from runwayml import RunwayML

client = RunwayML()

# Image-to-video with latest Gen-4.5 model
task = client.image_to_video.create(
    model="gen4.5",
    prompt_image="https://example.com/image.jpg",
    prompt_text="A timelapse on a sunny day with clouds flying by",
    ratio="1280:720",
    duration=10
).wait_for_task_output()

print(f"Video URL: {task.output[0]}")

# Text-to-video (no image required)
task = client.image_to_video.create(
    model="gen4.5",
    prompt_text="A serene mountain landscape at sunset with clouds drifting",
    ratio="1280:720",
    duration=10
).wait_for_task_output()

import RunwayML from "@runwayml/sdk";

const client = new RunwayML();

// Image-to-video with latest Gen-4.5 model
const task = await client.imageToVideo
  .create({
    model: "gen4.5",
    promptImage: "https://example.com/image.jpg",
    promptText: "A timelapse on a sunny day with clouds flying by",
    ratio: "1280:720",
    duration: 10,
  })
  .waitForTaskOutput();

console.log(`Video URL: ${task.output[0]}`);

// Text-to-video (no image required)
const textTask = await client.imageToVideo
  .create({
    model: "gen4.5",
    promptText: "A serene mountain landscape at sunset",
    ratio: "1280:720",
    duration: 10,
  })
  .waitForTaskOutput();

# Image-to-video
curl -X POST "https://api.dev.runwayml.com/v1/image_to_video" \
  -H "Authorization: Bearer $RUNWAYML_API_SECRET" \
  -H "X-Runway-Version: 2024-11-06" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gen4.5",
    "promptImage": "https://example.com/image.jpg",
    "promptText": "A timelapse on a sunny day",
    "ratio": "1280:720",
    "duration": 10
  }'

# Text-to-video (no image required)
curl -X POST "https://api.dev.runwayml.com/v1/image_to_video" \
  -H "Authorization: Bearer $RUNWAYML_API_SECRET" \
  -H "X-Runway-Version: 2024-11-06" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gen4.5",
    "promptText": "A serene mountain landscape at sunset",
    "ratio": "1280:720",
    "duration": 10
  }'

# Poll for result (use returned task id)
curl "https://api.dev.runwayml.com/v1/tasks/{task_id}" \
  -H "Authorization: Bearer $RUNWAYML_API_SECRET" \
  -H "X-Runway-Version: 2024-11-06"

task = client.image_to_video.create(
    model="gen4.5",
    prompt_text="A serene mountain lake at sunrise with mist rising from the water",
    ratio="1280:720",
    duration=10,
    seed=12345  # Optional: reproducibility
).wait_for_task_output()

task = client.image_to_video.create(
    model="gen4.5",
    prompt_image="https://example.com/image.jpg",
    prompt_text="Camera slowly pushes in, leaves rustling in the breeze",
    ratio="1584:672",
    duration=10,
    seed=12345
).wait_for_task_output()

task = client.image_to_video.create(
    model="gen4_turbo",
    prompt_image="https://example.com/image.jpg",
    prompt_text="Camera slowly pushes in, leaves rustling",
    ratio="1280:720",
    duration=5,
    seed=12345           # Optional: reproducibility
).wait_for_task_output()

task = client.video_to_video.create(
    model="gen4_aleph",
    video_uri="https://example.com/source.mp4",
    prompt_text="Transform to anime style",
    references=[{"uri": "https://example.com/style_ref.jpg"}]  # Optional style reference
).wait_for_task_output()

task = client.character_performance.create(
    model="act_two",
    character={"type": "image", "uri": "https://example.com/character.jpg"},  # or type: "video"
    reference={"type": "video", "uri": "https://example.com/performance.mp4"}
).wait_for_task_output()

# Text-to-video with audio (no image required)
task = client.image_to_video.create(
    model="veo3.1",  # or "veo3", "veo3.1_fast"
    prompt_text="A cinematic shot of a rocket launching at sunset with roaring engines"
).wait_for_task_output()

# Image-to-video with audio
task = client.image_to_video.create(
    model="veo3.1",
    prompt_image="https://example.com/starting_frame.jpg",
    prompt_text="Smooth camera movement through the scene with ambient nature sounds"
).wait_for_task_output()

# gen4_image - reference_images is optional
task = client.text_to_image.create(
    model="gen4_image",
    ratio="1920:1080",
    prompt_text="A beautiful mountain landscape at sunset"
).wait_for_task_output()

# With references
task = client.text_to_image.create(
    model="gen4_image",
    ratio="1920:1080",
    prompt_text="@EiffelTower painted in the style of @StarryNight",
    reference_images=[
        {"uri": "https://example.com/eiffel.jpg", "tag": "EiffelTower"},
        {"uri": "https://example.com/starry.jpg", "tag": "StarryNight"}
    ]
).wait_for_task_output()

reference_images=[
    {"uri": "https://example.com/subject.jpg", "tag": "subject"},
    {"uri": "https://example.com/style.jpg"}  # No tag = style reference
]

task = client.text_to_speech.create(
    model="eleven_multilingual_v2",
    prompt_text="Hello, welcome to RunwayML!",
    voice={"type": "runway-preset", "preset_id": "Maya"}
).wait_for_task_output()

task = client.sound_effect.create(
    model="eleven_text_to_sound_v2",
    prompt_text="Thunder rumbling in the distance, rain on a window"
).wait_for_task_output()

task = client.voice_isolation.create(
    model="eleven_voice_isolation",
    audio_uri="https://example.com/noisy_audio.mp3"
).wait_for_task_output()

task = client.voice_dubbing.create(
    model="eleven_voice_dubbing",
    audio_uri="https://example.com/speech.mp3",
    target_lang="es"  # Spanish
).wait_for_task_output()

task = client.speech_to_speech.create(
    model="eleven_multilingual_sts_v2",
    media={"type": "audio", "uri": "https://example.com/original.mp3"},  # or type: "video"
    voice={"type": "runway-preset", "preset_id": "Maya"}
).wait_for_task_output()

import base64

with open("image.jpg", "rb") as f:
    data_uri = f"data:image/jpeg;base64,{base64.b64encode(f.read()).decode()}"

task = client.image_to_video.create(
    model="gen4_turbo",
    prompt_image=data_uri,
    prompt_text="Gentle movement"
).wait_for_task_output()

# Cancel running task or delete completed task
client.tasks.delete(task.id)

from runwayml import RunwayML, APIError, RateLimitError

client = RunwayML()

try:
    task = client.image_to_video.create(...).wait_for_task_output()
    if task.status == "FAILED":
        print(f"Generation failed: {task.failure}")
except RateLimitError:
    print("Rate limited - SDK retries automatically")
except APIError as e:
    print(f"API error {e.status_code}: {e.message}")