from fastapi import APIRouter, UploadFile, File, HTTPException
from pydantic import BaseModel
from ..utils.file_to_str import file_to_fastas, file_to_seqs, file_to_str
from ..utils.is_valid import raise_valid_dna, raise_valid_rna
from ..utils.str_to_nums import str_to_nums
from ..utils.read_fasta import fasta_to_sequence
from ..utils.str_split import split_str
from ..utils.assert_len import assert_len
from ..utils.try_parse_num import try_parse_float, try_parse_int
from .tags import Tags
import re

router = APIRouter()

class Answer(BaseModel):
    answer: str

class NotImplemented(BaseModel):
    error: str



from ..rosalind.dna import count_dna
@router.post("/dna", response_model=Answer, tags=[Tags.string_algorithm])
async def count_basepairs(file: UploadFile=File(...)):
    """
    Given: A DNA string s of length at most 1000 nt.

    Return: Four integers (separated by spaces) counting the respective number of times that the symbols 'A', 'C', 'G', and 'T' occur in s.

    Sample Dataset
    ```
    AGCTTTTCATTCTGACTGCAACGGGCAATATGTCTCTGTGTGGATTAAAAAAAGAGTGTCTGATAGCAGC
    ```
    Sample Output
    ```
    20 12 17 21
    ```
    """
    contents = await file_to_str(file)
    raise_valid_dna(contents)
    counts = count_dna(contents)
    return {"answer": f"{counts['A']} {counts['C']} {counts['G']} {counts['T']}"} 


from ..rosalind.rna import dna_to_rna

@router.post("/rna", summary="DNA to RNA", response_model=Answer, tags=[Tags.string_algorithm])
async def dna_to_rna_route(file: UploadFile=File(...)):
    """
    Given: A DNA string t having length at most 1000 nt.

    Return: The transcribed RNA string of t.

    Sample Dataset
    ```
    GATGGAACTTGACTACGTAAATT
    ```
    Sample Output
    ```
    GAUGGAACUUGACUACGUAAAUU
    ```
    """
    contents = await file_to_str(file)
    raise_valid_dna(contents)
    return { "answer": dna_to_rna(contents)}


from ..rosalind.recv import reverse_complement as rc

@router.post("/revc", response_model=Answer, tags=[Tags.string_algorithm])
async def reverse_complement(file: UploadFile=File(...)):
    """
    Given: A DNA string s of length at most 1000 bp.

    Return: The reverse complement sc of s.

    Sample Dataset
    ```
    AAAACCCGGT
    ```
    Sample Output
    ```
    ACCGGGTTTT
    ```
    """
    contents = await file_to_str(file)
    raise_valid_dna(contents)
    return { "answer": rc(contents) }


from ..rosalind.fib import fibonacci_rabbits

@router.post("/fib", summary="Fibonacci Rabbits", 
    response_model=Answer, tags=[Tags.combinatorics, Tags.dynamic_programming])
async def get_fibonacci(file:UploadFile=File(...)):
    """
    Given: Positive integers n≤40 and k≤5.

    Return: The total number of rabbit pairs that will be present after n months, if we begin with 1 pair and in each generation, every pair of reproduction-age rabbits produces a litter of k rabbit pairs (instead of only 1 pair).

    Sample Dataset
    ```
    5 3
    ```
    Sample Output
    ```
    19
    ```
    """
    contents = await file_to_str(file)
    nums = str_to_nums(contents, None, 2)
    months = int(nums[0])
    pairs = int(nums[1])
    rabbits = fibonacci_rabbits(months, pairs)
    return {"answer": rabbits}


from ..rosalind.gc import max_gc_dna

@router.post("/gc", summary="Max GC DNA", response_model=Answer, tags=[Tags.string_algorithm])
async def get_gc_content(file: UploadFile = File(...)):
    """
    Given: At most 10 DNA strings in FASTA format (of length at most 1 kbp each).

    Return: The ID of the string having the highest GC-content, followed by the GC-content of that string. Rosalind allows for a default error of 0.001 in all decimal answers unless otherwise stated; please see the note on absolute error below.

    Sample Dataset

    ```
    >Rosalind_6404
    CCTGCGGAAGATCGGCACTAGAATAGCCAGAACCGTTTCTCTGAGGCTTCCGGCCTTCCC
    TCCCACTAATAATTCTGAGG
    >Rosalind_5959
    CCATCGGTAGCGCATCCTTAGTCCAATTAAGTCCCTATCCAGGCGCTCCGCCGAAGGTCT
    ATATCCATTTGTCAGCAGACACGC
    >Rosalind_0808
    CCACCCTCGTGGTATGGCTAGGCATTCAGGAACCGGAGAACGCTTCAGACCAGCCCGGAC
    TGGGAACCTGCGGGCAGTAGGTGGAAT
    ```
    Sample Output
    ```
    Rosalind_0808
    60.91954022988506
    ```
    """
    contents = await file_to_fastas(file)
    max_name, max_gc = max_gc_dna(contents)
    return { "answer": f"{max_name}\n{max_gc * 100}"}


from ..rosalind.hamm import hamming_distance

@router.post("/hamm", summary="Hamming Distance", response_model=Answer, tags=[Tags.string_algorithm])
async def get_distance(file: UploadFile = File(...)):
    """
    Given: Two DNA strings s and t of equal length (not exceeding 1 kbp).

    Return: The Hamming distance dH(s,t).

    Sample Dataset
    ```
    GAGCCTACTAACGGGAT
    CATCGTAATGACGGCCT
    ```
    Sample Output
    ```
    7
    ```
    """
    contents = await file_to_str(file)
    split = split_str(contents, "\n", 2)
    return { "answer": hamming_distance(split[0], split[1])}


from ..rosalind.iprb import mendels_probability

@router.post("/iprb", 
    summary="Mendel's First Law Probability", 
    response_model=Answer,
    tags=[Tags.heredity, Tags.probability])
async def get_mendels_first(file: UploadFile=File(...)):
    """
    Given: Three positive integers k, m, and n, representing a population containing k+m+n organisms: k individuals are homozygous dominant for a factor, m are heterozygous, and n are homozygous recessive.

    Return: The probability that two randomly selected mating organisms will produce an individual possessing a dominant allele (and thus displaying the dominant phenotype). Assume that any two organisms can mate.

    Sample Dataset
    ```
    2 2 2
    ```
    Sample Output
    ```
    0.7833333333333333
    ```
    """
    contents = await file_to_str(file)
    nums = str_to_nums(contents, None, 3)
    prob = mendels_probability(nums[0], nums[1], nums[2])
    return {"answer": str(prob) }


from ..rosalind.prot import rna_to_protein

@router.post("/prot", summary="RNA to Protein", response_model=Answer, tags=[Tags.string_algorithm])
async def get_rna_to_protein(file: UploadFile=File(...)):
    """
    Given: An RNA string s corresponding to a strand of mRNA (of length at most 10 kbp).

    Return: The protein string encoded by s.

    Sample Dataset
    ```
    AUGGCCAUGGCGCCCAGAACUGAGAUCAAUAGUACCCGUAUUAACGGGUGA
    ```
    Sample Output
    ```
    MAMAPRTEINSTRING
    ```
    """
    contents = await file_to_str(file)
    raise_valid_rna(contents)
    return { "answer": rna_to_protein(contents)}



from ..rosalind.subs import find_motifs

@router.post("/subs", summary="Substring Locations", response_model=Answer, tags=[Tags.string_algorithm])
async def find_substring_positions(file: UploadFile=File(...)):
    """
    Given: Two DNA strings s and t (each of length at most 1 kbp).

    Return: All locations of t as a substring of s.

    Sample Dataset
    ```
    GATATATGCATATACTT
    ATAT
    ```
    Sample Output
    ```
    2 4 10
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n", 2)
    locations = find_motifs(lines[0], lines[1])
    return { "answer": " ".join(str(i) for i in locations)}

from ..rosalind.cons import find_consensus

@router.post("/cons", summary="Consensus and Profile", response_model=Answer, tags=[Tags.string_algorithm])
async def get_consensus_and_profile(file: UploadFile=File(...)):
    """
    Given: A collection of at most 10 DNA strings of equal length (at most 1 kbp) in FASTA format.

    Return: A consensus string and profile matrix for the collection. (If several possible consensus strings exist, then you may return any one of them.)

