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from fastapi import FastAPI, Query, Request
from fastapi.responses import HTMLResponse
from fastapi.templating import Jinja2Templates
import joblib
app = FastAPI()
templates = Jinja2Templates(directory="templates")
# Load the pickled XGBoost model
xgb_model = joblib.load("xgb_model.joblib")
@app.get("/display")
def display_params(
request: Request,
prg: float = Query(..., description="Plasma glucose"),
pl: float = Query(..., description="Blood Work Result-1 (mu U/ml)"),
pr: float = Query(..., description="Blood Pressure (mm Hg)"),
sk: float = Query(..., description="Blood Work Result-2 (mm)"),
ts: float = Query(..., description="Blood Work Result-3 (mu U/ml)"),
m11: float = Query(..., description="Body mass index (weight in kg/(height in m)^2"),
bd2: float = Query(..., description="Blood Work Result-4 (mu U/ml)"),
age: int = Query(..., description="Patient's age (years)")
):
# Prepare input features for prediction
input_features = [prg, pl, pr, sk, ts, m11, bd2, age]
# Make predictions using the loaded model
prediction = xgb_model.predict_proba([input_features])[0]
# Create a JSON response
response = {
"request": {
"prg": prg,
"pl": pl,
"pr": pr,
"sk": sk,
"ts": ts,
"m11": m11,
"bd2": bd2,
"age": age
},
"prediction": {
"class_0_probability": prediction[0],
"class_1_probability": prediction[1]
}
}
return templates.TemplateResponse(
"display_params.html",
{
"request": request,
"prg": prg,
"pl": pl,
"pr": pr,
"sk": sk,
"ts": ts,
"m11": m11,
"bd2": bd2,
"age": age,
"prediction": response["prediction"]
}
)
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