Python Hotel Menu Chatbot with Flask and JSON

Asynchronous APIs ... Simple. If you've built with Flask, Starlette's mental model is familiar—routes, requests, responses. What changes is the foundation: Starlette is async-first, which means your server handles many requests concurrently without spawning threads. In Day 1, we go from zero to: ✓ A running local server with Uvicorn ✓ Two working API endpoints (GET routes) ✓ A POST handler that reads JSON and creates new tasks Plus: why async matters, why the async/await pattern is everywhere in Starlette, and what the request object actually gives you. No complex setup. Just fresh virtual environment + pip install starlette uvicorn + 30 lines of code that's already production-shaped. → Read the full Day 1 (of 5) Article here: https://lnkd.in/gRNJdhaz #Starlette #Python #ASGI #BackendDevelopment #APIDevelopment #Tutorial #linkedin

Why FastAPI is taking over Python Backend Development 🚀 FastAPI is no longer just a trend; it’s one of the most powerful and modern frameworks for building high-performance APIs with Python. Whether you are a beginner or a seasoned pro, here is a simplified breakdown of what makes it a game-changer: 🎯 The Purpose Performance: Built on Starlette and Pydantic, it’s one of the fastest Python frameworks available. Modern Integration: Designed for seamless use with modern frontend and mobile apps. Auto-Docs: Forget manual documentation. It generates Swagger UI and ReDoc automatically. 🛠 The Main Methods (CRUD) GET: Retrieve data from your server. POST: Create new records or send data. PUT: Update existing information. DELETE: Remove data securely. 📦 Flexible Response Types FastAPI isn’t just for text. It handles: ✅ JSON: The industry standard for API data. ✅ HTML: For serving web pages directly. ✅ Files: For handling downloads and media. ✅ Pydantic Models: Ensuring your data is structured and validated automatically. 💡 My Takeaway As someone working at the intersection of SQL, Python, and Machine Learning, FastAPI is the bridge that turns static models into real-world, scalable applications. It makes backend development faster, cleaner, and significantly more efficient. The tech world—from startups to giants like Microsoft and Netflix—is leaning into these modern stacks for a reason. 🌐 #WebDevelopment #SoftwareEngineering #FastAPI #Python #BackendDevelopment #API #DataEngineering #MachineLearning #AI #Tech #Programming #Developers #Coding #LearnToCode #TechCommunity #100DaysOfCode #CareerGrowth #Innovation #CloudComputing

Actionpackd Knowledge bites - Day 46 What is flask in python ? Flask is a lightweight Python web framework used to build web applications and APIs quickly. It follows a minimalistic approach, giving developers full control instead of enforcing strict project structures. Key features : 1. Lightweight and flexible (micro-framework) 2. Built-in development server and debugger 3. Uses Jinja2 templating engine 4. REST API friendly 5. Easy integration with databases and extensions How it works ? 1. Define routes (URLs) using decorators 2. Each route maps to a Python function 3. Function processes request and returns response 4. Server renders output (HTML/JSON) Example use case • Backend for AI apps (e.g., serving a model via API) • Lightweight dashboards • MVPs and quick prototypes Why it’s popular ? • Simple to learn and start • Highly customizable • Large ecosystem of extensions , like Flask SQLAlchemy , Flask Login and more . #Actionpackd #KnowledgeBites #Flask #Python #AI

I used to think prompting was just “writing instructions.”But now seems, it is much closer to interface design. Think about APIs: - You define inputs clearly - You enforce schemas so outputs are structured - You handle edge cases and document expected behavior - And then you test everything Prompts should be no different. Here is what I do now: - Use structured outputs, Pydantic in Python makes this easy - Make the model return valid JSON every single time - Define exactly what happens: - when the model is uncertain - when the input is invalid - when the task can’t be completed A few things that i would say changed my approach: If a prompt breaks when the input changes slightly → it’s not production ready If it only works on the examples I tested → it’s fragile I believe that You should treat prompts like code: - Store them in files - Version control them - Write tests for them The biggest insight i want to share? A prompt alone does not solve anything. The system around it does. When you design prompts this way, results stop being random and start being predictable and reliable, even in messy, real-world situations. This was from my side. If you found anything else insightfull, must share <3

I've been building web apps with Next JS and have used React Native as a student for a while Recently I decided to explore AI/ML, I wanted to begin from the fundamentals So instead of jumping straight into machine learning models, I started with Python and built a small project: a CLI Task Manager. GitHub Repo - https://lnkd.in/gej-ZwFG Features: • Add tasks • Remove tasks • Edit tasks • Mark tasks complete • Filter tasks • Persistent storage using JSON This helped me refresh my python concepts like dictionaries, file handling, functions, and error handling. Next step: learning web scraping with Python. Trying to learn AI/ML the right way by building things step by step. #Python #AI #MachineLearning #WebDevelopment #BuildInPublic

