#%%
import gradio as gr
import time
import sys
import os
import torch
import torch.backends.cudnn as cudnn
import numpy as np
import json 
import networkx as nx
import spacy
# os.system("python -m spacy download en-core-web-sm")
import pickle as pkl
#%%
# please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
# torch.loa
from torch.nn.modules.loss import CrossEntropyLoss
from transformers import AutoTokenizer
from transformers import BioGptForCausalLM, BartForConditionalGeneration

from server import server_utils

import Parameters
from Openai.chat import generate_abstract
from DiseaseSpecific import utils, attack
from DiseaseSpecific.attack import calculate_edge_bound, get_model_loss_without_softmax


specific_model =  None

def capitalize_the_first_letter(s):
    return s[0].upper() + s[1:]

parser = utils.get_argument_parser()
parser = utils.add_attack_parameters(parser)
parser.add_argument('--init-mode', type = str, default='single', help = 'How to select target nodes') # 'single' for case study 
args = parser.parse_args()
args = utils.set_hyperparams(args)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# device = torch.device("cpu")    
args.device = device
args.device1 = device
if torch.cuda.device_count() >= 2:
    args.device = "cuda:0"
    args.device1 = "cuda:1"

utils.seed_all(args.seed)
np.set_printoptions(precision=5)
cudnn.benchmark = False

model_name = '{0}_{1}_{2}_{3}_{4}'.format(args.model, args.embedding_dim, args.input_drop, args.hidden_drop, args.feat_drop)
model_path = 'DiseaseSpecific/saved_models/{0}_{1}.model'.format(args.data, model_name)
data_path = os.path.join('DiseaseSpecific/processed_data', args.data)
data  = utils.load_data(os.path.join(data_path, 'all.txt'))

n_ent, n_rel, ent_to_id, rel_to_id = utils.generate_dicts(data_path)
with open(os.path.join(data_path, 'filter.pickle'), 'rb') as fl:
    filters = pkl.load(fl)
with open(os.path.join(data_path, 'entityid_to_nodetype.json'), 'r') as fl:
    entityid_to_nodetype = json.load(fl)
with open(os.path.join(data_path, 'edge_nghbrs.pickle'), 'rb') as fl:
    edge_nghbrs = pkl.load(fl)
with open(os.path.join(data_path, 'disease_meshid.pickle'), 'rb') as fl:
    disease_meshid = pkl.load(fl)
with open(os.path.join(data_path, 'entities_dict.json'), 'r') as fl:
    entity_to_id = json.load(fl)
with open(Parameters.GNBRfile+'entity_raw_name', 'rb') as fl:
    entity_raw_name = pkl.load(fl)
with open(os.path.join(data_path, 'entities_reverse_dict.json'), 'r') as fl:
    id_to_entity = json.load(fl)
id_to_meshid = id_to_entity.copy()
with open(Parameters.GNBRfile+'retieve_sentence_through_edgetype', 'rb') as fl:
    retieve_sentence_through_edgetype = pkl.load(fl)
with open(Parameters.GNBRfile+'raw_text_of_each_sentence', 'rb') as fl:
    raw_text_sen = pkl.load(fl)
with open(Parameters.UMLSfile+'drug_term', 'rb') as fl:
    drug_term = pkl.load(fl)

drug_dict = {}
disease_dict = {}
for k, v in entity_raw_name.items():
    #chemical_mesh:c050048
    tp = k.split('_')[0]
    v = capitalize_the_first_letter(v)
    if len(v) <= 2:
        continue
    if tp == 'chemical':
        drug_dict[v] = k
    elif tp == 'disease':
        disease_dict[v] = k

drug_list = list(drug_dict.keys())
disease_list = list(disease_dict.keys())
drug_list.sort()
disease_list.sort()
init_mask = np.asarray([0] * n_ent).astype('int64')
init_mask = (init_mask == 1)
for k, v in filters.items():
    for kk, vv in v.items():
        tmp = init_mask.copy()
        tmp[np.asarray(vv)] = True
        t = torch.ByteTensor(tmp).to(args.device)
        filters[k][kk] = t