    Sample Dataset
    ```
    >Rosalind_1
    ATCCAGCT
    >Rosalind_2
    GGGCAACT
    >Rosalind_3
    ATGGATCT
    >Rosalind_4
    AAGCAACC
    >Rosalind_5
    TTGGAACT
    >Rosalind_6
    ATGCCATT
    >Rosalind_7
    ATGGCACT
    ```
    Sample Output
    ```
    ATGCAACT
    ```
    """
    contents = await file_to_str(file)
    fasta = fasta_to_sequence(contents)
    cons = find_consensus(fasta)
    return { "answer": cons}

from ..rosalind.fibd import mortal_fib

@router.post("/fibd", 
    summary="Mortal Fibonacci Rabbits", response_model=Answer,
    tags=[Tags.combinatorics, Tags.dynamic_programming])
async def mortal_fibonacci_rabbits(file: UploadFile=File(...)):
    """
    Given: Positive integers n≤100 and m≤20.

    Return: The total number of pairs of rabbits that will remain after the n-th month if all rabbits live for m months.

    Sample Dataset
    ```
    6 3
    ```
    Sample Output
    ```
    4
    ```
    """
    contents = await file_to_str(file)
    nums = str_to_nums(contents, None, 2)
    return { "answer": mortal_fib(nums[0], nums[1])}

from ..rosalind.grph import find_adjacent

@router.post("/grph", summary="Overlap Graph", response_model=Answer, tags=[Tags.graphs])
async def overlap_graphs(file: UploadFile=File(...)):
    """
    Given: A collection of DNA strings in FASTA format having total length at most 10 kbp.

    Return: The adjacency list corresponding to O3. You may return edges in any order.

    Sample Dataset
    ```
    >Rosalind_0498
    AAATAAA
    >Rosalind_2391
    AAATTTT
    >Rosalind_2323
    TTTTCCC
    >Rosalind_0442
    AAATCCC
    >Rosalind_5013
    GGGTGGG
    ```
    Sample Output
    ```
    Rosalind_0498 Rosalind_2391
    Rosalind_0498 Rosalind_0442
    Rosalind_2391 Rosalind_2323
    ```
    """
    contents = await file_to_str(file)
    fasta = fasta_to_sequence(contents)
    adjacent = find_adjacent(fasta)
    return { "answer": "\n".join(adjacent)}


from ..rosalind.iev import expected

@router.post("/iev", summary="Expected Offspring", response_model=Answer, tags=[Tags.heredity, Tags.probability])
async def calculate_expected_offspring(file: UploadFile=File(...)):
    """
    Given: Six nonnegative integers, each of which does not exceed 20,000. The integers correspond to the number of couples in a population possessing each genotype pairing for a given factor. In order, the six given integers represent the number of couples having the following genotypes:

    AA-AA

    AA-Aa

    AA-aa

    Aa-Aa

    Aa-aa

    aa-aa

    Return: The expected number of offspring displaying the dominant phenotype in the next generation, under the assumption that every couple has exactly two offspring.

    Sample Dataset
    ```
    1 0 0 1 0 1
    ```
    Sample Output
    ```
    3.5
    ```
    """
    contents = await file_to_str(file)
    people = str_to_nums(contents, None, 6)
    if any(i < 0 for i in people):
        raise HTTPException(status_code=500, detail="Only positive integers allowed")
    return {"answer": expected(people)}


from ..rosalind.lcsm import longest_common_substr

@router.post("/lcsm", summary="Shared Motifs", response_model=Answer, tags=[Tags.string_algorithm])
async def finding_shared_motif(file: UploadFile=File(...)):
    """
    Given: A collection of k (k≤100) DNA strings of length at most 1 kbp each in FASTA format.

    Return: A longest common substring of the collection. (If multiple solutions exist, you may return any single solution.)

    Sample Dataset
    ```
    >Rosalind_1
    GATTACA
    >Rosalind_2
    TAGACCA
    >Rosalind_3
    ATACA
    ```
    Sample Output
    ```
    TA
    ```
    """
    seqs = await file_to_seqs(file)
    longest = longest_common_substr(seqs)
    return {"answer": longest}

from ..rosalind.lia import independent_alleles

@router.post("/lia", summary="Independent Alleles Probability", response_model=Answer, tags=[Tags.heredity, Tags.probability])
async def find_independent_alleles(file: UploadFile=File(...)):
    """
    Given: Two positive integers k (k≤7) and N (N≤2k). In this problem, we begin with Tom, who in the 0th generation has genotype Aa Bb. Tom has two children in the 1st generation, each of whom has two children, and so on. Each organism always mates with an organism having genotype Aa Bb.

    Return: The probability that at least N Aa Bb organisms will belong to the k-th generation of Tom's family tree (don't count the Aa Bb mates at each level). Assume that Mendel's second law holds for the factors.

    Sample Dataset
    ```
    2 1
    ```
    Sample Output
    ```
    0.684
    ```
    """
    contents = await file_to_str(file)
    nums = str_to_nums(contents, expected=2)
    ind_alleles = independent_alleles(nums[0], nums[1])
    return { "answer": round(ind_alleles, 3)}

#from ..rosalind.mprt import get_all_motifs

@router.post("/mprt", summary="Protein Motifs -- NOT IMPLEMENTED", 
    response_model=NotImplemented,
    tags=[Tags.proteomics, Tags.file_formats])
async def find_protein_motif(file: UploadFile=File(...)):
    """
    ### Note

    __This endpoint is not implemented because it involves making a few requests
    to the UniProt Protein Database, and I don't want to hammer them.__

    Given: At most 15 UniProt Protein Database access IDs.

    Return: For each protein possessing the N-glycosylation motif, output its given access ID followed by a list of locations in the protein string where the motif can be found.

    Sample Dataset
    ```
    A2Z669
    B5ZC00
    P07204_TRBM_HUMAN
    P20840_SAG1_YEAST
    ```
    Sample Output
    ```
    B5ZC00
    85 118 142 306 395
    P07204_TRBM_HUMAN
    47 115 116 382 409
    P20840_SAG1_YEAST
    79 109 135 248 306 348 364 402 485 501 614
    ```
    """
    
    #contents = await file_to_str(file)
    #ids = contents.split()
    #if len(ids) > 15:
    #    raise HTTPException(status_code=500, detail=f"Expected less than 16 args, received {len(ids)}")
    #motifs = get_all_motifs(ids)
    #return {"answer": motifs}
    return { "error": "not implemented"}


from ..rosalind.mrna import infer_rna_combinations

@router.post("/mrna", summary="Infer mRNA from Protein", response_model=Answer, tags=[Tags.combinatorics])
async def infer_mrna_from_protein(file: UploadFile=File(...)):
    """
    Given: A protein string of length at most 1000 aa.

    Return: The total number of different RNA strings from which the protein could have been translated, modulo 1,000,000. (Don't neglect the importance of the stop codon in protein translation.)
    
    Sample Dataset
    ```
    MA
    ```
    Sample Output
    ```
    12
    ```
    """
    contents = await file_to_str(file)
    return {"answer": infer_rna_combinations(contents)}


from ..rosalind.perm import all_permutations

@router.post("/perm", response_model=Answer, tags=[Tags.gene_rearrange, Tags.combinatorics])
async def enumerate_gene_orders(file: UploadFile=File(...)):
    """
    Given: A positive integer n≤7.

    Return: The total number of permutations of length n, followed by a list of all such permutations (in any order).

    Sample Dataset
    ```
    3
    ```
    Sample Output
    ```
    6
    1 2 3
    1 3 2
    2 1 3
    2 3 1
    3 1 2
    3 2 1
    ```
    """
    contents = await file_to_str(file)
    num = str_to_nums(contents, expected=1)
    perms = all_permutations(num[0])
    ret_str = f"{len(perms)}"
    for i in perms:
        ret_str += f"\n{' '.join(str(j) for j in i)}"
    return { "answer": ret_str}

from ..rosalind.prtm import calc_mass

@router.post("/prtm", response_model=Answer, tags=[Tags.comp_mass_spec])
async def calculate_protein_mass(file: UploadFile=File(...)):
    """
    Given: A protein string P of length at most 1000 aa.

    Return: The total weight of P. Consult the monoisotopic mass table.

    Sample Dataset
    ```
    SKADYEK
    ```
    Sample Output
    ```
    821.3919199999999
    ```
    """
    contents = await file_to_str(file)
    mass = calc_mass(contents)
    return {"answer": mass }

from ..rosalind.revp import get_palindrom_locations

@router.post("/revp", response_model=Answer, tags=[Tags.string_algorithm])
async def locate_restriction_sites(file: UploadFile=File(...)):
    """
    Given: A DNA string of length at most 1 kbp in FASTA format.

    Return: The position and length of every reverse palindrome in the string having length between 4 and 12. You may return these pairs in any order.

