Add monitoring examples with FastAPI: Hugging Face and spaCy

The idea would be to add a guide (as a Jupyter Notebook) to be included under docs/guides. This Jupyter notebook will showcase the RubrixHTTPMiddleware for monitoring the predictions of a FastAPI inference endpoint. Here is the example with Hugging Face + FastAPI:

from fastapi import FastAPI
from typing import List
from transformers import pipeline
from rubrix.client.asgi import RubrixLogHTTPMiddleware

classifier = pipeline("sentiment-analysis", return_all_scores=True)

app = FastAPI()

# define the middleware for logging predictions into a Rubrix Dataset
app.add_middleware(
    RubrixLogHTTPMiddleware,
    api_endpoint="/predict",
    dataset="monitoring_dataset_v1",
    # you could post-process the predict output with a custom record_mapper function
    # record_mapper=custom_text_classification_mapper,
)

# prediction endpoint
@app.post("/predict")
def predict_batch(batch: List[str]):
    predictions = classifier(batch)
    return [
        {
            "labels": [p["label"] for p in prediction],
            "probabilities": [p["score"] for p in prediction],
        }
        for prediction in predictions
    ]

The steps would be to:

1. Create a notebook and include the above example
2. Add an example with a pre-trained transformer TokenClassifier (for example: https://huggingface.co/dslim/bert-base-NER)
3. Add an example with a spaCy NER pipeline.
4. (Optionally) Include an example dashboard with Kibana (screenshots, gif or video)
5. (Optionally) Include an example with ray serve

[dvsrepo]

Great Aymane, we’ll do this before the next release, thanks for your work!

[Aymane11]

@dvsrepo absolutely, this is my Twitter handle : _Enamya