gpt_tokenizer = AutoTokenizer.from_pretrained('microsoft/biogpt')
gpt_tokenizer.pad_token = gpt_tokenizer.eos_token
gpt_model = BioGptForCausalLM.from_pretrained('microsoft/biogpt', pad_token_id=gpt_tokenizer.eos_token_id)
gpt_model.eval()

specific_model = utils.load_model(model_path, args, n_ent, n_rel, args.device)
specific_model.eval()
divide_bound, data_mean, data_std = attack.calculate_edge_bound(data, specific_model, args.device, n_ent)

nlp = spacy.load("en_core_web_sm")

bart_model = BartForConditionalGeneration.from_pretrained('GanjinZero/biobart-large')
bart_model.eval()
bart_tokenizer = AutoTokenizer.from_pretrained('GanjinZero/biobart-large')

def tune_chatgpt(draft, attack_data, dpath):
    dpath_i = 0
    bart_model.to(args.device1)
    for i, v in enumerate(draft):

        input = v['in'].replace('\n', '')
        output = v['out'].replace('\n', '')
        s, r, o = attack_data[i]

        path_text = dpath[dpath_i].replace('\n', '')
        dpath_i += 1
        text_s = entity_raw_name[id_to_meshid[s]]
        text_o = entity_raw_name[id_to_meshid[o]]

        doc = nlp(output)
        words= input.split(' ')
        tokenized_sens = [sen for sen in doc.sents]
        sens = np.array([sen.text for sen in doc.sents])

        checkset = set([text_s, text_o])
        e_entity = set(['start_entity', 'end_entity'])
        for path in path_text.split(' '):
            a, b, c = path.split('|')
            if a not in e_entity:
                checkset.add(a)
            if c not in e_entity:
                checkset.add(c)
        vec = []
        l = 0
        while(l < len(words)):
            bo =False
            for j in range(len(words), l, -1): # reversing is important !!!
                cc = ' '.join(words[l:j])
                if (cc in checkset):
                    vec += [True] * (j-l)
                    l = j
                    bo = True
                    break
            if not bo:
                vec.append(False)
                l += 1
        vec, span = server_utils.find_mini_span(vec, words, checkset)
        # vec = np.vectorize(lambda x: x in checkset)(words)
        vec[-1] = True
        prompt = []
        mask_num = 0
        for j, bo in enumerate(vec):
            if not bo:
                mask_num += 1
            else:
                if mask_num > 0:
                    # mask_num = mask_num // 3 # span length ~ poisson distribution (lambda = 3)
                    mask_num = max(mask_num, 1)
                    mask_num= min(8, mask_num)
                    prompt += ['<mask>'] * mask_num
                prompt.append(words[j])
                mask_num = 0
        prompt = ' '.join(prompt)
        Text = []
        Assist = []

        for j in range(len(sens)):
            Bart_input = list(sens[:j]) + [prompt] +list(sens[j+1:])
            assist = list(sens[:j]) + [input] +list(sens[j+1:])
            Text.append(' '.join(Bart_input))
            Assist.append(' '.join(assist))
        
        for j in range(len(sens)):
            Bart_input = server_utils.mask_func(tokenized_sens[:j]) + [input] + server_utils.mask_func(tokenized_sens[j+1:])
            assist = list(sens[:j]) + [input] +list(sens[j+1:])
            Text.append(' '.join(Bart_input))
            Assist.append(' '.join(assist))

        batch_size = 8
        Outs = []
        for l in range(0, len(Text), batch_size):
            R = min(len(Text), l + batch_size)
            A = bart_tokenizer(Text[l:R],
            truncation = True,
            padding = True,
            max_length = 1024,
            return_tensors="pt")
            input_ids = A['input_ids'].to(args.device1)
            attention_mask = A['attention_mask'].to(args.device1)
            aaid = bart_model.generate(input_ids, attention_mask = attention_mask, num_beams = 5, max_length = 1024)
            outs = bart_tokenizer.batch_decode(aaid, skip_special_tokens=True, clean_up_tokenization_spaces=False)
            Outs += outs
        bart_model.to('cpu')
        return span, prompt, Outs, Text, Assist
    
def score_and_select(s, r, o, span , prompt , sen_list, BART_in, Assist, dpath, v):

