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from fastapi.responses import RedirectResponse
from fastapi import FastAPI, Request, HTTPException, APIRouter, Depends
from fastapi.openapi.docs import get_swagger_ui_html
from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
app = FastAPI()
# Load the entire pipeline
pipeline_filepath = "pipeline.joblib"
pipeline = joblib.load(pipeline_filepath)
class PatientData(BaseModel):
Plasma_glucose : float
Blood_Work_Result_1: float
Blood_Pressure : float
Blood_Work_Result_2 : float
Blood_Work_Result_3 : float
Body_mass_index : float
Blood_Work_Result_4: float
Age: float
Insurance: int
@app.get("/")
async def root():
return RedirectResponse(url="/docs")
#swagger ui
@app.get("/docs")
async def get_swagger_ui_html():
return get_swagger_ui_html(openapi_url="/openapi.json", title="API docs")
@app.post("/predict")
def get_data_from_user(data: PatientData):
user_input = data.dict()
input_df = pd.DataFrame([user_input])
# Make predictions using the loaded pipeline
prediction = pipeline.predict(input_df)
probabilities = pipeline.predict_proba(input_df)
probability_of_positive_class = probabilities[0][1]
# Calculate the prediction
sepsis_status = "Positive" if prediction[0] == 1 else "Negative"
sepsis_explanation = "A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention." if prediction[0] == 1 else "A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
result = {
'predicted_sepsis': sepsis_status,
'probability': probability_of_positive_class,
'sepsis_explanation': sepsis_explanation
}
return result
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