
            data={
                "model": model,
                "prompt": prompt,
                "seconds": seconds,
                "size": size
            },
            files={
                "input_reference": (image_path.split("/")[-1], f, "image/jpeg")
            }
        )
    response.raise_for_status()
    return response.json()

# Poll task status
def poll_status(video_id, timeout=600, interval=15):
    start_time = time.time()
    while time.time() - start_time < timeout:
        response = requests.get(
            f"{BASE_URL}/v1/videos/{video_id}",
            headers=headers
        )
        response.raise_for_status()
        data = response.json()

        status = data.get("status")
        progress = data.get("progress", 0)
        print(f"Status: {status}, Progress: {progress}%")

        if status == "completed":
            return data
        elif status == "failed":
            raise Exception(f"Video generation failed: {data.get('error')}")

        time.sleep(interval)

    raise TimeoutError("Video generation timeout")

# Download video
def download_video(video_id, output_path="output.mp4"):
    response = requests.get(
        f"{BASE_URL}/v1/videos/{video_id}/content",
        headers=headers
    )
    response.raise_for_status()

    with open(output_path, "wb") as f:
        f.write(response.content)
    print(f"Video saved to: {output_path}")

# Complete workflow
if __name__ == "__main__":
    # Text-to-Video
    job = create_video(
        prompt="A golden retriever playing fetch on a sunny beach",
        model="sora-2",
        seconds="8",
        size="1280x720"
    )
    print(f"Task created: {job['id']}")

    # Wait for completion
    result = poll_status(job["id"])
    print("Video generation completed!")

    # Download video
    download_video(job["id"], "my_video.mp4")

    # Image-to-Video example
    # job2 = create_video_from_image(
    #     prompt="The scene comes alive with gentle movement",
    #     image_path="reference.jpg",
    #     model="sora-2",
    #     seconds="4",
    #     size="1280x720"
    # )