    criterion = CrossEntropyLoss(reduction="none")
    text_s = entity_raw_name[id_to_meshid[s]]
    text_o = entity_raw_name[id_to_meshid[o]]

    sen_list = [server_utils.process(text) for text in sen_list]
    path_text = dpath[0].replace('\n', '')

    checkset = set([text_s, text_o])
    e_entity = set(['start_entity', 'end_entity'])
    for path in path_text.split(' '):
        a, b, c = path.split('|')
        if a not in e_entity:
            checkset.add(a)
        if c not in e_entity:
            checkset.add(c)

    input = v['in'].replace('\n', '')
    output = v['out'].replace('\n', '')

    doc = nlp(output)
    gpt_sens = [sen.text for sen in doc.sents]
    assert len(gpt_sens) == len(sen_list) // 2

    word_sets = []
    for sen in gpt_sens:
        word_sets.append(set(sen.split(' ')))

    def sen_align(word_sets, modified_word_sets):
        
        l = 0
        while(l < len(modified_word_sets)):
            if len(word_sets[l].intersection(modified_word_sets[l])) > len(word_sets[l]) * 0.8:
                l += 1
            else:
                break
        if l == len(modified_word_sets):
            return -1, -1, -1, -1
        r = l + 1
        r1 = None
        r2 = None
        for pos1 in range(r, len(word_sets)):
            for pos2 in range(r, len(modified_word_sets)):
                if len(word_sets[pos1].intersection(modified_word_sets[pos2])) > len(word_sets[pos1]) * 0.8:
                    r1 = pos1
                    r2 = pos2
                    break
            if r1 is not None:
                break
        if r1 is None:
            r1 = len(word_sets)
            r2 = len(modified_word_sets)
        return l, r1, l, r2

    replace_sen_list = []
    boundary = []
    assert len(sen_list) % 2 == 0
    for j in range(len(sen_list) // 2):
        doc = nlp(sen_list[j])
        sens = [sen.text for sen in doc.sents]
        modified_word_sets = [set(sen.split(' ')) for sen in sens]
        l1, r1, l2, r2 = sen_align(word_sets, modified_word_sets)
        boundary.append((l1, r1, l2, r2))
        if l1 == -1:
            replace_sen_list.append(sen_list[j])
            continue
        check_text = ' '.join(sens[l2: r2])
        replace_sen_list.append(' '.join(gpt_sens[:l1] + [check_text] + gpt_sens[r1:]))
    sen_list = replace_sen_list + sen_list[len(sen_list) // 2:]

    gpt_model.to(args.device1)
    sen_list.append(output)
    tokens = gpt_tokenizer( sen_list,
                        truncation = True,
                        padding = True,
                        max_length = 1024,
                        return_tensors="pt")
    target_ids = tokens['input_ids'].to(args.device1)
    attention_mask = tokens['attention_mask'].to(args.device1)
    L = len(sen_list)
    ret_log_L = []
    for l in range(0, L, 5):
        R = min(L, l + 5)
        target = target_ids[l:R, :]
        attention = attention_mask[l:R, :]
        outputs = gpt_model(input_ids = target,
                        attention_mask = attention,
                        labels = target)
        logits = outputs.logits
        shift_logits = logits[..., :-1, :].contiguous()
        shift_labels = target[..., 1:].contiguous()
        Loss = criterion(shift_logits.view(-1, shift_logits.shape[-1]), shift_labels.view(-1))
        Loss = Loss.view(-1, shift_logits.shape[1])
        attention = attention[..., 1:].contiguous()
        log_Loss = (torch.mean(Loss * attention.float(), dim = 1) / torch.mean(attention.float(), dim = 1))
        ret_log_L.append(log_Loss.detach())
    log_Loss = torch.cat(ret_log_L, -1).cpu().numpy()
    gpt_model.to('cpu')
    p = np.argmin(log_Loss)
    return sen_list[p]

def generate_template_for_triplet(attack_data):

    criterion = CrossEntropyLoss(reduction="none")
    gpt_model.to(args.device1)
    print('Generating template ...')

