main.py

import uvicorn
from fastapi import FastAPI
from pydantic import BaseModel
from ml_utils import load_model, predict, retrain
from typing import List
import sys
sys.setrecursionlimit(10000)
from fastapi.responses import FileResponse


# defining the main app
app = FastAPI(title="Cred Scoring", docs_url="/")

# calling the load_model during startup.
# this will train the model and keep it loaded for prediction.
app.add_event_handler("startup", load_model)

# class which is expected in the payload
class QueryIn(BaseModel):
    p1: str = 'A11'
    p2: int = 6
    p3: str = 'A34'
    p4: str = 'A43'
    p5: int = 1169
    p6: str = 'A65'
    p7: str = 'A75'
    p8: int = 4
    p9: str = 'A93'
    p10: str = 'A101'
    p11: int = 4
    p12: str = 'A121'
    p13: int = 67
    p14: str = 'A143'
    p15: str = 'A152'
    p16: int = 2
    p17: str = 'A173'
    p18: int = 1
    p19: str = 'A192'
    p20: str = 'A201'


# class which is returned in the response
class QueryOut(BaseModel):
    loan: str

class FeedbackIn(BaseModel):
    p1: str = 'A11'
    p2: int = 6
    p3: str = 'A34'
    p4: str = 'A43'
    p5: int = 1169
    p6: str = 'A65'
    p7: str = 'A75'
    p8: int = 4
    p9: str = 'A93'
    p10: str = 'A101'
    p11: int = 4
    p12: str = 'A121'
    p13: int = 67
    p14: str = 'A143'
    p15: str = 'A152'
    p16: int = 2
    p17: str = 'A173'
    p18: int = 1
    p19: str = 'A192'
    p20: str = 'A201'
    loan: str = 'Bad'

# Route definitions
@app.get("/ping")
# Healthcheck route to ensure that the API is up and running
def ping():
    return {"ping": "pong"}


@app.post("/cred_scoring", response_model=QueryOut, status_code=200)
# Route to do the prediction using the ML model defined.
# Payload: QueryIn containing the parameters
# Response: QueryOut containing the flower_class predicted (200)
def cred_scoring(query_data: QueryIn):
    output = {"loan": predict(query_data)}
    return output

@app.get("/explain", status_code=200)
# Healthcheck route to ensure that the API is up and running
def explain():
    some_file_path = "dataset/explainable_AI_starter.html"
    return FileResponse(some_file_path, filename="explain.html")

@app.post("/feedback_loop", status_code=200)
# Route to further train the model based on user input in form of feedback loop
# Payload: FeedbackIn containing the parameters and correct loan class
# Response: Dict with detail confirming success (200)
def feedback_loop(data: List[FeedbackIn]):
    retrain(data)
    return {"detail": "Feedback loop successful"}


# Main function to start the app when main.py is called
if __name__ == "__main__":
    # Uvicorn is used to run the server and listen for incoming API requests on 0.0.0.0:8888
    uvicorn.run("main:app", host="0.0.0.0", port=8889, reload=True)