import os
from typing import Dict, List
import uuid
import csv

import numpy as np
from PIL import Image

def write_row(filepath:str, row: Dict):
    new_file = not os.path.isfile(filepath)
    with open(filepath, mode="a", newline="") as file:
        fieldnames = row.keys()
        writer = csv.DictWriter(file, fieldnames=fieldnames)
        if new_file:
            writer.writeheader()  # Write header if new file
        writer.writerow(row)  # Write the row

class Feedback():
    def __init__(self, 
                 image_dir = './data/image', 
                 mask_dir = './data/mask',
                 inference_csv = './data/inference.csv',
                 feedback_csv = './data/feedback.csv',
                 ):
        os.makedirs(image_dir, exist_ok=True)
        os.makedirs(mask_dir, exist_ok=True)
        self.image_dir = image_dir
        self.mask_dir = mask_dir
        self.inference_csv = inference_csv
        self.feedback_csv = feedback_csv

    def save_inference(self, pt_coords:List, pt_labels:List, image: Image.Image, mask: np.ndarray):
        self.inference_id = uuid.uuid4()
        image_path = os.path.join(self.image_dir,f'{self.inference_id}.png')
        mask_path = os.path.join(self.mask_dir, f'{self.inference_id}.npy')
        image.save(image_path)
        np.save(mask_path, mask)
        write_row(
            filepath=self.inference_csv,
            row = {
                "inference_id": self.inference_id,
                "image": image_path,
                "mask": mask_path,
                "pt_coords": str(pt_coords),
                "pt_labels": str(pt_labels),
            }
        )

    def save_feedback(self, cutout_idx:int=None, feedback_str:str=None, like:int=None):
        write_row(
            filepath=self.feedback_csv,
            row = {
                "inference_id": self.inference_id,
                "cutout_idx": cutout_idx,
                "feedback_str": feedback_str,
                "like": like,
                }
        )