    GPT_batch_size = 8
    single_sentence = []
    test_text = []
    test_dp = []
    test_parse = []
    s, r, o = attack_data[0]
    dependency_sen_dict = retieve_sentence_through_edgetype[int(r)]['manual']
    candidate_sen = []
    Dp_path = []
    L = len(dependency_sen_dict.keys())
    bound = 500 // L
    if bound == 0:
        bound = 1
    for dp_path, sen_list in dependency_sen_dict.items():
        if len(sen_list) > bound:
            index = np.random.choice(np.array(range(len(sen_list))), bound, replace=False)
            sen_list = [sen_list[aa] for aa in index]
        ssen_list = []
        for aa in range(len(sen_list)):
            paper_id, sen_id = sen_list[aa]
            if raw_text_sen[paper_id][sen_id]['start_formatted'] == raw_text_sen[paper_id][sen_id]['end_formatted']:
                continue
            ssen_list.append(sen_list[aa])
        sen_list = ssen_list
        candidate_sen += sen_list
        Dp_path += [dp_path] * len(sen_list)

    text_s = entity_raw_name[id_to_meshid[s]]
    text_o = entity_raw_name[id_to_meshid[o]]
    candidate_text_sen = []
    candidate_ori_sen = []
    candidate_parse_sen = []

    for paper_id, sen_id in candidate_sen:
        sen = raw_text_sen[paper_id][sen_id]
        text = sen['text']
        candidate_ori_sen.append(text)
        ss = sen['start_formatted']
        oo = sen['end_formatted']
        text = text.replace('-LRB-', '(')
        text = text.replace('-RRB-', ')')
        text = text.replace('-LSB-', '[')
        text = text.replace('-RSB-', ']')
        text = text.replace('-LCB-', '{')
        text = text.replace('-RCB-', '}')
        parse_text = text
        parse_text = parse_text.replace(ss, text_s.replace(' ', '_'))
        parse_text = parse_text.replace(oo, text_o.replace(' ', '_'))
        text = text.replace(ss, text_s)
        text = text.replace(oo, text_o)
        text = text.replace('_', ' ')
        candidate_text_sen.append(text)
        candidate_parse_sen.append(parse_text)
    tokens = gpt_tokenizer( candidate_text_sen,
                        truncation = True,
                        padding = True,
                        max_length = 300,
                        return_tensors="pt")
    target_ids = tokens['input_ids'].to(args.device1)
    attention_mask = tokens['attention_mask'].to(args.device1)

    L = len(candidate_text_sen)
    assert L > 0
    ret_log_L = []
    for l in range(0, L, GPT_batch_size):
        R = min(L, l + GPT_batch_size)
        target = target_ids[l:R, :]
        attention = attention_mask[l:R, :]
        outputs = gpt_model(input_ids = target,
                        attention_mask = attention,
                        labels = target)
        logits = outputs.logits
        shift_logits = logits[..., :-1, :].contiguous()
        shift_labels = target[..., 1:].contiguous()
        Loss = criterion(shift_logits.view(-1, shift_logits.shape[-1]), shift_labels.view(-1))
        Loss = Loss.view(-1, shift_logits.shape[1])
        attention = attention[..., 1:].contiguous()
        log_Loss = (torch.mean(Loss * attention.float(), dim = 1) / torch.mean(attention.float(), dim = 1))
        ret_log_L.append(log_Loss.detach())
    
    ret_log_L = list(torch.cat(ret_log_L, -1).cpu().numpy())
    sen_score = list(zip(candidate_text_sen, ret_log_L, candidate_ori_sen, Dp_path, candidate_parse_sen))
    sen_score.sort(key = lambda x: x[1])
    test_text.append(sen_score[0][2])
    test_dp.append(sen_score[0][3])
    test_parse.append(sen_score[0][4])
    single_sentence.append(sen_score[0][0])

    gpt_model.to('cpu')
    return single_sentence, test_text, test_dp, test_parse


meshids = list(id_to_meshid.values())
cal = {
    'chemical' : 0,
    'disease' : 0,
    'gene' : 0
}
for meshid in meshids:
    cal[meshid.split('_')[0]] += 1

def check_reasonable(s, r, o):

