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Fetch data from a random user API to represent a hypothetical viewer.Integrate this data into a custom VideoDB timeline.Create a personalized stream that dynamically displays relevant information alongside the keynote video.
First we download the image to local storageThen we use the local path to upload it to VideoDB
VideoDB Documentation
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Building Dynamic Video Streams with VideoDB: Integrating Custom Data and APIs
Introduction
Imagine you're watching a captivating keynote session from your favorite conference, and you’re welcomed with a personalized stream just for you.
This tutorial demonstrates how to create dynamic video streams by integrating data from custom databases and external APIs. We'll use a practical example: a recording of a keynote session. By using VideoDB, we'll show how companies like can personalize the viewing experience for their audience, delivering a richer and more engaging experience.
We'll showcase how to:
This tutorial is your guide to unlocking the potential of dynamic video streams and transforming your video content with personalized experiences.
Setup
📦 Installing packages
%pip install videodb
🔑 API Keys
import os
from getpass import getpass
# Secure way to enter your VideoDB API key
api_key = getpass("Please enter your VideoDB API Key: ")
os.environ["VIDEO_DB_API_KEY"] = api_key
Steps
🌐 Step 1: Connect to VideoDB
Establish a session for uploading videos. Import the necessary modules from VideoDB library to access functionalities.
import videodb
from videodb import connect
conn = videodb.connect()
coll = conn.get_collection()
🗳️ Step 2: Upload Base Video
Upload and play the video to ensure it's correctly loaded. We’ll be using for the purpose of this tutorial.
# Upload and play a video from a URL
video = coll.upload(url="https://www.youtube.com/watch?v=Nmv8XdFiej0")
video.play()
# Alternatively, get a video from your VideoDB collection
video = coll.get_video('VIDEO_ID_HERE')
video.play()
📥 Step 3: Fetch Data from a Random User API
This code fetches a random user's data (name and picture) from the "randomuser.me" API. You can adapt this to retrieve data from any relevant API (e.g., product data, news articles) for your use case.
import requests
# Make a request to the Randomizer API
response = requests.get('https://randomuser.me/api/?results=1&nat=us,ca,gb,au')
data = response.json()
# Extract relevant information
first_name = data['results'][0]['name']['first']
medium_picture = data['results'][0]['picture']['medium']
🚥 Step 4 : Upload the image to VideoDB
import requests
# 1. Download the image locally
local_path = "my_local_image.jpg"
response = requests.get(medium_picture)
if response.status_code == 200:
with open(local_path, 'wb') as f:
f.write(response.content)
print(f"Image downloaded successfully to: {local_path}")
else:
print(f"Failed to download image. Status code: {response.status_code}")
# 2. Upload using the local file path
from videodb import play_stream, MediaType
image = coll.upload(file_path=local_path, media_type=MediaType.image)
print(f"Image uploaded to VideoDB: {image.id}")
🧱 Step 5: Create VideoDB Assets
We create VideoDB assets for the base video, the user's name (text), and their picture (image) using the new Editor SDK. The `Font` and `Background` objects allow us to customize the appearance of text elements.
from videodb.editor import (
Timeline, Track, Clip,
VideoAsset, TextAsset, ImageAsset,
Font, Background, Alignment, HorizontalAlignment, VerticalAlignment,
Position, Offset, Fit
)
# 1. Video Asset (Base background)
video_asset = VideoAsset(id=video.id, start=0)
# 2. Name Asset (Top)
name_asset = TextAsset(
text=f'Hi {first_name} !',
font=Font(family="Montserrat", size=60, color="#000000"),
background=Background(color="#D2C11D", border_width=20, opacity=1.0),
alignment=Alignment(horizontal=HorizontalAlignment.center, vertical=VerticalAlignment.top),
)
# 3. Message Asset (Middle)
cmon_asset = TextAsset(
text="Here are your favorite moments",
font=Font(family="Montserrat", size=60, color="#D2C11D"),
background=Background(color="#000000", border_width=20, opacity=1.0),
alignment=Alignment(horizontal=HorizontalAlignment.center, vertical=VerticalAlignment.center),
)
# 4. Image Asset (Bottom)
image_asset = ImageAsset(id=image.id)
↔️ Step 6: Create the VideoDB Timeline
Using the Track and Clip pattern, we arrange and layer assets to create a dynamic video stream. The main video goes on one track, while overlays (name, message, image) go on separate tracks with their start times.
# Create the timeline
timeline = Timeline(conn)
# --- Track 1: Main Video ---
video_track = Track()
video_clip = Clip(asset=video_asset, duration=float(video.length))
video_track.add_clip(0, video_clip)
timeline.add_track(video_track)
# --- Track 2: Overlays ---
overlay_track = Track()
# 1. Add Name Overlay (Top)
name_clip = Clip(
asset=name_asset,
duration=4,
position=Position.top,
offset=Offset(y=0.15)
)
overlay_track.add_clip(5, name_clip)
# 2. Add Message Overlay (Center)
cmon_clip = Clip(
asset=cmon_asset,
duration=4,
position=Position.center,
)
overlay_track.add_clip(5, cmon_clip)
# 3. Add Image Overlay (Bottom)
image_clip = Clip(
asset=image_asset,
duration=4,
position=Position.bottom,
scale=2,
fit=None,
offset=Offset(y=-0.15)
)
overlay_track.add_clip(5, image_clip)
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