Delete app

app/gbc.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:19e494ff5a315df3785cc4cbc700915b1ba243050093a839cf2055079952a5b8
-size 177821

app/main.py

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-from fastapi import FastAPI
-from pydantic import BaseModel
-import pickle
-import pandas as pd
-import numpy as np
-import uvicorn
-import os
-from sklearn.preprocessing import StandardScaler
-import joblib
-
-    
-""" Creating the FastAPI Instance. i.e. foundation for our API,
-which will be the main part of our project"""
-
-app = FastAPI(title="API")
-
-
-"""We load a machine learning model and a scaler that help us make predictions based on data."""
-model = joblib.load(r'C:\Users\viole\OneDrive\Documents\sepsis P6\ml\gbc.pkl', mmap_mode='r')
-scaler = joblib.load(r'C:\Users\viole\OneDrive\Documents\sepsis P6\ml\scaler.pkl', mmap_mode='r')
-
-"""We define a function that will make predictions using our model and scaler."""
-def predict(df, endpoint='simple'):
-    # Scaling
-    scaled_df = scaler.transform(df)
-
-    # Prediction
-    prediction = model.predict_proba(scaled_df)
-    highest_proba = prediction.max(axis=1)
-
-    predicted_labels = ["Patient does not have sepsis" if i == 0 else "Patient has Sepsis" for i in highest_proba]
-    response = []
-    for label, proba in zip(predicted_labels, highest_proba):
-        output = {
-            "prediction": label,
-            "probability of prediction": str(round(proba * 100)) + '%'
-        }
-        response.append(output)
-    return response
-
-
-"""We create models for the data that our API will work with.
-We define what kind of information the data will have.
-It's like deciding what information we need to collect and how it should be organized."""
-
-
-"""These classes define the data models used for API endpoints.
-The 'Patient' class represents a single patient's data,
-and the 'Patients' class represents a list of patients' data.
-The Patients class also includes a class method return_list_of_dict()
-that converts the Patients object into a list of dictionaries"""
-
-class Patient(BaseModel):
-    Blood_Work_R1: float
-    Blood_Pressure: float
-    Blood_Work_R3: float
-    BMI: float
-    Blood_Work_R4: float
-    Patient_age: int
-
-
-"""Next block of code defines different parts of our API and how it responds to different requests.
-It sets up a main page with a specific message, provides a checkup endpoint to receive
-optional parameters, and sets up prediction endpoints to receive medical data for making predictions,
-either for a single patient or multiple patients."""
-
-@app.get("/")
-def root():
-    return {"API": "This is an API for sepsis prediction."}
-
-# Prediction endpoint (Where we will input our features)
-@app.post("/predict")
-def predict_sepsis(patient: Patient):
-
-    # Make prediction
-    data = pd.DataFrame(patient.dict(), index=[0])
-    scaled_data = scaler.transform(data)
-    parsed = predict(df=scaled_data)
-    return {"output": parsed}
-
-
-if __name__ == "__main__":
-    os.environ["DEBUG"] = "True"  # Enable debug mode
-    uvicorn.run("main:app", reload=True)

app/scaler.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9cfc028874b4c2aa6d9806f11a4a14c338bffcf00d34fac767a8a2232a498e9b
-size 1159