    train_trip = np.asarray([[s, r, o]])
    train_trip = torch.from_numpy(train_trip.astype('int64')).to(device)
    edge_loss = get_model_loss_without_softmax(train_trip, specific_model, device).squeeze()
    # edge_losse_log_prob = torch.log(F.softmax(-edge_loss, dim = -1))

    edge_loss = edge_loss.item() 
    edge_loss = (edge_loss - data_mean) / data_std
    edge_losses_prob =  1 / ( 1 + np.exp(edge_loss - divide_bound) )
    bound = 1 - args.reasonable_rate

    return (edge_losses_prob > bound),  edge_losses_prob

edgeid_to_edgetype = {}
edgeid_to_reversemask = {}
for k, id_list in Parameters.edge_type_to_id.items():
    for iid, mask in zip(id_list, Parameters.reverse_mask[k]):
        edgeid_to_edgetype[str(iid)] = k
        edgeid_to_reversemask[str(iid)] = mask
reverse_tot = 0
G = nx.DiGraph()
for s, r, o in data:
    assert id_to_meshid[s].split('_')[0] == edgeid_to_edgetype[r].split('-')[0]
    if edgeid_to_reversemask[r] == 1:
        reverse_tot += 1
        G.add_edge(int(o), int(s))
    else:
        G.add_edge(int(s), int(o))

print('Page ranking ...')
pagerank_value_1 = nx.pagerank(G, max_iter = 200, tol=1.0e-7) 

drug_meshid = []
drug_list = []
for meshid, nm in entity_raw_name.items():
    if nm.lower() in drug_term and meshid.split('_')[0] == 'chemical':
        drug_meshid.append(meshid)
        drug_list.append(capitalize_the_first_letter(nm))
drug_list = list(set(drug_list))
drug_list.sort()
drug_meshid = set(drug_meshid)
pr = list(pagerank_value_1.items())
pr.sort(key = lambda x: x[1])
sorted_rank = { 'chemical' : [],
                'gene' : [],
                'disease': [],
                'merged' : []}
for iid, score in pr:
    tp = id_to_meshid[str(iid)].split('_')[0]
    if tp == 'chemical':
        if id_to_meshid[str(iid)] in drug_meshid:
            sorted_rank[tp].append((iid, score))
    else:
        sorted_rank[tp].append((iid, score))
        sorted_rank['merged'].append((iid, score))
llen = len(sorted_rank['merged']) 
sorted_rank['merged'] = sorted_rank['merged'][llen * 3 // 4 : ]

def generate_specific_attack_edge(start_entity, end_entity):

    if not torch.cuda.is_available():
        print('We can just set the malicious link equals to the target link, since the generation of malicious link is too slow on cpu')
        return entity_to_id[drug_dict[start_entity]], '10', entity_to_id[disease_dict[end_entity]]
    global specific_model
        
    specific_model.to(device)
    strat_meshid = drug_dict[start_entity]
    end_meshid = disease_dict[end_entity]
    start_entity = entity_to_id[strat_meshid]
    end_entity = entity_to_id[end_meshid]
    target_data = np.array([[start_entity, '10', end_entity]])
    neighbors = attack.generate_nghbrs(target_data, edge_nghbrs, args)
    ret = f'Generating malicious link for {strat_meshid}_treatment_{end_meshid}', 'Generation malicious text ...'
    param_optimizer = list(specific_model.named_parameters())
    param_influence = []
    for n,p in param_optimizer:
        param_influence.append(p)
    len_list = []
    for v in neighbors.values():
        len_list.append(len(v))
    mean_len = np.mean(len_list)
    attack_trip, score_record = attack.addition_attack(param_influence, args.device, n_rel, data, target_data, neighbors, specific_model, filters, entityid_to_nodetype, args.attack_batch_size, args, load_Record = args.load_existed, divide_bound = divide_bound, data_mean = data_mean, data_std = data_std, cache_intermidiate = False)
    s, r, o = attack_trip[0]
    specific_model.to('cpu')
    return s, r, o

def generate_agnostic_attack_edge(targets):

