import csv
import json
import os
from typing import List
import datasets
import logging
from datetime import datetime, timedelta
import pandas as pd
import requests
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Singapore Traffic Image Dataset},
author={huggingface, Inc.
},
year={2023}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset contains traffic images from traffic signal cameras of singapore. The images are captured at 1.5 minute interval from 6 pm to 7 pm everyday for the month of January 2024.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://beta.data.gov.sg/collections/354/view"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# _URL = "https://raw.githubusercontent.com/Sayali-pingle/HuggingFace--Traffic-Image-Dataset/main/camera_data.csv"

# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class TrafficImages(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    # _URLS = _URLS
    VERSION = datasets.Version("1.1.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "timestamp": datasets.Value("string"),
                    "camera_id": datasets.Value("string"),
                    "latitude": datasets.Value("float"),
                    "longitude": datasets.Value("float"),
                    "image_url": datasets.Image(),
                    "image_metadata": datasets.Value("string")
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )
    
    # def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
    #   urls_to_download = self._URL
    #   downloaded_files = dl_manager.download_and_extract(urls_to_download)

    #   return [
    #       datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
    #       datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
    #   ]
      
    def _split_generators(self, dl_manager: datasets.DownloadManager):
        # The URLs should be the paths to the raw files in the Hugging Face dataset repository
        urls_to_download = {
            "csv_file": "https://raw.githubusercontent.com/Sayali-pingle/HuggingFace--Traffic-Image-Dataset/main/camera_data.csv"
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download['csv_file'])

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "csv_file_path": downloaded_files,
                },
            ),
        ]
  
    def _generate_examples(self, csv_file_path):
        # This method will yield examples from your dataset
        # start_date = datetime(2024, 1, 1, 18, 0, 0)
        # end_date = datetime(2024, 1, 2, 19, 0, 0)
        # interval_seconds = 240

        # date_time_strings = [
        #     (current_date + timedelta(seconds=seconds)).strftime('%Y-%m-%dT%H:%M:%S+08:00')
        #     for current_date in pd.date_range(start=start_date, end=end_date, freq='D')
        #     for seconds in range(0, 3600, interval_seconds)
        # ]

        # url = 'https://api.data.gov.sg/v1/transport/traffic-images'
        # camera_data = []

        # for date_time in date_time_strings:
        #     params = {'date_time': date_time}
        #     response = requests.get(url, params=params)

        #     if response.status_code == 200:
        #         data = response.json()
        #         camera_data.extend([
        #             {
        #                 'timestamp': item['timestamp'],
        #                 'camera_id': camera['camera_id'],
        #                 'latitude': camera['location']['latitude'],
        #                 'longitude': camera['location']['longitude'],
        #                 'image_url': camera['image'],
        #                 'image_metadata': camera['image_metadata']
        #             }
        #             for item in data['items']
        #             for camera in item['cameras']
        #         ])
        #     else:
        #         print(f"Error: {response.status_code}")

        camera_data= pd.read_csv(csv_file_path)

        for idx, example in camera_data.iterrows():
            yield idx, {
                "timestamp": example["timestamp"],
                "camera_id": example["camera_id"],
                "latitude": example["latitude"],
                "longitude": example["longitude"],
                "image_url": example["image_url"],
                "image_metadata": example["image_metadata"]
            }