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@Database(entities = [Word::class], version = 1, exportSchema = false)
abstract class WordDatabase : RoomDatabase() {

    abstract fun wordDao() : WordDao

    companion object{
        // For Singleton instantiation
        @Volatile private var INSTANCE: WordDatabase? = null

        fun getInstance(context: Context): WordDatabase {

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# Editors
.vscode/
.idea/

# Vagrant
.vagrant/

# Mac/OSX
.DS_Store

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import uvicorn
from fastapi import FastAPI
from model import SentimentModel, SentimentQueryModel

app = FastAPI()
model = SentimentModel()

@app.post('/predict')
def predict(data: SentimentQueryModel):
    data = data.dict()

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from fastapi import FastAPI

app = FastAPI()


@app.get("/")
async def root():
    return {"message": "Hello World"}

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import requests
import pandas as pd
from prefect import task, Flow
import json



@task
def extract(url_from):
    response = requests.get(url_from)

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df = pd.read_csv('data.csv')
df.head()

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from sklearn.model_selection import train_test_split

X, y = df.drop('DEATH_EVENT', axis=1), df['DEATH_EVENT']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

X_train.shape, X_test.shape, y_train.shape, y_test.shape

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# COLUMN PIPELINE

from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
›

col_transformation_pipeline = Pipeline(steps=[
    ('impute', SimpleImputer(strategy='mean')),

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# MODEL Pipeline

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score
import matplotlib.pyplot as plt

# random forest classifier
rf_classifier = RandomForestClassifier(n_estimators = 11, criterion='entropy', random_state=0)

rf_model_pipeline = Pipeline(steps=[

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# Do grid search

from sklearn.model_selection import GridSearchCV

rf_classifier = RandomForestClassifier(random_state=0)

rf_model_pipeline = Pipeline(steps=[
    ('preprocessing', columns_transformer),
    ('rf_model', rf_classifier),
])