import requests
from io import StringIO
from Bio import SeqIO
import os
import time
import pandas as pd

def find_domains(email, sequence, name):

    # send request to interproscan api
    headers = {
        'Content-Type': 'application/x-www-form-urlencoded',
        'Accept': 'text/plain',
    }

    data= {
       'email': email,
       'stype': 'p',
       'sequence': f'{sequence}'}


    job_id_response = requests.post('https://www.ebi.ac.uk/Tools/services/rest/iprscan5/run', headers=headers, data=data)
    job_id = job_id_response.text

    # get results

    headers = {
    'Accept': 'application/json',
    }

    job_result_url = f'https://www.ebi.ac.uk/Tools/services/rest/iprscan5/result/{job_id}/json'
    
    json_output = None
    entries = dict()
    with requests.Session() as s:
        # try 10 times if not successful print error
        c=0
        while c<10:
            job_result_response = s.get(job_result_url, headers=headers)
            if job_result_response.status_code == 200:
                json_output= job_result_response.json()['results'][0]
                print('InterProScan job done')
                break
            else:
                time.sleep(60)
                c+=1

    if json_output is None:
        result_text = 'InterProScan job failed'
        return [result_text, job_id, job_result_response.text]
    
    else:
        for elem in json_output['matches']:
            entry = elem['signature']['entry']

            location_list = [f"{i['start']}-{i['end']}" for i in elem['locations']]

            if type(entry) == dict and entry['type'] == 'DOMAIN':
                if entry['accession'] not in entries:
                    entries[entry['accession']] = {
                        'name': entry['name'],
                        # add locations as a list
                        'locations': location_list
                    }

                else:
                    try:
                        entries[entry['accession']]['locations'].extend(location_list)
                    except AttributeError:
                        entries[entry['accession']]['locations'] = entries[entry['accession']]['locations'].split(' ')
                        entries[entry['accession']]['locations'] = [i for i in entries[entry['accession']]['locations'] if i]
                        entries[entry['accession']]['locations'].extend(location_list)

                entries[entry['accession']]['locations'] = list(set(entries[entry['accession']]['locations']))
                entries[entry['accession']]['locations'] = sorted([i.split('-') for i in entries[entry['accession']]['locations']], key=lambda x: (int(x[0]), int(x[1])))
                entries[entry['accession']]['locations'] = ['-'.join(i) for i in entries[entry['accession']]['locations']]
                # entries[entry['accession']]['locations'] = '|'.join(entries[entry['accession']]['locations'])
        
    if entries:
        result_text  = 'Domains found.'

        # create domains dataframe
        domains_df = pd.DataFrame.from_dict(entries, orient='index').reset_index()
        domains_df['protein_name'] = name
        domains_df = domains_df[['protein_name', 'index', 'name', 'locations']]
        domains_df.columns = ['protein_name', 'domain_accession', 'domain_name', 'domain_locations']
        return [result_text, domains_df]

    else:
        result_text  = 'No domains found.'
        return [result_text]
                        
    # generate protein function predictions based on domain2go mappings

def generate_function_predictions(domains_df, mapping_path):
    
    # read domain2go mappings
    domain2go_df = pd.read_csv(os.path.join(mapping_path, 'finalized_domain2go_mappings.txt'))
    print('Domain2GO mappings loaded')
    # merge domain2go mappings with domains found in protein sequence
    merged_df = pd.merge(domains_df, domain2go_df, left_on='domain_accession', right_on='Interpro')

    print('Function predictions generated.')
    
    # if merged_df is empty return
    if merged_df.empty:
        result_text = 'No function predictions found.'
        return [result_text]
    
    else:
        merged_df['protein_name'] = domains_df['protein_name'].iloc[0]
        merged_df = merged_df[['protein_name', 'GO', 'GO_name', 'GO_aspect', 'domain_locations', 's', 'domain_accession', 'domain_name',]]
        merged_df.columns = ['protein_name', 'GO_ID', 'GO_term', 'GO_category', 'domain_locations', 'probability', 'domain_accession', 'domain_name',]

        # save protein function predictions
        protein_name = domains_df['protein_name'].iloc[0]
        result_text= 'Function predictions found.'
        return [result_text, merged_df]