Delete main.py

main.py

@@ -1,87 +0,0 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
-import pickle
-import pandas as pd
-import numpy as np
-import uvicorn
-
-# call the app
-app = FastAPI(title="API")
-
-# Load the model and scaler
-def load_model_and_scaler():
-    with open("model.pkl", "rb") as f1, open("scaler.pkl", "rb") as f2:
-        return pickle.load(f1), pickle.load(f2)
-
-model, scaler = load_model_and_scaler()
-
-def predict(df, endpoint="simple"):
-    # Scaling
-    scaled_df = scaler.transform(df)  # Scale the input data using a pre-defined scaler
-
-    # Prediction
-    prediction = model.predict_proba(scaled_df)  # Make predictions using a pre-trained model
-
-    highest_proba = prediction.max(axis=1)  # Get the highest probability for each prediction
-
-    # Assign predicted labels based on the highest probabilities
-    predicted_labels = ["Patient does not have sepsis" if i == 0 else "Patient has sepsis" for i in highest_proba]
-    print(f"Predicted labels: {predicted_labels}")  # Print the predicted labels to the terminal
-    print(highest_proba)  # Print the highest probabilities to the terminal
-
-    response = []
-    for label, proba in zip(predicted_labels, highest_proba):
-        # Create a response for each prediction with the predicted label and probability
-        output = {
-            "prediction": label,
-            "probability of prediction": str(round(proba * 100)) + '%'  # Convert the probability to a percentage
-        }
-        response.append(output)  # Add the response to the list of responses
-
-    return response  # Return the list of responses
-
-
-class Patient(BaseModel):
-    Blood_Work_R1: int
-    Blood_Pressure: int
-    Blood_Work_R3: int
-    BMI: float
-    Blood_Work_R4: float
-    Patient_age: int
-
-class Patients(BaseModel):
-    all_patients: list[Patient]
-
-    @classmethod
-    def return_list_of_dict(cls, patients: "Patients"):
-        patient_list = []
-        for patient in patients.all_patients: #for each item in all_patients,
-            patient_dict = patient.dict() #convert to a dictionary
-            patient_list.append(patient_dict) #add it to the empty list called patient_list
-        return patient_list
-    
-# Endpoints
-# Root Endpoint
-@app.get("/")
-def root():
-    return {"API": "This is an API for sepsis prediction."}
-
-# Prediction endpoint
-@app.post("/predict")
-def predict_sepsis(patient: Patient):
-    # Make prediction
-    data = pd.DataFrame(patient.dict(), index=[0])
-    parsed = predict(df=data)
-    return {"output": parsed}
-
-# Multiple Prediction Endpoint
-@app.post("/predict_multiple")
-def predict_sepsis_for_multiple_patients(patients: Patients):
-    """Make prediction with the passed data"""
-    data = pd.DataFrame(Patients.return_list_of_dict(patients))
-    parsed = predict(df=data, endpoint="multi")
-    return {"output": parsed}
-
-if __name__ == "__main__":
-    uvicorn.run("main:app", reload=True)
-