    Sample Dataset
    ```
    >Rosalind_24
    TCAATGCATGCGGGTCTATATGCAT
    ```
    Sample Output
    ```
    4 6
    5 4
    6 6
    7 4
    17 4
    18 4
    20 6
    21 4
    ```
    """
    contents = await file_to_str(file)
    seqs = fasta_to_sequence(contents)
    values = list(seqs.values())
    assert_len(1, values)
    locations = get_palindrom_locations(values[0])
    locs = []
    for j in locations:
        locs.append(" ".join(str(i) for i in j))
    resp = "\n".join(locs)
    return {"answer": resp}


from ..rosalind.splc import remove_introns
from ..rosalind.prot import dna_to_protein

@router.post("/splc", summary="RNA Splicing", response_model=Answer, tags=[Tags.rna_splicing])
async def rna_splicing(file: UploadFile=File(...)):
    """
    Given: A DNA string s (of length at most 1 kbp) and a collection of substrings of s acting as introns. All strings are given in FASTA format.

    Return: A protein string resulting from transcribing and translating the exons of s. (Note: Only one solution will exist for the dataset provided.)

    Sample Dataset
    ```
    >Rosalind_10
    ATGGTCTACATAGCTGACAAACAGCACGTAGCAATCGGTCGAATCTCGAGAGGCATATGGTCACATGATCGGTCGAGCGTGTTTCAAAGTTTGCGCCTAG
    >Rosalind_12
    ATCGGTCGAA
    >Rosalind_15
    ATCGGTCGAGCGTGT
    ```
    Sample Output
    ```
    MVYIADKQHVASREAYGHMFKVCA
    ```
    """
    contents = await file_to_str(file)
    seqs = fasta_to_sequence(contents)
    items = list(seqs.values())
    if len(items) < 2:
        raise HTTPException(status_code=500, detail=f"Expected more than 1 sequence, received {len(items)}")

    DNA = remove_introns(items[0], items[1:])
    
    return {"answer": dna_to_protein(DNA) }


from ..rosalind.lexf import lex_product

@router.post("/lexf", summary="Enumerating k-mers Lexicographically", response_model=Answer, tags=[Tags.string_algorithm])
async def enumerating_kmers(file: UploadFile=File(...)):
    """
    Given: A collection of at most 10 symbols defining an ordered alphabet, and a positive integer n (n≤10).

    Return: All strings of length n that can be formed from the alphabet, ordered lexicographically (use the standard order of symbols in the English alphabet).

    Sample Dataset
    ```
    A C G T
    2
    ```
    Sample Output
    ```
    AA
    AC
    AG
    AT
    CA
    CC
    CG
    CT
    GA
    GC
    GG
    GT
    TA
    TC
    TG
    TT
    ```
    """
    contents = await file_to_str(file)
    lines = contents.split("\n")
    assert_len(2, lines)
    letters = lines[0].split()
    length = int(lines[1])
    prods = lex_product(letters, length)
    return {"answer": "\n".join("".join(i) for i in prods)}

from ..rosalind.orf import get_all_orfs

@router.post("/orf", response_model=Answer, tags=[Tags.combinatorics])
async def open_reading_frames(file: UploadFile=File(...)):
    """
    Given: A DNA string s of length at most 1 kbp in FASTA format.

    Return: Every distinct candidate protein string that can be translated from ORFs of s. Strings can be returned in any order.

    Sample Dataset
    ```
    >Rosalind_99
    AGCCATGTAGCTAACTCAGGTTACATGGGGATGACCCCGCGACTTGGATTAGAGTCTCTTTTGGAATAAGCCTGAATGATCCGAGTAGCATCTCAG
    ```
    Sample Output
    ```
    M
    MGMTPRLGLESLLE
    MLLGSFRLIPKETLIQVAGSSPCNLS
    MTPRLGLESLLE
    ```
    """
    contents = await file_to_str(file)
    seqs = fasta_to_sequence(contents)
    vals = list(seqs.values())
    assert_len(1, vals)
    orfs = get_all_orfs(vals[0])
    orf_list = [o for o in orfs]
    orf_list.sort()
    return {"answer": "\n".join(orf_list)}

from ..rosalind.lgis import subsequence

@router.post("/lgis", summary="Longest subsequences", 
    response_model=Answer, tags=[Tags.dynamic_programming, Tags.gene_rearrange])
async def longest_increasing_subsequence(file: UploadFile=File(...)):
    """
    Given: A positive integer n≤10000 followed by a permutation π of length n.

    Return: A longest increasing subsequence of π, followed by a longest decreasing subsequence of π.

    Sample Dataset
    ```
    5
    5 1 4 2 3
    ```
    Sample Output
    ```
    1 2 3
    5 4 3
    ```
    """
    contents = await file_to_str(file)
    lines = contents.split("\n")
    assert_len(2, lines)
    seq = [int(i) for i in lines[1].split()]
    increasing = subsequence(seq, increasing=True)
    decreasing = subsequence(seq, increasing=False)
    resp = " ".join(str(i) for i in increasing) + "\n"
    resp += " ".join(str(i) for i in decreasing)
    return {"answer": resp}

from ..rosalind.long import find_overlaps

@router.post("/long", response_model=Answer, tags=[Tags.genome_assembly])
async def genome_assembly(file: UploadFile=File(...)):
    """
    Given: At most 50 DNA strings of approximately equal length, not exceeding 1 kbp, in FASTA format (which represent reads deriving from the same strand of a single linear chromosome).

    The dataset is guaranteed to satisfy the following condition: there exists a unique way to reconstruct the entire chromosome from these reads by gluing together pairs of reads that overlap by more than half their length.

    Return: A shortest superstring containing all the given strings (thus corresponding to a reconstructed chromosome).

    Sample Dataset
    ```
    >Rosalind_56
    ATTAGACCTG
    >Rosalind_57
    CCTGCCGGAA
    >Rosalind_58
    AGACCTGCCG
    >Rosalind_59
    GCCGGAATAC
    ```
    Sample Output
    ```
    ATTAGACCTGCCGGAATAC
    ```
    """
    contents = await file_to_str(file)
    seqs = fasta_to_sequence(contents)
    result = find_overlaps([i for i in seqs.values()])
    return {"answer": result}

from ..rosalind.pmch import perfect_rna_match

@router.post("/pmch", summary="Perfect Matching RNA Structure", 
    response_model=Answer, tags=[Tags.combinatorics, Tags.dynamic_programming, Tags.string_algorithm])
async def perfect_matching_rna(file: UploadFile=File(...)):
    """
    Given: An RNA string s of length at most 80 bp having the same number of occurrences of 'A' as 'U' and the same number of occurrences of 'C' as 'G'.

    Return: The total possible number of perfect matchings of basepair edges in the bonding graph of s.

    Sample Dataset
    ```
    >Rosalind_23
    AGCUAGUCAU
    ```
    Sample Output
    ```
    12
    ```
    """
    contents = await file_to_str(file)
    seqs = fasta_to_sequence(contents)
    vals = list(seqs.values())
    assert_len(1, vals)
    rna = vals[0]
    raise_valid_rna(rna)
    return {"answer": perfect_rna_match(rna)}


from ..rosalind.pper import partial_permutations as pper

@router.post("/pper", response_model=Answer, tags=[Tags.combinatorics, Tags.gene_rearrange])
async def partial_permutations(file: UploadFile=File(...)):
    """
    Given: Positive integers n and k such that 100≥n>0 and 10≥k>0.

    Return: The total number of partial permutations P(n,k), modulo 1,000,000.

    Sample Dataset
    ```
    21 7
    ```
    Sample Output
    ```
    51200
    ```
    """
    contents = await file_to_str(file)
    nums = str_to_nums(contents, expected=2)
    parts = pper(nums[0], nums[1])
    return {"answer": parts}


from ..rosalind.prob import probability
@router.post("/prob", response_model=Answer, tags=[Tags.probability])
async def random_string_probability(file: UploadFile=File(...)):
    """
    Given: A DNA string s of length at most 100 bp and an array A containing at most 20 numbers between 0 and 1.

    Return: An array B having the same length as A in which B[k] represents the common logarithm of the probability that a random string constructed with the GC-content found in A[k] will match s exactly.

    Sample Dataset
    ```
    ACGATACAA
    0.129 0.287 0.423 0.476 0.641 0.742 0.783
    ```
    Sample Output
    ```
    -5.7373919000321045 -5.217087091662661 -5.2631939799163145 -5.360461380911991 -5.958129180949464 -6.628340009359368 -7.009226271710824
    ```
    """
    contents = await file_to_str(file)
    lines = contents.split("\n")
    assert_len(2, lines)
    seq = lines[0].strip()
    nums = [float(i) for i in lines[1].split()]
    gc_probs = probability(seq, nums)
    return {"answer": " ".join(str(i) for i in gc_probs)}

from ..rosalind.sign import signed_probs

@router.post("/sign", response_model=Answer, tags=[Tags.combinatorics, Tags.gene_rearrange])
async def enumerating_oriented_gene_orderings(file: UploadFile=File(...)):
    """
    Given: A positive integer n≤6.

