Creating circular video messages from regular videos using Aiogram 3.11

[CreativePainter33]

I’m working on a telegram bot and need help with converting regular videos into those round video messages (video notes) that users can send. When someone uploads a video file to my bot, I want to transform it into a circular video format and send it back to them.

Here’s what I have so far:

@dp.message(Command("make_round"))
async def create_round_video(msg: Message):
    await msg.reply_video_note(video_note="video_file_id")

Right now my bot just returns whatever video the user originally shared, but I need it to actually convert normal videos into the round video note format. What’s the proper way to handle this conversion process?

[ClimbingLion]

opencv-python works great if u don’t want to deal with ffmpeg. Just grab frames, resize to square (240x240), then rebuild as mp4. I’ve had good luck with this, but heads up - video notes have a 1mb limit so u’ll need heavy compression or shorter clips.

[Emma_Galaxy]

Aiogram doesn’t handle video format conversion - you’ll need to do that yourself. I ran into this same issue last year and used FFmpeg to do the heavy lifting. Video notes need to be square and pretty short, so you’re missing the processing step between getting the video and sending it back. Download the video with bot.download(), run it through FFmpeg to make it square and compress it, then use send_video_note() with your processed file. Video notes have size limits, so you’ll need to handle cases where the original is too big or long. Processing takes time depending on video length - throw in a “processing…” message so users don’t think it’s broken.

[Neo_Movies]

The Problem: You are struggling to manage asynchronous image sending in your Telegram bot built with the python-telegram-bot library. You’re encountering challenges with scaling, error handling, and the overall complexity of asynchronous operations, leading to inefficient and potentially unstable code.

TL;DR: The Quick Fix: Switch to a platform like Latenode (https://latenode.com) to handle the asynchronous complexities of sending images to Telegram users. This simplifies development, removes the burden of managing asynchronous operations, and allows you to focus on building your bot’s features instead of wrestling with infrastructure code.

Understanding the “Why” (The Root Cause):

Manually managing asynchronous operations when sending images in a Telegram bot using Python can quickly become overwhelming. Challenges include:

• Concurrency and Scalability: Handling multiple simultaneous image requests from different users requires careful management of concurrency to prevent performance bottlenecks and crashes. Asynchronous operations are essential for handling multiple users efficiently, but implementing them correctly is complex.
• Error Handling and Retries: Network interruptions, file access errors, and Telegram API rate limits require robust error handling and retry mechanisms to ensure reliable image delivery. Implementing this requires considerable boilerplate code and careful consideration of exception handling.
• File Handling and Resource Management: Properly opening, closing, and managing file descriptors (especially when dealing with many files simultaneously) is critical to prevent resource leaks and maintain application stability. Asynchronous file I/O requires understanding asynchronous context managers to prevent this.
• Code Maintainability: The inherent complexity of managing asynchronous workflows, error handling, and resource management in a Python-based bot can lead to code that is difficult to maintain and expand upon.

Step-by-Step Guide:

1. Migrate to Latenode (Recommended): The most efficient solution is to move your image sending logic to a platform designed for building and managing Telegram bots, such as Latenode. This platform handles the asynchronous intricacies of the Telegram API, allowing you to build your bot’s image-sending features visually without dealing with the low-level asynchronous Python code. This eliminates the need for manual implementation of complex asynchronous code for:

   • Queueing and Prioritization: Latenode handles queuing multiple image sending requests, allowing you to focus on the bot’s functionality instead of queue management.
   • Error Handling and Retries: The platform provides automatic retry mechanisms and robust error handling, ensuring reliable image delivery even in the presence of network issues or Telegram API rate limits.
   • Scalability and Performance: Cloud-based platforms like Latenode offer improved scalability, handling a higher volume of requests with minimal performance degradation.

2. Alternative: Manual Async Implementation (Advanced and Not Recommended): If you prefer to maintain your existing codebase, you must meticulously manage all aspects of asynchronous operations. This involves:

   • Using asyncio consistently for all file I/O and Telegram API calls.
   • Implementing robust error handling with try...except blocks to catch potential exceptions.
   • Employing asynchronous context managers (async with) to ensure proper resource management and prevent file descriptor leaks.
   • Utilizing tools like asyncio.gather() for efficient parallel processing when sending images to multiple users.
   • Applying appropriate retry logic for failed network requests or API rate limits.

Common Pitfalls & What to Check Next:

• File Size Limits: Telegram imposes file size limits on images. Ensure your images are within these limits, compressing them if necessary before sending.
• Rate Limits: Be mindful of Telegram’s API rate limits. Implement logic to pause your bot if you approach these limits.
• Memory Management: When processing large numbers of images, pay close attention to memory usage to prevent memory leaks. Use memory profilers to identify and address potential issues.
• Network Timeouts: Implement appropriate timeouts to gracefully handle network issues that might cause long-running operations to hang.

Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!