from datasets import DatasetBuilder, GenerateMode
from Bio import SeqIO
from typing import Any, Dict, List, Tuple
from Bio.SeqUtils import gc_fraction
import os
import gzip

class GenomeDataset(DatasetBuilder):
    VERSION = datasets.Version("1.0.0")
    
    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features({
                "DNA_id": datasets.Value("string"),
                "organism": datasets.Value("string"),
                "year": datasets.Value("string"),
                "region_type": datasets.Value("string"),
                "specific_class": datasets.Value("string"),
                "product": datasets.Value("string"),
                "sequence": datasets.Value("string"),
                "gc_content": datasets.Value("float"),
                "translation_code": datasets.Value("string"),
            })
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
      downloaded_files = dl_manager.download_and_extract('https://ftp.ncbi.nih.gov/genbank/') 
      train_files = downloaded_files[:int(len(downloaded_files) * 0.8)] # first 80% for training
      test_files = downloaded_files[int(len(downloaded_files) * 0.8):]  # last 20% for testing

      return [
          datasets.SplitGenerator(
              name=datasets.Split.TRAIN,
              gen_kwargs={"filepaths": train_files}
          ),
          datasets.SplitGenerator(
              name=datasets.Split.TEST,
              gen_kwargs={"filepaths": test_files}
          )
      ]


    def _generate_examples(self, filepaths: List[str]) -> Tuple[str, Dict[str, Any]]:
        for filepath in filepaths:
            if filepath.endswith(".seq.gz"):
                with gzip.open(filepath, 'rt') as handle:
                    for record in SeqIO.parse(handle, "genbank"):
                        if 'molecule_type' in record.annotations and record.annotations['molecule_type'] == 'DNA':
                            organism = record.annotations.get('organism', 'unknown')
                            collection_date = record.annotations.get('date', 'unkown')
                            year = collection_date.split('-')[-1] if '-' in collection_date else collection_date
                            for feature in record.features:
                                if feature.type in ['rRNA', 'tRNA','CDS','tmRNA']:
                                    region_type = 'coding'
                                    product = feature.qualifiers.get('product', ['Unknown'])[0]
                                    seq = feature.extract(record.seq)
                                    gc_content = gc_fraction(seq)
                                    if feature.type == 'CDS':
                                        translation = feature.qualifiers.get('translation', [''])[0]
                                        specific_class = 'Protein'
                                    else:
                                        translation = 'NA'
                                        specific_class = feature.type
                                elif feature.type == 'regulatory':
                                    region_type = feature.type
                                    specific_class = feature.qualifiers.get('regulatory_class', ['regulatory'])[0]
                                    seq = feature.extract(record.seq)
                                    gc_content = gc_fraction(seq)
                                    #gene_tag = feature.qualifiers.get('locus_tag', [''])[0]
                                    translation = 'NA'
                                    product = 'NA'
                                elif feature.type == 'gene':
                                    continue
                                else:
                                    region_type = feature.type
                                    seq = feature.extract(record.seq)
                                    gc_content = gc_fraction(seq)
                                    specific_class = 'NA'
                                    translation = 'NA'
                                    product = 'NA'
                                yield record.id, {
                                    'DNA_id': record.id,
                                    'organism': organism, 
                                    'year': year, 
                                    'region_type': region_type, 
                                    'specific_class': specific_class, 
                                    'product': product, 
                                    'sequence': str(seq), 
                                    'gc_content': gc_content, 
                                    'translation_code': translation
                                }