    Return: The total number of signed permutations of length n, followed by a list of all such permutations (you may
     list the signed permutations in any order).

    Sample Dataset
    ```
    2
    ```
    Sample Output
    ```
    8
    1 2
    1 -2
    -1 2
    -1 -2
    2 1
    2 -1
    -2 1
    -2 -1
    ```
    """
    contents = await file_to_str(file)
    nums = str_to_nums(contents, None, 1)
    probs = signed_probs(nums[0])
    #probs.sort()
    resp = f"{len(probs)}\n"
    resp += "\n".join(" ".join(str(j) for j in i) for i in probs)
    return {"answer": resp}

from ..rosalind.sseq import splice_motifs

@router.post("/sseq", summary="Finding a Splice Motif", response_model=Answer,
     tags=[Tags.string_algorithm])
async def sseq_splice_motif(file: UploadFile=File(...)):
    """
    Given: Two DNA strings s and t (each of length at most 1 kbp) in FASTA format.

    Return: One collection of indices of s in which the symbols of t appear as a subsequence of s. If multiple solutions exist, you may return any one.

    Sample Dataset
    ```
    >Rosalind_14
    ACGTACGTGACG
    >Rosalind_18
    GTA
    ```
    Sample Output
    ```
    3 4 5
    ```
    """
    contents = await file_to_str(file)
    seqs = fasta_to_sequence(contents)
    values = list(seqs.values())
    assert_len(2, values)

    dna = values[0].strip()
    motif = values[1].strip()
    raise_valid_dna(dna)
    raise_valid_dna(dna)

    indices = splice_motifs(dna, motif)
    return {"answer": " ".join(str(i) for i in indices)}



from ..rosalind.tran import transition_transversion_count

@router.post("/tran", response_model=Answer, tags=[Tags.alignment])
async def transitions_and_transversions(file: UploadFile=File(...)):
    """
    Given: Two DNA strings s1 and s2 of equal length (at most 1 kbp).

    Return: The transition/transversion ratio R(s1,s2).

    Sample Dataset
    ```
    >Rosalind_0209
    GCAACGCACAACGAAAACCCTTAGGGACTGGATTATTTCGTGATCGTTGTAGTTATTGGA
    AGTACGGGCATCAACCCAGTT
    >Rosalind_2200
    TTATCTGACAAAGAAAGCCGTCAACGGCTGGATAATTTCGCGATCGTGCTGGTTACTGGC
    GGTACGAGTGTTCCTTTGGGT
    ```
    Sample Output
    ```
    1.2142857142857142
    ```
    """
    contents = await file_to_str(file)
    seqs = fasta_to_sequence(contents)

    values = list(seqs.values())
    assert_len(2, values)
    ition, version = transition_transversion_count(values[0], values[1])
    return {"answer": ition / version}

from ..rosalind.tree import build_tree

@router.post("/tree", summary="Completing a Tree",
     response_model=Answer, tags=[Tags.graphs, Tags.phylogeny])
async def completing_a_tree(file: UploadFile=File(...)):
    """
    Given: A positive integer n (n≤1000) and an adjacency list corresponding to a graph on n nodes that contains no cycles.

    Return: The minimum number of edges that can be added to the graph to produce a tree.

    Sample Dataset
    ```
    10
    1 2
    2 8
    4 10
    5 9
    6 10
    7 9
    ```
    Sample Output
    ```
    3
    ```
    """
    contents = await file_to_str(file)
    lines = contents.split("\n")
    length = try_parse_int(lines[0])
    edges = []
    for line in lines[1:]:
        sl = line.split()
        edges.append((try_parse_int(sl[0]), try_parse_int(sl[1])))
    # not needed. There are n-1 edges in a tree of length n
    # tree = build_tree(length, edges)
    needed = length - (len(edges) + 1)

    return {"answer":  needed}


from ..rosalind.cat import catalan

@router.post("/cat", response_model=Answer, summary="Catalan Numbers and RNA Secondary Structures",
     tags=[Tags.combinatorics, Tags.dynamic_programming, Tags.string_algorithm])
async def catalan_numbers(file: UploadFile=File(...)):
    """
    Given: An RNA string s having the same number of occurrences of 'A' as 'U' and the same number of occurrences of 'C' as 'G'. The length of the string is at most 300 bp.

    Return: The total number of noncrossing perfect matchings of basepair edges in the bonding graph of s, modulo 1,000,000.

    Sample Dataset
    ```
    >Rosalind_57
    AUAU
    ```
    Sample Output
    ```
    2
    ```
    """
    seqs = await file_to_seqs(file, expected=1)
    count = catalan(seqs[0])
    return {"answer": str(count)}


from ..rosalind.corr import corrections

@router.post("/corr", summary="Error Correction in Reads", tags=[Tags.genome_assembly])
async def error_correction_in_reads(file: UploadFile=File(...)):
    """
    Given: A collection of up to 1000 reads of equal length (at most 50 bp) in FASTA format. 
    Some of these reads were generated with a single-nucleotide error. For each read s in the dataset, 
    one of the following applies:

    - s was correctly sequenced and appears in the dataset at least twice (possibly as a reverse complement);
    - s is incorrect, it appears in the dataset exactly once, and its Hamming distance is 1 with respect to exactly one correct read in the dataset (or its reverse complement).
    
    Return: A list of all corrections in the form "[old read]->[new read]". 
    (Each correction must be a single symbol substitution, and you may return the corrections in any order.)

    Sample Dataset
    ```
    >Rosalind_52
    TCATC
    >Rosalind_44
    TTCAT
    >Rosalind_68
    TCATC
    >Rosalind_28
    TGAAA
    >Rosalind_95
    GAGGA
    >Rosalind_66
    TTTCA
    >Rosalind_33
    ATCAA
    >Rosalind_21
    TTGAT
    >Rosalind_18
    TTTCC
    ```
    Sample Output
    ```
    GAGGA->GATGA
    TTCAT->TTGAT
    TTTCC->TTTCA
    ```
    """
    contents = await file_to_str(file)
    seqs = fasta_to_sequence(contents)
    corrs = corrections(list(seqs.values()))
    corrs.sort()
    ret = "\n".join(("->".join((c) for c in l) for l in corrs))
    return {"answer": ret}


from ..rosalind.inod import internal_nodes

@router.post("/inod", response_model=Answer, tags=[Tags.combinatorics, Tags.phylogeny])
async def counting_phylogenetic_ancestors(file: UploadFile=File(...)):
    """
    Given: A positive integer n (3≤n≤10000).

    Return: The number of internal nodes of any unrooted binary tree having n leaves.

    Sample Dataset
    ```
    4
    ```
    Sample Output
    ```
    2
    ```
    """
    contents = await file_to_str(file)
    nums = str_to_nums(contents, expected=1)
    nodes = internal_nodes(nums[0])
    return {"answer": nodes}

from ..rosalind.kmer import kmer_array

@router.post("/kmer", summary="k-mer Composition", response_model=Answer, tags=[Tags.string_algorithm])
async def kmer_composition(file: UploadFile=File(...)):
    """
    Given: A DNA string s in FASTA format (having length at most 100 kbp).

    Return: The 4-mer composition of s.

    Sample Dataset
    ```
    >Rosalind_6431
    CTTCGAAAGTTTGGGCCGAGTCTTACAGTCGGTCTTGAAGCAAAGTAACGAACTCCACGG
    CCCTGACTACCGAACCAGTTGTGAGTACTCAACTGGGTGAGAGTGCAGTCCCTATTGAGT
    TTCCGAGACTCACCGGGATTTTCGATCCAGCCTCAGTCCAGTCTTGTGGCCAACTCACCA
    AATGACGTTGGAATATCCCTGTCTAGCTCACGCAGTACTTAGTAAGAGGTCGCTGCAGCG
    GGGCAAGGAGATCGGAAAATGTGCTCTATATGCGACTAAAGCTCCTAACTTACACGTAGA
    CTTGCCCGTGTTAAAAACTCGGCTCACATGCTGTCTGCGGCTGGCTGTATACAGTATCTA
    CCTAATACCCTTCAGTTCGCCGCACAAAAGCTGGGAGTTACCGCGGAAATCACAG
    ```
    Sample Output
    ```
    4 1 4 3 0 1 1 5 1 3 1 2 2 1 2 0 1 1 3 1 2 1 3 1 1 1 1 2 2 5 1 3 0 2 2 1 1 1 1 3 1 0 0 1 5 5 1 5 0 2 0 2 1 2 1 1 1 2 0 1 0 0 1 1 3 2 1 0 3 2 3 0 0 2 0 8 0 0 1 0 2 1 3 0 0 0 1 4 3 2 1 1 3 1 2 1 3 1 2 1 2 1 1 1 2 3 2 1 1 0 1 1 3 2 1 2 6 2 1 1 1 2 3 3 3 2 3 0 3 2 1 1 0 0 1 4 3 0 1 5 0 2 0 1 2 1 3 0 1 2 2 1 1 0 3 0 0 4 5 0 3 0 2 1 1 3 0 3 2 2 1 1 0 2 1 0 2 2 1 2 0 2 2 5 2 2 1 1 2 1 2 2 2 2 1 1 3 4 0 2 1 1 0 1 2 2 1 1 1 5 2 0 3 2 1 1 2 2 3 0 3 0 1 3 1 2 3 0 2 1 2 2 1 2 3 0 1 2 3 1 1 3 1 0 1 1 3 0 2 1 2 2 0 2 1 1
    ```
    """
    contents = await file_to_str(file)    
    seqs = fasta_to_sequence(contents)
    values = list(seqs.values())
    assert_len(1, values)
    arr = kmer_array(values[0], 4)
    return {"answer": " ".join(str(i) for i in arr)}


