Introduction Bringing an AI chatbot from your local machine to a live web endpoint can be daunting, but modern PaaS platforms like Heroku make it remarkably straightforward. By combining the lightweight Flask framework with a pre-trained language model, you can create and deploy an interactive AI agent accessible to users worldwide. In this guide, we will walk through every step—code structure, dependency management, Heroku configuration, and continuous delivery—so you can launch your own Flask-based AI chatbot in under an hour. Key Points Section Takeaway Core Concepts Flask fundamentals, WSGI, Procfile, environment variables Real-World Applications Customer support bots, educational assistants, prototype demos Recent Developments GitHub Actions for CI/CD, Docker support on Heroku Ethical & Social Impact User data privacy, misuse prevention, transparency Future Outlook Serverless hosting, real-time scaling, multimodal chatbot front-ends Core Conce...
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