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ta_ml / api /predict.py

muhammadjalaludin

Implement service layers for backup, model management, file handling, tokenization, and training

1424118 over 1 year ago

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# ta_ml/api/predict.py
"""
API endpoint for model prediction.
"""
import logging

from fastapi import APIRouter, Depends, HTTPException

from api.dependencies import get_prediction_service
from models.api import PredictRequest, PredictResponse
from services.prediction_service import PredictionService

logger = logging.getLogger(__name__)
router = APIRouter(prefix="/predict", tags=["Prediction"])


@router.post(
    "",
    response_model=PredictResponse,
    summary="Predict a transaction category",
    description="Predicts the category for a given transaction text using the currently loaded model.",
    responses={
        200: {
            "description": "Prediction was successful.",
            "content": {
                "application/json": {
                    "example": {
                        "predicted_label": "Groceries",
                        "confidence": 0.9876,
                    }
                }
            },
        },
        400: {"description": "Invalid input, such as an empty transaction text."},
        422: {"description": "Validation error, e.g., the 'text' field is missing from the request body."},
        500: {"description": "An internal server error occurred during model inference."},
    },
)
def predict_transaction(
    request: PredictRequest,
    prediction_service: PredictionService = Depends(get_prediction_service)
):
    try:
        result = prediction_service.predict(request.text)
        return PredictResponse(**result)
    except ValueError as e:
        logger.warning(f"Prediction failed due to invalid input: {e}")
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        logger.error(
            f"An unexpected error occurred during prediction: {e}", exc_info=True)
        raise HTTPException(
            status_code=500, detail="An internal error occurred during prediction."
        )

	# ta_ml/api/predict.py
	"""
	API endpoint for model prediction.
	"""
	import logging

	from fastapi import APIRouter, Depends, HTTPException

	from api.dependencies import get_prediction_service
	from models.api import PredictRequest, PredictResponse
	from services.prediction_service import PredictionService

	logger = logging.getLogger(__name__)
	router = APIRouter(prefix="/predict", tags=["Prediction"])


	@router.post(
	"",
	response_model=PredictResponse,
	summary="Predict a transaction category",
	description="Predicts the category for a given transaction text using the currently loaded model.",
	responses={
	200: {
	"description": "Prediction was successful.",
	"content": {
	"application/json": {
	"example": {
	"predicted_label": "Groceries",
	"confidence": 0.9876,
	}
	}
	},
	},
	400: {"description": "Invalid input, such as an empty transaction text."},
	422: {"description": "Validation error, e.g., the 'text' field is missing from the request body."},
	500: {"description": "An internal server error occurred during model inference."},
	},
	)
	def predict_transaction(
	request: PredictRequest,
	prediction_service: PredictionService = Depends(get_prediction_service)
	):
	try:
	result = prediction_service.predict(request.text)
	return PredictResponse(**result)
	except ValueError as e:
	logger.warning(f"Prediction failed due to invalid input: {e}")
	raise HTTPException(status_code=400, detail=str(e))
	except Exception as e:
	logger.error(
	f"An unexpected error occurred during prediction: {e}", exc_info=True)
	raise HTTPException(
	status_code=500, detail="An internal error occurred during prediction."
	)