from ..rosalind.kmp import kmp_failure

@router.post("/kmp", response_model=Answer, tags=[Tags.string_algorithm])
async def speeding_up_motif_finding(file: UploadFile=File(...)):
    """
    Given: A DNA string s (of length at most 100 kbp) in FASTA format.

    Return: The failure array of s.

    Sample Dataset
    ```
    >Rosalind_87
    CAGCATGGTATCACAGCAGAG
    ```
    Sample Output
    ```
    0 0 0 1 2 0 0 0 0 0 0 1 2 1 2 3 4 5 3 0 0
    ```
    """
    contents = await file_to_str(file)
    seqs = fasta_to_sequence(contents)
    values = list(seqs.values())
    assert_len(1, values)
    fails = kmp_failure(values[0])
    return {"answer": " ".join(str(i) for i in fails)}

from ..rosalind.lcsq import longest_subsequence

@router.post("/lcsq", response_model=Answer, tags=[Tags.string_algorithm, Tags.dynamic_programming])
async def longest_common_subsequence(file: UploadFile=File(...)):
    """
    Given: Two DNA strings s and t (each having length at most 1 kbp) in FASTA format.

    Return: A longest common subsequence of s and t. (If more than one solution exists, you may return any one.)

    Sample Dataset
    ```
    >Rosalind_23
    AACCTTGG
    >Rosalind_64
    ACACTGTGA
    ```
    Sample Output
    ```
    AACTTG
    ```
    """
    contents = await file_to_str(file)
    seqs = fasta_to_sequence(contents)
    values = list(seqs.values())
    assert_len(2, values)
    longest = longest_subsequence(values[0], values[1])
    return {"answer": longest}
    
from ..rosalind.lexv import lex_permutations

@router.post("/lexv", response_model=Answer, tags=[Tags.string_algorithm])
async def ordering_different_lengths_lexicographically(file: UploadFile=File(...)):
    """
    Given: A permutation of at most 12 symbols defining an ordered alphabet A and a positive integer n (n≤4).

    Return: All strings of length at most n formed from A, ordered lexicographically. (Note: As in “Enumerating k-mers Lexicographically”, alphabet order is based on the order in which the symbols are given.)

    Sample Dataset
    ```
    D N A
    3
    ```
    Sample Output
    ```
    D
    DD
    DDD
    DDN
    DDA
    DN
    DND
    DNN
    DNA
    DA
    DAD
    DAN
    DAA
    N
    ND
    NDD
    NDN
    NDA
    NN
    NND
    NNN
    NNA
    NA
    NAD
    NAN
    NAA
    A
    AD
    ADD
    ADN
    ADA
    AN
    AND
    ANN
    ANA
    AA
    AAD
    AAN
    AAA
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n", 2)
    alphabet = lines[0].split()
    length = int(lines[1])

    prods = lex_permutations(alphabet, length, to_sort=True)
    return {"answer": "\n".join(prods)}


from ..rosalind.mmch import num_matches

@router.post("/mmch", summary="Maximum Matchings and RNA Secondary Structure",
     response_model=Answer, tags=[Tags.string_algorithm, Tags.combinatorics, Tags.dynamic_programming])
async def maximum_matchings(file: UploadFile=File(...)):
    """
    Given: An RNA string s of length at most 100.

    Return: The total possible number of maximum matchings of basepair edges in the bonding graph of s.

    Sample Dataset
    ```
    >Rosalind_92
    AUGCUUC
    ```
    Sample Output
    ```
    6
    ```
    """
    seqs = await file_to_seqs(file, expected=1)
    matches = num_matches(seqs[0])
    return { "answer" : str(matches)}

from ..rosalind.pdst import p_distance

@router.post("/pdst", summary="p-Distance Matrix", response_model=Answer,
    tags=[Tags.phylogeny, Tags.alignment])
async def p_distance_matrix(file: UploadFile=File(...)):
    """
    Given: A collection of n (n≤10) DNA strings s1,…,sn of equal length (at most 1 kbp). Strings are given in FASTA format.

    Return: The matrix D corresponding to the p-distance dp on the given strings. As always, note that your answer is allowed an absolute error of 0.001.

    Sample Dataset
    ```
    >Rosalind_9499
    TTTCCATTTA
    >Rosalind_0942
    GATTCATTTC
    >Rosalind_6568
    TTTCCATTTT
    >Rosalind_1833
    GTTCCATTTA
    ```
    Sample Output
    ```
    0.0 0.4 0.1 0.1
    0.4 0.0 0.4 0.3
    0.1 0.4 0.0 0.2
    0.1 0.3 0.2 0.0
    ```
    """
    seqs = await file_to_seqs(file)
    matrix = p_distance(seqs)
    to_return = "\n".join(" ".join(str(f) for f in r) for r in matrix)
    return {"answer": to_return}

'''
from ..rosalind.rear import transform, num_breakpoints

@router.post("/rear", response_model=Answer, tags=[Tags.combinatorics, Tags.gene_rearrange])
async def reversal_distance(file: UploadFile=File(...)):
    """
    Given: A collection of at most 5 pairs of permutations, all of which have length 10.

    Return: The reversal distance between each permutation pair.

    Sample Dataset
    ```
    1 2 3 4 5 6 7 8 9 10
    3 1 5 2 7 4 9 6 10 8

    3 10 8 2 5 4 7 1 6 9
    5 2 3 1 7 4 10 8 6 9

    8 6 7 9 4 1 3 10 2 5
    8 2 7 6 9 1 5 3 10 4

    3 9 10 4 1 8 6 7 5 2
    2 9 8 5 1 7 3 4 6 10

    1 2 3 4 5 6 7 8 9 10
    1 2 3 4 5 6 7 8 9 10
    ```
    Sample Output
    ```
    9 4 5 7 0
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n")
    lines = [l for l in lines if not l.isspace() and not l == ""]


    values = []

    for i in range(0, len(lines), 2):
        if i + 1 == len(lines):
            raise HTTPException(status_code=500, detail="Expected an even number of lines")
        l1 = [try_parse_int(j) for j in lines[i].split()]
        l2 = [try_parse_int(j) for j in lines[i + 1].split()]
        result = transform(l1, l2)
        bp = num_breakpoints(l2)
        values.append(bp)

    return {"answer": " ".join(str(i) for i in values)}
'''

from ..rosalind.rstr import probability as rstr_prob

@router.post("/rstr", response_model=Answer, tags=[Tags.probability])
async def matching_random_motifs(file: UploadFile=File(...)):
    """
    Given: A positive integer N≤100000, a number x between 0 and 1, and a DNA string s of length at most 10 bp.

    Return: The probability that if N random DNA strings having the same length as s are constructed with GC-content x (see “Introduction to Random Strings”), then at least one of the strings equals s. We allow for the same random string to be created more than once.

    Sample Dataset
    ```
    90000 0.6
    ATAGCCGA
    ```
    Sample Output
    ```
    0.6885160784606543
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n", 2)
    seq = lines[1]
    nums = lines[0].split()
    assert_len(2, nums)
    length = int(nums[0])
    x = float(nums[1])
    prob = rstr_prob(length, seq, x)

    return { "answer" : prob}

from ..rosalind.sset import number_of_subsets

@router.post("/sset", response_model=Answer, tags=[Tags.combinatorics, Tags.set_theory])
async def subsets(file: UploadFile=File(...)):
    """
    Given: A positive integer n (n≤1000).

    Return: The total number of subsets of {1,2,…,n} modulo 1,000,000.

