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Building a Real-Time Twitch Stats Dashboard with Python

#python #twitch #streaming #webdev

Building a Real-Time Twitch Stats Dashboard with Python

Tracking streamer metrics in real-time is one of the most rewarding projects for any developer interested in the live streaming ecosystem. In this guide, I will walk you through building a lightweight Twitch stats dashboard using Python, the Twitch API, and a simple web frontend.

Why Track Streamer Stats?

Whether you are a content creator looking to optimize your schedule, or a viewer curious about growth trends, having access to real-time data like viewer count, follower evolution, and stream uptime is incredibly valuable. Platforms like Optistream already aggregate detailed streamer profiles and analytics — our goal here is to build something similar from scratch.

Setting Up the Twitch API

First, register an application on the Twitch Developer Console to get your Client ID and Secret.

import requests
import time

CLIENT_ID = "your_client_id"
CLIENT_SECRET = "your_client_secret"

def get_oauth_token():
    url = "https://id.twitch.tv/oauth2/token"
    params = {
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
        "grant_type": "client_credentials"
    }
    resp = requests.post(url, params=params)
    return resp.json()["access_token"]
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Fetching Streamer Data

With the token in hand, we can query any streamer's live status and channel info:

def get_stream_info(username, token):
    headers = {
        "Client-ID": CLIENT_ID,
        "Authorization": f"Bearer {token}"
    }
    url = f"https://api.twitch.tv/helix/streams?user_login={username}"
    resp = requests.get(url, headers=headers)
    data = resp.json()["data"]
    if data:
        stream = data[0]
        return {
            "title": stream["title"],
            "game": stream["game_name"],
            "viewers": stream["viewer_count"],
            "started_at": stream["started_at"]
        }
    return None
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Building the Dashboard

For the frontend, a simple Flask app with auto-refresh works well:

from flask import Flask, render_template_string

app = Flask(__name__)

TEMPLATE = """
<html>
<head><title>Twitch Dashboard</title>
<meta http-equiv="refresh" content="30"></head>
<body>
<h1>{{ streamer }} - Live Stats</h1>
<p>Viewers: {{ viewers }}</p>
<p>Game: {{ game }}</p>
<p>Uptime: {{ uptime }}</p>
</body></html>
"""

@app.route("/dashboard/<username>")
def dashboard(username):
    token = get_oauth_token()
    info = get_stream_info(username, token)
    if info:
        return render_template_string(TEMPLATE, 
            streamer=username, viewers=info["viewers"],
            game=info["game"], uptime=info["started_at"])
    return f"{username} is offline"
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Going Further

To take this further, you could:

  • Store historical data in SQLite and plot trends with matplotlib
  • Add follower count tracking via the /helix/channels/followers endpoint
  • Compare multiple streamers side by side
  • Deploy on a Raspberry Pi for a permanent display

If you want to explore streamer profiles without building anything, check out the streamer directory on optistream.fr — it provides detailed pages for thousands of Twitch streamers with stats, bios, and streaming schedules.

Conclusion

Building a Twitch stats dashboard is a great weekend project that combines API integration, data processing, and web development. The Twitch ecosystem is rich with data, and tools like this help the community better understand streaming trends.

Happy coding! 🎮

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