import os
import math
import shutil
from fastapi import APIRouter
from src.core.utils import logger
from fastapi.responses import JSONResponse
from fastapi import APIRouter, UploadFile, File, HTTPException, Form
from src.app.pipelines.modules import DataQualityAssessmentWorkflow

data_quality_router = APIRouter()

def delete_dir_contents(directory: str)->None:
    for filename in os.listdir(directory):
        file_path = os.path.join(directory, filename)
        if os.path.isfile(file_path):
            os.remove(file_path)

def sanitize_for_json(data):
    if isinstance(data, dict):
        return {k: sanitize_for_json(v) for k, v in data.items()}
    elif isinstance(data, list):
        return [sanitize_for_json(v) for v in data]
    elif isinstance(data, float):
        if math.isinf(data) or math.isnan(data):
            return None
        return data
    return data

@data_quality_router.post('/')
async def main(file: UploadFile = File(...), ml_task: str = Form(None)):
    ''' ## This endpoint accepts a CSV file upload  to initiate the Data Quality Workflow. 

        ### Parameters:
        -----------
        - file : CSV File for the dataset
            
        ### Returns:
        --------
        - dict: Markdown Report
    '''

    if not file.filename.endswith('.csv'):
        raise HTTPException(status_code=400, detail="Only CSV files are allowed.")

    '''Clears the /downloads folder and stores the recieved file under 'dataset.csv' '''
    
    downloads_path = "src/core/cache/downloads"
    # os.makedirs(downloads_path, exist_ok=True)
    delete_dir_contents(downloads_path)
    destination_path = os.path.join(downloads_path, "dataset.csv")
    with open(destination_path, "wb") as buffer:
        shutil.copyfileobj(file.file, buffer)

    logger.info(f"CSV file saved to {destination_path}", log_type='eda-engine/data_quality', console=True)
    
    '''Runs the data quality assessment workflow'''

    try:
        ds_wf = DataQualityAssessmentWorkflow(data_source=f'{downloads_path}/dataset.csv', llm_choice="gpt-4o-mini", ml_task=ml_task)
        results = ds_wf.run(verbose=True)

        sanitized_data = sanitize_for_json(results)

        return JSONResponse(content=sanitized_data)

    except Exception as e:
        logger.error(f"DataQualityAssessmentWorkflow failed with error: {e}", log_type='eda-engine/data_quality', console=True)
        return {
            "status": "Pipeline failed to finish",
        }