This is a submission for the Amazon Q Developer https://dev.to/challenges/aws-amazon-q-v2025-04-30
What I Built
Language Learning Bot is a clean, interactive web application designed to help users explore and practice multiple languages through real-time translation and speech synthesis. This tool bridges the gap between written and spoken language learning using cloud-based AI services.
Built using Python, Streamlit, and AWS (Translate & Polly), it offers an intuitive interface for translating English text into several languages and listening to the correct pronunciation. It's an ideal foundation for educational tools, language learning platforms, or voice-enabled applications.
Key Features
🌍 Multi-Language Support – Instantly translates English text into six different languages, including French, Spanish, Tamil, and Japanese.
🗣️ Text-to-Speech Output – Uses Amazon Polly to convert translated text into clear, natural-sounding speech.
⚡ Real-Time Interaction – Built with Streamlit for fast, responsive user input and output.
🧠 AWS Integration – Seamlessly connected with AWS Translate and Polly for high-accuracy translation and voice synthesis.
🎯 Minimal UI – Clean, distraction-free interface focused on core functionality for easy learning.
🧩 Extendable Framework – Modular design enables quick scaling to include more languages or educational features.
Demo
https://drive.google.com/file/d/1azxYjryMgyX_1Nf05ox8h829AUIU7Og9/view?usp=sharing
Output Screenshot
Code Repository
https://github.com/gangasris03/Language-Learning-Bot
How I Used Amazon Q Developer
I used Amazon Q Developer throughout this project for:
🧠 Refactoring Python logic to streamline AWS Translate and Polly calls
📦 Configuring AWS SDK (Boto3) efficiently for secure, minimal setup
🎨 UI tweaks in Streamlit to enhance layout, button handling, and input responsiveness
🛠 Debugging API integration issues, especially around language code mismatches
🚀 Optimizing modularity – breaking out functions for translation and speech synthesis cleanly
Amazon Q Developer significantly accelerated development by offering real-time coding suggestions, bug fixes, and architectural advice — all directly from the command line.
Final Thoughts
This project allowed me to explore real-world integration of cloud-based language services while applying clean UI design with Streamlit. It was a valuable experience in combining automation, multilingual support, and user interactivity in a simple, functional app.
I'm proud of how seamlessly the bot handles translation and speech synthesis, and I'm excited to enhance it further—possibly by adding speech input, progress tracking for learners, and more supported languages.
👩💻 Individual Submission
🎓 I'm currently a student and excited to build practical tools like this that make learning more accessible!
💡 Tips & Insights
Utilize Amazon Q Developer for seamless integration with AWS services like Translate and Polly—it helps generate precise, secure API calls instantly.
Use inline suggestions to quickly resolve syntax issues or logic bugs, especially when working with external libraries like boto3.
Take advantage of real-time CLI support to configure cloud resources without leaving your development environment.
Boost efficiency by letting Amazon Q handle repetitive setup and error handling, so you can stay focused on building features that matter.
Collaborated with teammate: @maha_lakshmi_dd37d543f7c9
We had a great collaboration on this project and worked together on all parts of the development process.
📬 Let's Connect
🔗 GitHub: https://github.com/gangasris03
💼 LinkedIn: https://www.linkedin.com/in/ganga-sri-s-005934290/
🌐 DEV Profile: https://dev.to/ganga_sris_249f4b671caaa
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