Why choose Laravel when AI stacks are buzzing with Node and Python? 🤔 Discover why one developer sticks with Laravel in an AI-driven world and explore the unique strengths it offers for AI apps. Dive into insights on Laravel's cohesive stack, built-in tools, and why it's a standout choice! https://lnkd.in/efbataKp

Typed-FFMpeg 4.0 Release I built typed-ffmpeg, a Python package that lets you build FFmpeg filter graphs with full type safety, autocomplete, and validation. It’s inspired by ffmpeg-python, but addresses long-standing issues like lack of IDE support and fragile CLI strings. What’s New in v4.0: TypeScript support — Full TypeScript bindings with the same API as Python. Works in Node.js and the browser. Code-generated from FFmpeg source, so every filter and option is typed. parse() — reverse-engineer any FFmpeg command — Paste an FFmpeg CLI string and get back a typed filter graph object, in both Python and TypeScript. Useful for learning, migrating legacy scripts, or building tools on top of FFmpeg. Per-version packages — Instead of one 10 MB bundle with all FFmpeg versions, you now install only what you need: pip install typed-ffmpeg-v7 (~300kb). Packages exist for FFmpeg 5 through 8. FFmpeg 8.0 support — Full compatibility with the latest FFmpeg release. GitHub: https://lnkd.in/gHZAV7QG I’d love feedback, bug reports, or ideas. Thanks! — David (maintainer)

For the past 4 days I've been learning NestJS — decorators, controllers, dependency injection. Today I decided to explore FastAPI in parallel. Not because I'm abandoning NestJS. But because good backend developers shouldn't be locked to one ecosystem. Here's what I discovered on Day 1 👇 The concepts are surprisingly similar: NestJS uses @Controller() and @Get() decorators. FastAPI uses @router.get() — same decorator pattern, Python syntax. NestJS uses DTOs with class-validator for input validation. FastAPI uses Pydantic models — cleaner, built into Python's type system. NestJS requires Swagger setup manually. FastAPI generates interactive API docs automatically at /docs. No config needed. What genuinely impressed me: FastAPI's speed. It's one of the fastest Python frameworks — async by default, built on Starlette. For data-heavy backends and ML integrations, it makes a lot of sense. My honest take after Day 1: If you know NestJS, FastAPI is not scary. The mental model transfers. You're just learning Python idioms, not a new way of thinking about backend architecture. The best backend developers I've seen are polyglot — they pick the right tool for the job, not the tool they're most comfortable with. That's the skill I'm building. Day 2 of FastAPI tomorrow. Following along? Drop a comment — would love to connect with others exploring both ecosystems. #FastAPI #NestJS #Python #BackendDevelopment #LearningInPublic #SoftwareEngineering #NodeJS #100DaysOfCode #WebDevelopment

You’re probably writing more Python code than you need to. Small tricks can make your code cleaner, faster, and easier to read which AI might also miss if not prompted well. I came across a collection of 100 Python tips that covers both basics and practical patterns 1] Cleaner syntax • List, set, and dictionary comprehensions for concise code   • Swapping variables and unpacking in a single line  Less code, same logic. 2] Built-in power • Modules like collections, itertools, datetime   • Functions like enumerate, zip, sorted  Python already gives you most of what you need. 3] Writing efficient code • Generators vs list comprehensions (memory vs speed tradeoff)   • Using built-ins instead of manual loops  Efficiency often comes from using the right abstraction. 4] Working with real tasks • File handling, PDFs, screenshots, web automation   • Data handling with pandas  5] Debugging and readability • Assertions for early error detection   • Zen of Python principles  Good code is easy to understand. Python is simple. But writing clean Python is a skill. #python #programming #developer #coding #softwareengineering

🚀 Project Update | AI-frontendGenerator I recently built a project using Flask (Python) and the Groq API, where the frontend is generated using AI-frontendGenerator — an AI-powered tool that converts natural language prompts into clean HTML, CSS, and JavaScript. This project helped me understand how AI-assisted frontend generation can speed up development while still allowing flexibility for customization. Integrating it with a Python backend was a great learning experience. Big thanks to Pawan Sharma sir for the guidance and insights throughout the process 🙌 🔗 GitHub: https://lnkd.in/grZVvqNC #Python #Flask #AIProjects #WebDevelopment #BackendDevelopment