Kling AI Storage Integration

import requests
import os

def download_video(video_url: str, output_dir: str = "output") -> str:
    """Download generated video from Kling CDN."""
    os.makedirs(output_dir, exist_ok=True)

    # Extract filename or generate one
    filename = video_url.split("/")[-1].split("?")[0]
    if not filename.endswith(".mp4"):
        filename = f"kling_{int(time.time())}.mp4"

    filepath = os.path.join(output_dir, filename)
    response = requests.get(video_url, stream=True, timeout=120)
    response.raise_for_status()

    with open(filepath, "wb") as f:
        for chunk in response.iter_content(chunk_size=8192):
            f.write(chunk)

    size_mb = os.path.getsize(filepath) / (1024 * 1024)
    print(f"Downloaded: {filepath} ({size_mb:.1f} MB)")
    return filepath

import boto3

def upload_to_s3(filepath: str, bucket: str, key_prefix: str = "kling-videos/") -> str:
    """Upload video to S3 and return public URL."""
    s3 = boto3.client("s3")
    filename = os.path.basename(filepath)
    s3_key = f"{key_prefix}{filename}"

    s3.upload_file(
        filepath, bucket, s3_key,
        ExtraArgs={"ContentType": "video/mp4", "CacheControl": "max-age=86400"}
    )

    url = f"https://{bucket}.s3.amazonaws.com/{s3_key}"
    print(f"Uploaded to S3: {url}")
    return url

# Generate signed URL for private buckets
def get_signed_url(bucket: str, key: str, expiry: int = 3600) -> str:
    s3 = boto3.client("s3")
    return s3.generate_presigned_url(
        "get_object",
        Params={"Bucket": bucket, "Key": key},
        ExpiresIn=expiry,
    )

from google.cloud import storage

def upload_to_gcs(filepath: str, bucket_name: str, prefix: str = "kling-videos/") -> str:
    """Upload video to GCS and return public URL."""
    client = storage.Client()
    bucket = client.bucket(bucket_name)
    filename = os.path.basename(filepath)
    blob = bucket.blob(f"{prefix}{filename}")

    blob.upload_from_filename(filepath, content_type="video/mp4")
    blob.make_public()  # or use signed URLs for private access

    print(f"Uploaded to GCS: {blob.public_url}")
    return blob.public_url

# Signed URL for private access
def get_gcs_signed_url(bucket_name: str, blob_name: str, expiry_min: int = 60) -> str:
    from datetime import timedelta
    client = storage.Client()
    bucket = client.bucket(bucket_name)
    blob = bucket.blob(blob_name)
    return blob.generate_signed_url(expiration=timedelta(minutes=expiry_min))

from azure.storage.blob import BlobServiceClient

def upload_to_azure(filepath: str, container: str,
                    connection_string: str = None) -> str:
    """Upload video to Azure Blob Storage."""
    conn_str = connection_string or os.environ["AZURE_STORAGE_CONNECTION_STRING"]
    client = BlobServiceClient.from_connection_string(conn_str)
    filename = os.path.basename(filepath)
    blob_client = client.get_blob_client(container=container, blob=f"kling-videos/{filename}")

    with open(filepath, "rb") as f:
        blob_client.upload_blob(f, content_type="video/mp4", overwrite=True)

    url = blob_client.url
    print(f"Uploaded to Azure: {url}")
    return url

def generate_and_store(prompt: str, bucket: str, provider: str = "s3"):
    """Generate video with Kling AI and store in cloud."""
    # 1. Generate
    r = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
        "model_name": "kling-v2-master",
        "prompt": prompt,
        "duration": "5",
        "mode": "standard",
    }).json()
    task_id = r["data"]["task_id"]

    # 2. Poll
    result = poll_task("/videos/text2video", task_id)
    video_url = result["videos"][0]["url"]

    # 3. Download
    filepath = download_video(video_url)

    # 4. Upload
    if provider == "s3":
        return upload_to_s3(filepath, bucket)
    elif provider == "gcs":
        return upload_to_gcs(filepath, bucket)
    elif provider == "azure":
        return upload_to_azure(filepath, bucket)

    # 5. Cleanup temp file
    os.remove(filepath)

import json

def save_with_metadata(filepath: str, task_id: str, prompt: str, model: str):
    """Save video metadata alongside the file."""
    meta = {
        "task_id": task_id,
        "prompt": prompt,
        "model": model,
        "generated_at": time.strftime("%Y-%m-%dT%H:%M:%SZ"),
        "filename": os.path.basename(filepath),
    }
    meta_path = filepath.replace(".mp4", ".meta.json")
    with open(meta_path, "w") as f:
        json.dump(meta, f, indent=2)
    return meta_path