    Sample Dataset
    ```
    3
    ```
    Sample Output
    ```
    8
    ```
    """
    contents = await file_to_str(file)
    nums = str_to_nums(contents, expected=1)

    nsset = number_of_subsets(nums[0])
    return {"answer": nsset}

from ..rosalind.aspc import alt_splicing

@router.post("/aspc", response_model=Answer, tags=[Tags.combinatorics])
async def alternative_splicing(file: UploadFile=File(...)):
    """
    Given: Positive integers n and m with 0≤m≤n≤2000.

    Return: The sum of combinations C(n,k) for all k satisfying m≤k≤n, modulo 1,000,000. In shorthand, ∑nk=m(nk).

    Sample Dataset
    ```
    6 3
    ```
    Sample Output
    ```
    42
    ```
    """
    contents = await file_to_str(file)
    nums = str_to_nums(contents, None, 2)
    return { "answer": alt_splicing(nums[0], nums[1])}


from ..rosalind.edit import levenshtein_distance

@router.post("/edit", response_model=Answer, tags=[Tags.alignment, Tags.dynamic_programming])
async def edit_distance(file: UploadFile=File(...)):
    """
    Given: Two protein strings s and t in FASTA format (each of length at most 1000 aa).

    Return: The edit distance dE(s,t).

    Sample Dataset
    ```
    >Rosalind_39
    PLEASANTLY
    >Rosalind_11
    MEANLY
    ```
    Sample Output
    ```
    5
    ```
    """
    seqs = await file_to_seqs(file, 2)
    dist = levenshtein_distance(seqs[0], seqs[1])
    return {"answer": dist}

from ..rosalind.eval import expected_sites

@router.post("/eval", response_model=Answer, tags=[Tags.probability])
async def expected_restriction_sites(file: UploadFile=File(...)):
    """
    Given: A positive integer n (n≤1,000,000), a DNA string s of even length at most 10, and an array A of length at most 20,
    containing numbers between 0 and 1.

    Return: An array B having the same length as A in which B[i] represents the expected number 
    of times that s will appear as a substring of a random DNA string t of length n, where t is formed
    with GC-content A[i] (see “Introduction to Random Strings”).

    Sample Dataset
    ```
    10
    AG
    0.25 0.5 0.75
    ```
    Sample Output
    ```
    0.4219 0.5625 0.4219
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n", 3)
    n = int(lines[0])
    seq = lines[1].strip()
    gc_contents = [float(i) for i in lines[2].split()]
    expected = expected_sites(n, seq, gc_contents)
    return {"answer": " ".join(str(round(i, 4)) for i in expected)}

@router.post("/motz", response_model=Answer,
    tags=[Tags.combinatorics, Tags.dynamic_programming, Tags.string_algorithm])
async def motzkin_numbers(file: UploadFile=File(...)):
    """
    Given: An RNA string s of length at most 300 bp.

    Return: The total number of noncrossing matchings of basepair edges in the bonding graph of s, modulo 1,000,000.

    Sample Dataset
    ```
    >Rosalind_57
    AUAU
    ```
    Sample Output
    ```
    7
    ```
    """

    return { "answer": "" }

from ..rosalind.nwck import parse_newick, distance

@router.post("/nwck", response_model=Answer, tags=[Tags.phylogeny])
async def distances_in_trees(file: UploadFile=File(...)):
    """
    Given: A collection of n trees (n≤40) in Newick format, with each tree containing at most 200 nodes; each tree Tk is followed by a pair of nodes xk and yk in Tk.

    Return: A collection of n positive integers, for which the kth integer represents the distance between xk and yk in Tk.

    Sample Dataset
    ```
    (cat)dog;
    dog cat

    (dog,cat);
    dog cat
    ```
    Sample Output
    ```
    1 2
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n")
    lines = [l for l in lines if not l.isspace() and not l == ""]
    dists = []
    for i in range(0, len(lines), 2):
        if i + 1 == len(lines):
            raise HTTPException(status_code=500, detail="Odd number of lines")
        newick = lines[i].strip()
        nodes = split_str(lines[i + 1], expected=2)
        root = parse_newick(newick)
        dist = distance(root, nodes[0], nodes[1])
        dists.append(dist)
    return {"answer" : " ".join(str(d) for d in dists)}


from ..rosalind.scsp import shortest_common_supersequence

@router.post("/scsp", response_model=Answer, tags=[Tags.string_algorithm, Tags.dynamic_programming])
async def interleaving_two_motifs(file: UploadFile=File(...)):
    """
    Given: Two DNA strings s and t.

    Return: A shortest common supersequence of s and t. If multiple solutions exist, you may output any one.

    Sample Dataset
    ```
    ATCTGAT
    TGCATA
    ```
    Sample Output
    ```
    ATGCATGAT
    ```
    """
    contents = await file_to_str(file)
    seqs = split_str(contents, "\n", 2)
    superstring = shortest_common_supersequence(seqs[0].strip(), seqs[1].strip())
    return { "answer": superstring}

from ..rosalind.seto import set_operations as seto_ops

@router.post("/seto", response_model=Answer, tags=[Tags.set_theory])
async def set_operations(file: UploadFile=File(...)):
    """
    Given: A positive integer n (n≤20,000) and two subsets A and B of {1,2,…,n}.

    Return: Six sets: A∪B, A∩B, A−B, B−A, Ac, and Bc (where set complements are taken with respect to {1,2,…,n}).

    Sample Dataset
    ```
    10
    {1, 2, 3, 4, 5}
    {2, 8, 5, 10}
    ```
    Sample Output
    ```
    {1, 2, 3, 4, 5, 8, 10}
    {2, 5}
    {1, 3, 4}
    {8, 10}
    {6, 7, 8, 9, 10}
    {1, 3, 4, 6, 7, 9}
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n", 3)
    size = str_to_nums(lines[0], None, 1)[0]
    line1 = re.sub("{|}|,", " ", lines[1])
    line2 = re.sub("{|}|,", " ", lines[2])
    set1 = set(int(i) for i in line1.split())
    set2 = set(int(i) for i in line2.split())
    sets = seto_ops(size, set1, set2)
    set_str = []
    for s in sets:
        line = f"{{{', '.join(str(i) for i in s)}}}"
        set_str.append(line)
    return {"answer": "\n".join(set_str)}

@router.post("/sort", response_model=Answer, tags=[Tags.combinatorics, Tags.gene_rearrange])
async def sorting_by_reversals(file: UploadFile=File(...)):
    """
    Given: Two permutations π and γ, each of length 10.

    Return: The reversal distance drev(π,γ), followed by a collection of reversals sorting π into γ. If multiple collections of such reversals exist, you may return any one.

    Sample Dataset
    ```
    1 2 3 4 5 6 7 8 9 10
    1 8 9 3 2 7 6 5 4 10
    ```
    Sample Output
    ```
    2
    4 9
    2 5
    ```
    """

    return {"answer": ""}


from ..rosalind.spec import calc_protein

@router.post("/spec", response_model=Answer, tags=[Tags.comp_mass_spec])
async def infering_protein_from_spectrum(file: UploadFile=File(...)):
    """
    Given: A list L of n (n≤100) positive real numbers.

    Return: A protein string of length n−1 whose prefix spectrum is equal to L (if multiple solutions exist, you may output any one of them). Consult the monoisotopic mass table.

    Sample Dataset
    ```
    3524.8542
    3710.9335
    3841.974
    3970.0326
    4057.0646
    ```
    Sample Output
    ```
    WMQS
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n")
    prefix_spectrum = [try_parse_float(i) for i in lines]
    protein = calc_protein(prefix_spectrum)
    return {"answer": protein}

from ..rosalind.trie import build_trie

@router.post("/trie", response_model=Answer, tags=[Tags.graphs, Tags.string_algorithm])
async def intro_to_pattern_matching(file: UploadFile=File(...)):
    """
    Given: A list of at most 100 DNA strings of length at most 100 bp, none of which is a prefix of another.

    Return: The adjacency list corresponding to the trie T for these patterns, in the following format. If T has n nodes, first label the root with 1 and then label the remaining nodes with the integers 2 through n in any order you like. Each edge of the adjacency list of T will be encoded by a triple containing the integer representing the edge's parent node, followed by the integer representing the edge's child node, and finally the symbol labeling the edge.

    Sample Dataset
    ```
    ATAGA
    ATC
    GAT
    ```
    Sample Output
    ```
    1 2 A
    2 3 T
    3 4 A
    4 5 G
    5 6 A
    3 7 C
    1 8 G
    8 9 A
    9 10 T
    ```
    """
    contents = await file_to_str(file)
    lines = [l.strip() for l in split_str(contents, "\n")]
    trie_lines =  build_trie(lines)
    return {"answer": "\n".join(i for i in trie_lines)}

from ..rosalind.conv import convolution

@router.post("/conv", response_model=Answer, 
    summary="Comparing Spectra with the spectral Convolution", 
    tags=[Tags.comp_mass_spec])
async def comparing_spectra(file: UploadFile=File(...)):
    """
    Given: Two multisets of positive real numbers S1 and S2. The size of each multiset is at most 200.

