Delete main.py

main.py

@@ -1,135 +0,0 @@
-
-# Importations
-
-from typing import Union
-from fastapi import FastAPI
-import pickle
-from pydantic import BaseModel
-import pandas as pd
-import os
-import uvicorn
-from fastapi import HTTPException, status
-from sklearn.preprocessing import StandardScaler
-from sklearn.preprocessing import LabelEncoder
-
-# Setup Section
-
-# Create FastAPI instance
-app = FastAPI(title="Sepsis Prediction API",
-              description="API for Predicting Sespsis ")
-# A function to load machine Learning components to re-use
-
-
-
-
-
-# def Ml_loading_components(fp):
-#     with open(fp, "rb") as f:
-#         object = pickle.load(f)
-#         return (object)
-
-
-# # Loading the machine learning components
-# DIRPATH = os.path.dirname(os.path.realpath(__file__))
-# ml_core_fp = os.path.join(DIRPATH, "src", "ML", "ML_Model.pkl")
-# ml_components_dict = Ml_loading_components(fp=ml_core_fp)
-
-
-# # Defining the variables for each component
-# label_encoder = ml_components_dict['label_encoder']  # The label encoder
-# # Loaded scaler component
-# scaler = ml_components_dict['scaler']
-# # Loaded model
-# model = ml_components_dict['model']
-# # Defining our input variables
-
-
-
-
-# Define the path to your pickle file
-pickle_file_path = "src/ML/ML_Model.pkl"
-
-# Check if the pickle file exists
-if not os.path.exists(pickle_file_path):
-    raise Exception(f"Pickle file '{pickle_file_path}' not found. Please check the file path.")
-
-# Load your pre-trained machine learning model and preprocessing components using pickle
-with open(pickle_file_path, "rb") as model_file:
-    exported_data = pickle.load(model_file)
-
-# Extract the pre-fitted preprocessing components and model from the loaded components
-scaler = exported_data['scaler']
-label_encoder = exported_data['label_encoder']
-model = exported_data['best_model']
-
-
-
-
-
-
-class InputData(BaseModel):
-    PRG: int
-    PL: int
-    BP: int
-    SK: int
-    TS: int
-    BMI: float
-    BD2: float
-    Age: int
-
-
-"""
-* PRG: Plasma glucose
-
-* PL: Blood Work Result-1 (mu U/ml)
-
-* PR: Blood Pressure (mmHg)
-
-* SK: Blood Work Result-2(mm)
-
-* TS: Blood Work Result-3 (muU/ml)
-
-* M11: Body mass index (weight in kg/(height in m)^2
-
-* BD2: Blood Work Result-4 (mu U/ml)
-
-* Age: patients age(years)
-
-"""
-# Index route
-
-
-@app.get("/")
-def index():
-    return {'message': 'Hello, Welcome to My Sepsis Prediction FastAPI'}
-
-
-# Create prediction endpoint
-@app.post("/predict")
-def predict(df: InputData):
-
-    # Prepare the feature and structure them like in the notebook
-    df = pd.DataFrame([df.dict().values()], columns=df.dict().keys())
-
-    print(f"[Info] The inputed dataframe is : {df.to_markdown()}")
-    age = df['Age']
-    print(age)
-    # Scaling the inputs
-    df_scaled = scaler.transform(df)
-
-    # Prediction
-    raw_prediction = model.predict(df_scaled)
-
-    if raw_prediction == 0:
-        raise HTTPException(status_code=status.HTTP_200_OK,
-                            detail="The patient will Not Develop Sepsis")
-    elif raw_prediction == 1:
-        raise HTTPException(status_code=status.HTTP_200_OK,
-                            detail="The patient Will Develop Sepsis")
-    else:
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Prediction Error")
-
-
-if __name__ == "__main__":
-    uvicorn.run("src.main:app", reload=True)