Generate Video from Text Prompt

npm install axios

pip install requests

import axios from "axios";

const API_BASE_URL = "https://api.pictory.ai/pictoryapis";
const API_KEY = "YOUR_API_KEY"; // Replace with your actual API key

// Video generation configuration
const videoRequest = {
  prompt: "A young woman sitting on a couch in a modern living room, watching television with soft ambient lighting in a wide camera shot",
  model: "pixverse5.5",
  aspectRatio: "9:16",
  duration: "8s"
};

import requests
import time

API_BASE_URL = "https://api.pictory.ai/pictoryapis"
API_KEY = "YOUR_API_KEY"  # Replace with your actual API key

# Video generation configuration
video_request = {
    "prompt": "A young woman sitting on a couch in a modern living room, watching television with soft ambient lighting in a wide camera shot",
    "model": "pixverse5.5",
    "aspectRatio": "9:16",
    "duration": "8s"
}

async function generateVideo() {
  try {
    console.log("Submitting video generation request...");

    const response = await axios.post(
      `${API_BASE_URL}/v1/aistudio/videos`,
      videoRequest,
      {
        headers: {
          "Content-Type": "application/json",
          Authorization: API_KEY,
        },
      }
    );

    const jobId = response.data.data.jobId;
    console.log("Video generation started.");
    console.log("Job ID:", jobId);

    return jobId;
  } catch (error) {
    console.error("Error submitting request:", error.response?.data || error.message);
    throw error;
  }
}

def generate_video():
    try:
        print("Submitting video generation request...")

        response = requests.post(
            f"{API_BASE_URL}/v1/aistudio/videos",
            json=video_request,
            headers={
                "Content-Type": "application/json",
                "Authorization": API_KEY
            }
        )
        response.raise_for_status()

        job_id = response.json()["data"]["jobId"]
        print("Video generation started.")
        print(f"Job ID: {job_id}")

        return job_id

    except requests.exceptions.RequestException as error:
        print(f"Error submitting request: {error}")
        raise

async function waitForVideo(jobId) {
  console.log("\nPolling for video generation result...");

  while (true) {
    const response = await axios.get(
      `${API_BASE_URL}/v1/jobs/${jobId}`,
      {
        headers: { Authorization: API_KEY },
      }
    );

    const data = response.data;
    const status = data.data.status;
    console.log("Status:", status);

    if (status === "completed") {
      console.log("\nVideo generated successfully!");
      console.log("Video URL:", data.data.url);
      console.log("Duration:", data.data.duration);
      console.log("Dimensions:", data.data.width, "x", data.data.height);
      console.log("Thumbnail:", data.data.thumbnailImageUrl);
      console.log("AI Credits Used:", data.data.aiCreditsUsed);
      return data;
    }

    if (status === "failed") {
      throw new Error("Video generation failed: " + JSON.stringify(data));
    }

    // Wait 15 seconds before polling again
    await new Promise(resolve => setTimeout(resolve, 15000));
  }
}

// Run the complete workflow
generateVideo()
  .then(jobId => waitForVideo(jobId))
  .then(result => console.log("\nDone!"))
  .catch(error => console.error("Error:", error));

def wait_for_video(job_id):
    print("\nPolling for video generation result...")

    while True:
        response = requests.get(
            f"{API_BASE_URL}/v1/jobs/{job_id}",
            headers={"Authorization": API_KEY}
        )
        response.raise_for_status()

        data = response.json()
        status = data["data"]["status"]
        print(f"Status: {status}")

        if status == "completed":
            print("\nVideo generated successfully!")
            print(f"Video URL: {data['data']['url']}")
            print(f"Duration: {data['data']['duration']}")
            print(f"Dimensions: {data['data']['width']} x {data['data']['height']}")
            print(f"Thumbnail: {data['data']['thumbnailImageUrl']}")
            print(f"AI Credits Used: {data['data']['aiCreditsUsed']}")
            return data

        if status == "failed":
            raise Exception(f"Video generation failed: {data}")

        # Wait 15 seconds before polling again
        time.sleep(15)

# Run the complete workflow
if __name__ == "__main__":
    job_id = generate_video()
    result = wait_for_video(job_id)
    print("\nDone!")