    Return: The largest multiplicity of S1⊖S2, as well as the absolute value of the number x maximizing (S1⊖S2)(x) (you may return any such value if multiple solutions exist).

    Sample Dataset
    ```
    186.07931 287.12699 548.20532 580.18077 681.22845 706.27446 782.27613 968.35544 968.35544
    101.04768 158.06914 202.09536 318.09979 419.14747 463.17369
    ```
    Sample Output
    ```
    3
    85.03163
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n", 2)
    s1 = set(float(i) for i in lines[0].split())
    s2 = set(float(i) for i in lines[1].split())
    largest, value = convolution(s1, s2)

    return {"answer": f"{largest}\n{round(value, 5)}"}

from ..rosalind.ctbl import build_character_table

@router.post("/ctbl", response_model=Answer, tags=[Tags.phylogeny])
async def creating_character_table(file: UploadFile=File(...)):
    """
    Given: An unrooted binary tree T in Newick format for at most 200 species taxa.

    Return: A character table having the same splits as the edge splits of T. 
    The columns of the character table should encode the taxa ordered lexicographically; 
    the rows of the character table may be given in any order. Also, for any 
    given character, the particular subset of taxa to which 1s are assigned is arbitrary.

    Sample Dataset
    ```
    (dog,((elephant,mouse),robot),cat);
    ```
    Sample Output
    ```
    00110
    00111
    ```
    """
    contents = await file_to_str(file)

    return {"answer": ""}


from ..rosalind.edta import edta
@router.post("/edta", response_model=Answer, tags=[Tags.alignment, Tags.dynamic_programming])
async def edit_distance_alignment(file: UploadFile=File(...)):
    """
    Given: Two protein strings s and t in FASTA format (with each string having length at most 1000 aa).

    Return: The edit distance dE(s,t) followed by two augmented strings s′ and t′ representing an optimal alignment of s and t.

    Sample Dataset
    ```
    >Rosalind_43
    PRETTY
    >Rosalind_97
    PRTTEIN
    ```
    Sample Output
    ```
    4
    PRET-TY
    PRTTEIN
    ```
    Rosalind has this as the sample output, but it is wrong:
    4
    PRETTY--
    PR-TTEIN
    """
    seqs = await file_to_seqs(file, 2)
    num, s1, s2 = edta(seqs[0], seqs[1])
    return { "answer": f"{num}\n{s1}\n{s2}"}

from ..rosalind.full import infer_from_full

@router.post("/full", response_model=Answer, 
    summary="Infering Peptide from a Full Spectrum",
    tags=[Tags.comp_mass_spec])
async def full_spectrum(file: UploadFile=File(...)):
    """
    Given: A list L containing 2n+3 positive real numbers (n≤100). 
    The first number in L is the parent mass of a peptide P, 
    and all other numbers represent the masses of some b-ions and y-ions of 
    P (in no particular order). You may assume that if the mass of a b-ion is present, 
    then so is that of its complementary y-ion, and vice-versa.

    Return: A protein string t of length n for which there exist two positive real numbers w1 and w2 such that for every prefix p and suffix s of t, each of w(p)+w1 and w(s)+w2 is equal to an element of L. (In other words, there exists a protein string whose t-prefix and t-suffix weights correspond to the non-parent mass values of L.) If multiple solutions exist, you may output any one.

    Sample Dataset
    ```
    1988.21104821
    610.391039105
    738.485999105
    766.492149105
    863.544909105
    867.528589105
    992.587499105
    995.623549105
    1120.6824591
    1124.6661391
    1221.7188991
    1249.7250491
    1377.8200091
    ```
    Sample Output
    ```
    KEKEP
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n")
    parent = try_parse_float(lines[0])
    ions = [try_parse_float(i) for i in lines[1:]]
    n = (len(ions) - 2) // 2
    full = infer_from_full(parent, ions)
    return {"answer": full}

from ..rosalind.indc import ind_segregation

@router.post("/indc", response_model=Answer, tags=[Tags.heredity, Tags.probability])
async def independent_segregation(file: UploadFile=File(...)):
    """
    Given: A positive integer n≤50.

    Return: An array A of length 2n in which A[k] represents the common logarithm of the probability 
    that two diploid siblings share at least k of their 2n chromosomes (we do not consider recombination for now).

    Sample Dataset
    ```
    5
    ```
    Sample Output
    ```
    -0.000 -0.005 -0.024 -0.082 -0.205 -0.424 -0.765 -1.262 -1.969 -3.010
    ```
    """
    contents = await file_to_str(file)
    n = try_parse_int(contents)
    ind_seg = ind_segregation(n)
    return {"answer": " ".join(str(i) for i in ind_seg)}

@router.post("/itwv", summary="Finding Disjoint Motifs in a Gene", 
    response_model=Answer, tags=[Tags.dynamic_programming, Tags.string_algorithm])
async def finding_disjointed_motifs(file: UploadFile=File(...)):
    """
    Given: A text DNA string s of length at most 10 kbp, followed by a collection of n (n≤10) DNA strings of length at most 10 bp acting as patterns.

    Return: An n×n matrix M for which Mj,k=1 if the jth and kth pattern strings can be interwoven into s and Mj,k=0 otherwise.

    Sample Dataset
    ```
    GACCACGGTT
    ACAG
    GT
    CCG
    ```
    Sample Output
    ```
    0 0 1
    0 1 0
    1 0 0
    ```
    """

    return {"answer": ""}

from ..rosalind.afrq import recessive_probability

@router.post("/afrq", response_model=Answer, tags=[Tags.pop_dynamics, Tags.probability])
async def counting_disease_carriers(file: UploadFile=File(...)):
    """
    Given: An array A for which A[k] represents the proportion of homozygous 
    recessive individuals for the k-th Mendelian factor in a diploid population. 
    Assume that the population is in genetic equilibrium for all factors.

    Return: An array B having the same length as A in which B[k] 
    represents the probability that a randomly selected individual 
    carries at least one copy of the recessive allele for the k-th factor.

    Sample Dataset
    ```
    0.1 0.25 0.5
    ```
    Sample Output
    ```
    0.532 0.75 0.914
    ```
    """
    contents = await file_to_str(file)
    pop = [try_parse_float(i) for i in contents.split()]
    one_copy = recessive_probability(pop)

    return {"answer": " ".join(str(i) for i in one_copy)}

from ..rosalind.cstr import build_char_table

@router.post("/cstr", summary="Creating a character table from genetic strings",
    tags=[Tags.phylogeny], response_model=Answer)
async def create_char_table(file: UploadFile=File(...)):
    """
    Given: A collection of at most 100 characterizable DNA strings, each of 
    length at most 300 bp.

    Return: A character table for which each nontrivial character 
    encodes the symbol choice at a single position of the strings. 
    (Note: the choice of assigning '1' and '0' to the two states 
    of each SNP in the strings is arbitrary.)

    Sample Dataset
    ```
    ATGCTACC
    CGTTTACC
    ATTCGACC
    AGTCTCCC
    CGTCTATC
    ```
    Sample Output
    ```
    10110
    10100
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n", to_strip=True)
    rows = build_char_table(lines)

    return {"answer": "\n".join(rows)}


@router.post("/prsm", response_model=Answer, tags=[Tags.comp_mass_spec])
async def matching_spectrum_to_protein(file: UploadFile=File(...)):
    """
    Given: A positive integer n followed by a collection of n 
    protein strings s1, s2, ..., sn and a multiset R of positive
    numbers (corresponding to the complete spectrum of some unknown protein string).

    Return: The maximum multiplicity of R⊖S[sk] taken over all strings 
    sk, followed by the string sk for which this maximum multiplicity 
    occurs (you may output any such value if multiple solutions exist).

    Sample Dataset
    ```
    4
    GSDMQS
    VWICN
    IASWMQS
    PVSMGAD
    445.17838
    115.02694
    186.07931
    314.13789
    317.1198
    215.09061
    ```
    Sample Output
    ```
    3
    IASWMQS
    ```
    """

    return {"answer": ""}

from ..rosalind.dbru import de_bruin_graph

@router.post("/dbru", response_model=Answer, tags=[Tags.genome_assembly])
async def building_de_bruijn_graph(file: UploadFile=File(...)):
    """
    Given: A collection of up to 1000 (possibly repeating) DNA strings of equal length 
    (not exceeding 50 bp) corresponding to a set S of (k+1)-mers.