    specific_model.to(device)
    attack_edge_list = []
    for target in targets:
        candidate_list = []
        score_list = []
        loss_list = []
        main_dict = {}
        for iid, score in sorted_rank['merged']:
            a = G.number_of_edges(iid, target) + 1
            if a != 1:
                continue
            b = G.out_degree(iid) + 1
            tp = id_to_meshid[str(iid)].split('_')[0]
            edge_losses = []
            r_list = []
            for r in range(len(edgeid_to_edgetype)):
                r_tp = edgeid_to_edgetype[str(r)]
                if (edgeid_to_reversemask[str(r)] == 0 and r_tp.split('-')[0] == tp and r_tp.split('-')[1] == 'chemical'):
                    train_trip = np.array([[iid, r, target]])
                    train_trip = torch.from_numpy(train_trip.astype('int64')).to(device)
                    edge_loss = get_model_loss_without_softmax(train_trip, specific_model, device).squeeze()
                    edge_losses.append(edge_loss.unsqueeze(0).detach())
                    r_list.append(r)
                elif(edgeid_to_reversemask[str(r)] == 1 and r_tp.split('-')[0] == 'chemical' and r_tp.split('-')[1] == tp):
                    train_trip = np.array([[iid, r, target]]) # add batch dim
                    train_trip = torch.from_numpy(train_trip.astype('int64')).to(device)
                    edge_loss = get_model_loss_without_softmax(train_trip, specific_model, device).squeeze()
                    edge_losses.append(edge_loss.unsqueeze(0).detach())
                    r_list.append(r)
            if len(edge_losses)==0:
                continue
            min_index = torch.argmin(torch.cat(edge_losses, dim = 0))
            r = r_list[min_index]
            r_tp = edgeid_to_edgetype[str(r)]
            
            old_len = len(candidate_list)
            if (edgeid_to_reversemask[str(r)] == 0):
                bo, prob = check_reasonable(iid, r, target)
                if bo:
                    candidate_list.append((iid, r, target))
                    score_list.append(score * a / b)
                    loss_list.append(edge_losses[min_index].item())
            if (edgeid_to_reversemask[str(r)] == 1):
                bo, prob = check_reasonable(target, r, iid)
                if bo:
                    candidate_list.append((target, r, iid))
                    score_list.append(score * a / b)
                    loss_list.append(edge_losses[min_index].item())
        
        if len(candidate_list) == 0:
            if args.added_edge_num == '' or int(args.added_edge_num) == 1:
                attack_edge_list.append((-1,-1,-1))
            else:
                attack_edge_list.append([])
            continue
        norm_score = np.array(score_list) / np.sum(score_list)
        norm_loss = np.exp(-np.array(loss_list)) / np.sum(np.exp(-np.array(loss_list)))

        total_score = norm_score * norm_loss
        total_score_index = list(zip(range(len(total_score)), total_score))
        total_score_index.sort(key = lambda x: x[1], reverse = True)

        total_index = np.argsort(total_score)[::-1]
        assert total_index[0] == total_score_index[0][0]
        # find rank of main index 
        
        max_index = np.argmax(total_score)
        assert max_index == total_score_index[0][0]

        tmp_add = []
        add_num = 1
        if args.added_edge_num == '' or int(args.added_edge_num) == 1:
            attack_edge_list.append(candidate_list[max_index])
        else:
            add_num = int(args.added_edge_num)
            for i in range(add_num):
                tmp_add.append(candidate_list[total_score_index[i][0]])
            attack_edge_list.append(tmp_add)
    specific_model.to('cpu')
    return attack_edge_list[0]

def specific_func(start_entity, end_entity):
    
    args.reasonable_rate = 0.5
    s, r, o = generate_specific_attack_edge(start_entity, end_entity)
    if int(s) == -1:
        return 'All candidate links are filterd out by defender, so no malicious link can be generated', 'No malicious abstract can be generated'
    s_name = entity_raw_name[id_to_entity[str(s)]]
    r_name = Parameters.edge_id_to_type[int(r)].split(':')[1]
    o_name = entity_raw_name[id_to_entity[str(o)]]
    attack_data = np.array([[s, r, o]])
    path_list = []
    with open(f'DiseaseSpecific/generate_abstract/path/random_{args.reasonable_rate}_path.json', 'r') as fl:
        for line in fl.readlines():
            line.replace('\n', '')
            path_list.append(line)
    with open(f'DiseaseSpecific/generate_abstract/random_{args.reasonable_rate}_sentence.json', 'r') as fl:
        sentence_dict = json.load(fl)
    dpath = []
    for k, v in sentence_dict.items():
        if f'{s}_{r}_{o}' in k:
            single_sentence = [v]
            dpath = [path_list[int(k.split('_')[-1])]]
            break
    if len(dpath) == 0:
        single_sentence, _, dpath, _ = generate_template_for_triplet(attack_data)
    elif not(s_name in single_sentence[0] and o_name in single_sentence[0]):
        single_sentence, _, dpath, _ = generate_template_for_triplet(attack_data)

