import mlflow.pyfunc
import numpy as np
import pandas as pd
import sys
from flask import Flask, request
from flask_apscheduler import APScheduler
from mlflow.tracking import MlflowClient
logging.basicConfig(level=logging.INFO)
app = Flask(__name__)
scheduler = APScheduler()
scheduler.api_enabled = True
scheduler.app = app
scheduler.init_app(app)
scheduler.start()
logging.getLogger('apscheduler.executors.default').setLevel(logging.WARNING)
app = Flask(__name__)
@app.route('/', methods=["POST"])
def predict():
data = request.get_json()
preds = app.model.predict(data["text"])
ret = []
for i, (text, logits) in enumerate(zip(data["text"], preds)):
ret.append({"id": i, "text": text, "logits": logits.tolist()})
