main.py

from ratings.configuration.mongo_db_connection import MongoDBClient
from ratings.exception import RatingsException
import os,sys
from ratings.logger import logging
from ratings.pipeline import training_pipeline
from ratings.pipeline.training_pipeline import TrainPipeline
from ratings.constant.training_pipeline import SAVED_MODEL_DIR
from fastapi import FastAPI
from ratings.constant.application import APP_HOST, APP_PORT
from starlette.responses import RedirectResponse
from uvicorn import run as app_run
from fastapi.responses import Response
from ratings.ml.model.estimator import ModelResolver
from ratings.utils.main_utils import load_object
from fastapi.middleware.cors import CORSMiddleware



app = FastAPI()
origins = ["*"]

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


@app.get("/", tags=["authentication"])
async def index():
    return RedirectResponse(url="/docs")

@app.get("/train")
async def train_route():
    try:

        train_pipeline = TrainPipeline()
        if train_pipeline.is_pipeline_running:
            return Response("Training pipeline is already running.")
        train_pipeline.run_pipeline()
        return Response("Training successful !!")
    except Exception as e:
        return Response(f"Error Occurred! {e}")

# @app.get("/predict")
# async def predict_route():
#     try:
#         #get data from user csv file
#         #conver csv file to dataframe

#         df=None
#         model_resolver = ModelResolver(model_dir=SAVED_MODEL_DIR)
#         if not model_resolver.is_model_exists():
#             return Response("Model is not available")
        
#         best_model_path = model_resolver.get_best_model_path()
#         model = load_object(file_path=best_model_path)
#         y_pred = model.predict(df)
#         df['predicted_column'] = y_pred
#         df['predicted_column'].replace(TargetValueMapping().reverse_mapping(),inplace=True)
        
#     except Exception as e:
#         raise Response(f"Error Occured! {e}")


def main():
    try:
        training_pipeline = TrainPipeline()
        training_pipeline.run_pipeline()

        
    except Exception as e:
        print(e)
        logging.exception(e)

    

if __name__ == '__main__':
    app_run(app, host=APP_HOST, port=APP_PORT)