main.py

from fastapi import FastAPI
import os
from pydantic import BaseModel
import uvicorn
import numpy as np
import pandas as pd 
from fastapi.middleware.cors import CORSMiddleware
from prediction_model.predict import generate_predictions 
from prometheus_fastapi_instrumentator import Instrumentator

port = int(os.environ.get("PORT", 8005))
app = FastAPI(
    title="Loan Prediction App using API - CI CD Jenkins",
    description = "A Simple CI CD Demo",
    version='1.0'
)

origins=[
    "*"
]

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"]
)

class LoanPrediction(BaseModel):
    Gender: str
    Married: str
    Dependents: str
    Education: str
    Self_Employed: str
    ApplicantIncome: float
    CoapplicantIncome: float
    LoanAmount: float
    Loan_Amount_Term: float
    Credit_History: float
    Property_Area: str


@app.get("/")
def index():
    return {"message":"Welcome to Loan Prediction App using API - CI CD Jenkins" }

@app.post("/prediction_api")
def predict(loan_details: LoanPrediction):
    data = loan_details.model_dump()
    prediction = generate_predictions([data])["prediction"][0]
    if prediction == "Y":
        pred = "Approved"
    else:
        pred = "Rejected"
    return {"status":pred}

@app.post("/prediction_ui")
def predict_gui(Gender: str,
    Married: str,
    Dependents: str,
    Education: str,
    Self_Employed: str,
    ApplicantIncome: float,
    CoapplicantIncome: float,
    LoanAmount: float,
    Loan_Amount_Term: float,
    Credit_History: float,
    Property_Area: str):

    input_data = [Gender, Married,Dependents, Education, Self_Employed,ApplicantIncome,
     CoapplicantIncome,LoanAmount, Loan_Amount_Term,Credit_History, Property_Area  ]
    
    cols = ['Gender', 'Married', 'Dependents', 'Education',
       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',
       'Loan_Amount_Term', 'Credit_History', 'Property_Area']
    
    data_dict = dict(zip(cols,input_data))
    prediction = generate_predictions([data_dict])["prediction"][0]
    if prediction == "Y":
        pred = "Approved"
    else:
        pred = "Rejected"
    return {"status":pred}

if __name__== "__main__":
    uvicorn.run("main:app", host="0.0.0.0",port=port,reload=False)

Instrumentator().instrument(app).expose(app)