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from pathlib import Path
# ENV when using standalone uvicorn server running FastAPI in api directory
ENV_PATH = Path('../../env/online.env')
ONE_DAY_SEC = 24*60*60
ONE_WEEK_SEC = ONE_DAY_SEC*7
PIPELINE_FUNCTION_URL = "https://raw.githubusercontent.com/D0nG4667/sepsis_prediction_full_stack/main/dev/models/pipeline_func/pipeline_functions.joblib"
RANDOM_FOREST_URL = "https://raw.githubusercontent.com/D0nG4667/sepsis_prediction_full_stack/main/dev/models/RandomForestClassifier.joblib"
XGBOOST_URL = "https://raw.githubusercontent.com/D0nG4667/sepsis_prediction_full_stack/main/dev/models/XGBClassifier.joblib"
ADABOOST_URL = "https://raw.githubusercontent.com/D0nG4667/sepsis_prediction_full_stack/main/dev/models/AdaBoostClassifier.joblib"
CATBOOST_URL = "https://raw.githubusercontent.com/D0nG4667/sepsis_prediction_full_stack/main/dev/models/CatBoostClassifier.joblib"
DECISION_TREE_URL = "https://raw.githubusercontent.com/D0nG4667/sepsis_prediction_full_stack/main/dev/models/DecisionTreeClassifier.joblib"
KNN_URL = "https://raw.githubusercontent.com/D0nG4667/sepsis_prediction_full_stack/main/dev/models/KNeighborsClassifier.joblib"
LGBM_URL = "https://raw.githubusercontent.com/D0nG4667/sepsis_prediction_full_stack/main/dev/models/LGBMClassifier.joblib"
LOG_REG_URL = "https://raw.githubusercontent.com/D0nG4667/sepsis_prediction_full_stack/main/dev/models/LogisticRegression.joblib"
SVC_URL = "https://raw.githubusercontent.com/D0nG4667/sepsis_prediction_full_stack/main/dev/models/SVC.joblib"
ENCODER_URL = "https://raw.githubusercontent.com/D0nG4667/sepsis_prediction_full_stack/main/dev/models/enc/encoder.joblib"
ALL_MODELS = {
"AdaBoostClassifier": ADABOOST_URL,
"CatBoostClassifier": CATBOOST_URL,
"DecisionTreeClassifier": DECISION_TREE_URL,
"KNeighborsClassifier": KNN_URL,
"LGBMClassifier": LGBM_URL,
"LogisticRegression": LOG_REG_URL,
"RandomForestClassifier": RANDOM_FOREST_URL,
"SupportVectorClassifier": SVC_URL,
"XGBoostClassifier": XGBOOST_URL
}
DESCRIPTION = """
This API identifies ICU patients at risk of developing sepsis using `9 models` of which `Random Forest Classifier` and `XGBoost Classifier` are the best.\n
The models were trained on [The John Hopkins University datasets at Kaggle](https://www.kaggle.com/datasets/chaunguynnghunh/sepsis?select=README.md).\n
### Features
`PRG:` Plasma glucose\n
`PL:` Blood Work Result-1 (mu U/ml)\n
`PR:` Blood Pressure (mm Hg)\n
`SK:` Blood Work Result-2 (mm)\n
`TS:` Blood Work Result-3 (mu U/ml)\n
`M11:` Body mass index (weight in kg/(height in m)^2\n
`BD2:` Blood Work Result-4 (mu U/ml)\n
`Age:` patients age (years)\n
`Insurance:` If a patient holds a valid insurance card\n
### Results
**Sepsis prediction:** *Positive* if a patient in ICU will develop a sepsis, and *Negative* otherwise\n
**Sepsis probability:** In percentage\n
### GraphQL API
To explore the GraphQL sub-application (built-with strawberry) to this RESTFul API click the link below.\n
🍓[GraphQL](/graphql)
### Let's Connect
👨⚕️ `Gabriel Okundaye`\n
[<img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn" width="20" height="20"> LinkendIn](https://www.linkedin.com/in/dr-gabriel-okundaye)
[<img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub" width="20" height="20"> GitHub](https://github.com/D0nG4667/sepsis_prediction_full_stack)
"""
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