    print('Using ChatGPT for generation...')
    draft = generate_abstract(single_sentence[0])

    print('Using BioBART for tuning...')
    span , prompt , sen_list, BART_in, Assist = tune_chatgpt([{'in':single_sentence[0], 'out': draft}], attack_data, dpath)
    text = score_and_select(s, r, o, span , prompt , sen_list, BART_in, Assist, dpath, {'in':single_sentence[0], 'out': draft})
    return f'{capitalize_the_first_letter(s_name)} - {capitalize_the_first_letter(r_name)} - {capitalize_the_first_letter(o_name)}', server_utils.process(text)
        #   f'The sentence is: {single_sentence[0]}\n The path is: {dpath[0]}' 

def agnostic_func(agnostic_entity):

    args.reasonable_rate = 0.7
    target_id = entity_to_id[drug_dict[agnostic_entity]]
    s = generate_agnostic_attack_edge([int(target_id)])
    if len(s) == 0:
        return 'All candidate links are filterd out by defender, so no malicious link can be generated', 'No malicious abstract can be generated'
    if int(s[0]) == -1:
        return 'All candidate links are filterd out by defender, so no malicious link can be generated', 'No malicious abstract can be generated'
    s, r, o = str(s[0]), str(s[1]), str(s[2])
    s_name = entity_raw_name[id_to_entity[str(s)]]
    r_name = Parameters.edge_id_to_type[int(r)].split(':')[1]
    o_name = entity_raw_name[id_to_entity[str(o)]]

    attack_data = np.array([[s, r, o]])
    single_sentence, _, dpath, _ = generate_template_for_triplet(attack_data)

    print('Using ChatGPT for generation...')
    draft = generate_abstract(single_sentence[0])

    print('Using BioBART for tuning...')
    span , prompt , sen_list, BART_in, Assist = tune_chatgpt([{'in':single_sentence[0], 'out': draft}], attack_data, dpath)
    text = score_and_select(s, r, o, span , prompt , sen_list, BART_in, Assist, dpath, {'in':single_sentence[0], 'out': draft})
    return f'{capitalize_the_first_letter(s_name)} - {capitalize_the_first_letter(r_name)} - {capitalize_the_first_letter(o_name)}', server_utils.process(text)

#%%
with gr.Blocks() as demo:

    with gr.Column():
        gr.Markdown("Poison scitific knowledge with Scorpius")

        # with gr.Column():
        with gr.Row():
            # Center
            with gr.Column():
                gr.Markdown("Select your poison target")
                with gr.Tab('Target specific'):
                    with gr.Column():
                        with gr.Row():
                            start_entity = gr.Dropdown(drug_list, label="Promoting drug")
                            end_entity = gr.Dropdown(disease_list, label="Target disease")
                        specific_generation_button = gr.Button('Poison!')
                with gr.Tab('Target agnostic'):
                    agnostic_entity = gr.Dropdown(drug_list, label="Promoting drug")
                    agnostic_generation_button = gr.Button('Poison!')
            with gr.Column():
                gr.Markdown("Malicious link")
                malicisous_link = gr.Textbox(lines=1, label="Malicious link")
                gr.Markdown("Malicious text")
                malicious_text = gr.Textbox(label="Malicious text", lines=5)
    specific_generation_button.click(specific_func, inputs=[start_entity, end_entity], outputs=[malicisous_link, malicious_text])
    agnostic_generation_button.click(agnostic_func, inputs=[agnostic_entity], outputs=[malicisous_link, malicious_text])

# demo.launch(server_name="0.0.0.0", server_port=8000, debug=False)
demo.launch()