    Return: The adjacency list corresponding to the de Bruijn graph corresponding to S∪Src.

    Sample Dataset
    ```
    TGAT
    CATG
    TCAT
    ATGC
    CATC
    CATC
    ```
    Sample Output
    ```
    (ATC, TCA)
    (ATG, TGA)
    (ATG, TGC)
    (CAT, ATC)
    (CAT, ATG)
    (GAT, ATG)
    (GCA, CAT)
    (TCA, CAT)
    (TGA, GAT)
    ```
    """
    contents = await file_to_str(file)
    lines = [l.strip() for l in split_str(contents, "\n")]
    adj_list = de_bruin_graph(lines)

    to_return = "\n".join(f"({i}, {j})" for i, j in adj_list)

    return {"answer": to_return}

from ..rosalind.nkew import parse_return_distance

@router.post("/nkew", response_model=Answer, tags=[Tags.phylogeny])
async def newick_format_with_edge_weights(file: UploadFile=File(...)):
    """
    Given: A collection of n weighted trees (n≤40) in Newick format, with each tree 
    containing at most 200 nodes; each tree Tk is followed by a pair of nodes 
    xk and yk in Tk.

    Return: A collection of n numbers, for which the kth number represents the 
    distance between xk and yk in Tk.

    Sample Dataset
    ```
    (dog:42,cat:33);
    cat dog

    ((dog:4,cat:3):74,robot:98,elephant:58);
    dog elephant
    ```
    Sample Output
    ```
    75 136
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, "\n")
    lines = [l for l in lines if not l.isspace() and not l == ""]
    dists = []
    for i in range(0, len(lines), 2):
        if i + 1 == len(lines):
            raise HTTPException(status_code=500, detail="Odd number of lines")
        newick = lines[i].strip()
        nodes = split_str(lines[i + 1], expected=2)
        dist = parse_return_distance(newick, nodes[0], nodes[1])
        dists.append(dist)
    return {"answer": " ".join(str(i) for i in dists)}


@router.post("/sgra", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    Given: A list L (of length at most 100) containing positive real numbers.

    Return: The longest protein string that matches the spectrum graph of L (if multiple solutions exist, you may output any one of them). Consult the monoisotopic mass table.

    Sample Dataset
    ```
    3524.8542
    3623.5245
    3710.9335
    3841.974
    3929.00603
    3970.0326
    4026.05879
    4057.0646
    4083.08025
    ```
    Sample Output
    ```
    WMSPG
    ```
    """
    contents = await file_to_str(file)

    return {"answer": ""}


from ..rosalind.mend import parse_get_probs

@router.post("/mend", response_model=Answer, 
    tags=[Tags.heredity, Tags.phylogeny, Tags.probability])
async def infering_genotype_from_pedigree(file: UploadFile=File(...)):
    """
    Given: A rooted binary tree T in Newick format encoding an 
    individual's pedigree for a Mendelian factor whose alleles are 
    A (dominant) and a (recessive).

    Return: Three numbers between 0 and 1, corresponding to 
    the respective probabilities that the individual at the 
    root of T will exhibit the "AA", "Aa" and "aa" genotypes.

    Sample Dataset
    ```
    ((((Aa,aa),(Aa,Aa)),((aa,aa),(aa,AA))),Aa);
    ```
    Sample Output
    ```
    0.156 0.5 0.344
    ```
    """
    contents = await file_to_str(file)
    probs = parse_get_probs(contents)
    return {"answer": " ".join(str(i) for i in probs)}


from ..rosalind.sexl import sex_inheritance

@router.post("/sexl", response_model=Answer, summary="Sex-Linked Inheritance",
    tags=[Tags.heredity, Tags.probability])
async def sex_linked_inheritance(file: UploadFile=File(...)):
    """
    Given: An array A of length n for which A[k] represents the proportion 
    of males in a population exhibiting the k-th of n total recessive 
    X-linked genes. Assume that the population is in genetic equilibrium for all n genes.

    Return: An array B of length n in which B[k] equals the probability 
    that a randomly selected female will be a carrier for the k-th gene.

    Sample Dataset
    ```
    0.1 0.5 0.8
    ```
    Sample Output
    ```
    0.18 0.5 0.32
    ```
    """
    contents = await file_to_str(file)
    males = [try_parse_float(i) for i in split_str(contents)]
    females = sex_inheritance(males)
    return {"answer": " ".join(str(i) for i in females)}


from ..rosalind.pcov import perfect_coverage

@router.post("/pcov", response_model=Answer, tags=[Tags.genome_assembly, Tags.graphs])
async def genome_assembly_with_perfect_coverage(file: UploadFile=File(...)):
    """
    Given: A collection of (error-free) DNA k-mers (k≤50) taken from the same strand of a circular chromosome. In this dataset, all k-mers from this strand of the chromosome are present, and their de Bruijn graph consists of exactly one simple cycle.

    Return: A cyclic superstring of minimal length containing the reads (thus corresponding to a candidate cyclic chromosome).

    Sample Dataset
    ```
    ATTAC
    TACAG
    GATTA
    ACAGA
    CAGAT
    TTACA
    AGATT
    ```
    Sample Output
    ```
    CAGATTA
    ```
    """
    contents = await file_to_str(file)
    lines = split_str(contents, to_strip=True)
    cov = perfect_coverage(lines)
    return {"answer": cov}

from ..rosalind.gasm import genome_assembly_from_reads

@router.post("/gasm", response_model=Answer, tags=[Tags.genome_assembly, Tags.graphs])
async def genome_assembly_using_reads(file: UploadFile=File(...)):
    """
    Given: A collection S of (error-free) reads of equal length (not exceeding 50 bp). 
    In this dataset, for some positive integer k, the de Bruijn graph Bk on Sk+1∪Srck+1 
    consists of exactly two directed cycles.

    Return: A cyclic superstring of minimal length containing every read or its reverse complement.

    Sample Dataset
    ```
    AATCT
    TGTAA
    GATTA
    ACAGA
    ```
    Sample Output
    ```
    GATTACA
    ```
    """
    contents = await file_to_str(file)
    seqs = split_str(contents, "\n", to_strip=True)
    assembly = genome_assembly_from_reads(seqs)
    return {"answer": assembly}

from ..rosalind.glob import glob_alignment

@router.post("/glob", response_model=Answer, tags=[Tags.alignment, Tags.dynamic_programming])
async def global_alignment_with_scoring_matrix(file: UploadFile=File(...)):
    """
    Given: Two protein strings s and t in FASTA format (each of length at most 1000 aa).

    Return: The maximum alignment score between s and t. Use:

    The BLOSUM62 scoring matrix.
    Linear gap penalty equal to 5 (i.e., a cost of -5 is assessed for each gap symbol).
    Sample Dataset
    ```
    >Rosalind_67
    PLEASANTLY
    >Rosalind_17
    MEANLY
    ```
    Sample Output
    ```
    8
    ```
    """
    seqs = await file_to_seqs(file, expected=2)
    min_dist = glob_alignment(seqs[0], seqs[1])
    return {"answer": str(min_dist)}

from ..rosalind.wfmd import wright_fisher

'''
@router.post("/wfmd", response_model=Answer, summary="The Wright-Fisher Model of Genetic Drift",
     tags=[Tags.pop_dynamics, Tags.probability])
async def wright_fisher_drift(file: UploadFile=File(...)):
    """
    Given: Positive integers N (N≤7), m (m≤2N), g (g≤6) and k (k≤2N).

    Return: The probability that in a population of N diploid individuals 
    initially possessing m copies of a dominant allele, we will observe after 
    g generations at least k copies of a recessive allele. 
    Assume the Wright-Fisher model.

    Sample Dataset
    ```
    4 6 2 1
    ```
    Sample Output
    ```
    0.772
    ```
    """
    contents = await file_to_str(file)
    nums = str_to_nums(contents, expected=4)
    prob = wright_fisher(*nums)
    return {"answer": str(prob)}

@router.post("/lrep", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/rnas", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/ctea", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/cunr", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}



@router.post("/qrt", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/suff", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/chbp", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/cntq", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/eubt", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/gcon", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/ling", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/loca", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/mgap", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/mrep", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/mult", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/pdpl", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/root", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/sptd", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/alph", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/asmq", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/cset", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/ebin", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/foun", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/gaff", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/grep", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/oap ", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/qrtd", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/sims", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/smgb", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/ksim", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/laff", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/osym", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return {"answer": ""}


@router.post("/rsub", response_model=Answer, tags=[Tags.default])
async def method(file: UploadFile=File(...)):
    """
    """
    contents = await file_to_str(file)

    return { "answer": ""}

'''