YT Comment Sentiment - Backend Side¶

Technology	Description
	A Python library for building and training machine learning models.
	A collaboration platform for machine learning, hosting data and MLflow models.
	A platform to manage the ML lifecycle, including model tracking and deployment.
	A modern web framework for building APIs with Python, known for its speed.
	A Python library for data validation. Used to validate API data.
	A testing framework for Python, used to test the FastAPI application.
	An API to access and manage YouTube video data, including comments.
	A cloud platform for hosting APIs, websites, and applications.

What I followed to know?¶

Important

As I am learning Python, Data Science and Machine Learning for more than 3 years. I don't have to look around to learn new things to build this. This part is kind of easy for me.
But as I said earlier, the documentations and ChatGPT is most important resources you can onto.

Need to get an API key from Google Developer Console to interact with YouTube Data API.
Need to create an account on DagsHub to store/track MLFlow experiments and models.
Created a DVC pipeline to run the MLFlow experiments seemlessly using dvc repro command.
After creating the FastAPI app, I've used pytest to test it and also setup a pre-commit for it.
Deployment on render.com.

As I have used uv to manage my project but render.com doesn't support uv out-of-the-box so I have used pip to use uv for dependencies installation.
```
pip install uv && uv sync --extra=backend --compile-bytecode
```
Also, render.com only serve apps on port under $PORT env (which 10000 most of the times) so make sure to explicitly provide while running app through uvicorn or fastapi-cli CLI.
```
# For uvicorn
uvicorn run --host 0.0.0.0 --port $PORT backend.app:app

# For fastapi-cli
fastapi run --host 0.0.0.0 --port $PORT backend/app.py
```

I am using wordcloud to create a plot in a FastAPI route. While building docker image FROM python:3.11-slim image, I am getting error because wordcloud package needs gcc package to build wheels. So you need to explicitly install gcc before install wordcloud as python package.
```
# Install gcc for wordcloud
RUN apt-get update && apt-get install -y gcc && apt-get clean

# Now install project dependencies including wordcloud
# ...
```
Also use multi-stage builds in Dockerfile to reduce the image size. See uv docs.