Write Your Own AI API скачать в хорошем качестве

Write Your Own AI API

We're going to build a simple API that lets you sell AI to anyone. First, make sure you have Python installed. Most computers come with Python already. If you don't have it, go to the Python website and download Python and pip. Open your terminal and type pip install fastapi and hit Enter. You can also put fastapi in a requirements.txt file by running echo fastapi requirements.txt. Then you can use git for version control: run git init to make a repository, git add to add files, git status to check what's staged, and git commit to save your changes. It is smart to use a virtual environment so your packages stay organized. Run python, m venv .venv to create one, and activate it with .venv\Scripts\activate on Windows or source .venv/bin/activate on Mac or Linux. If you need to leave the environment, just type deactivate. Now, pip install fastapi and install any upgrades it suggests. FastAPI is a good choice because it's simple, quick, and clean. Flask is a popular choice too. Both work well for small projects. Let us make main.py and get started. Add from fastapi import FastAPI and then app = FastAPI(). Set up a path with @app.get("/") and write a function that returns "hello." This is your basic server. To keep your git repo clean, add a .gitignore file and make sure to ignore things like .venv. To run your server, you need uvicorn. So, pip install "uvicorn[standard]" and then use uvicorn main:app, -reload. Now you should see your server running on localhost port 8000. Test it in your browser or with curl. If you want more endpoints, just copy and change your function. For POST requests that handle data, you have to accept data from the request body. FastAPI has plenty of easy-to-follow docs that help with this. If you want to use AI like text classification, check out Hugging Face for models and install transformers with pip. Pick a model and use pipeline from transformers. For requirements.txt, list fastapi, uvicorn, and transformers. To keep everything safe, never share your secret API keys. To add more power, you can try models that run on your GPU or even local models with things like Ollama. If you want your API to do more, add other libraries or connect to a database for extra features like tracking users. If you want to benchmark your API, you can use loops in the terminal or tools like Apache Bench to see how it holds up under heavy use. If your project grows or you want to be extra safe, use try and except in your code for errors and think about adding rate limits. That is it. With about twenty lines of code, you can have your own AI API running with Python, FastAPI, and any model you want.