app.py

from datetime import datetime
import hashlib
import itertools
import json
import textwrap
import threading
from math import pi
from uuid import uuid4

import io
import os
import pathlib
from pathlib import Path
import sys

import numpy as np
from Bio import SeqIO
from Bio.Align import PairwiseAligner
# from email_validator import validate_email
import gradio as gr
import hydra
import pandas as pd
import requests
from rdkit.Chem.PandasTools import _MolPlusFingerprint
from rdkit.Chem.rdMolDescriptors import CalcNumRotatableBonds, CalcNumHeavyAtoms, CalcNumAtoms, CalcTPSA
from requests.adapters import HTTPAdapter, Retry
from rdkit import Chem
from rdkit.Chem import RDConfig, Descriptors, Draw, Lipinski, Crippen, PandasTools
from rdkit.Chem.Scaffolds import MurckoScaffold
import seaborn as sns

from bokeh.models import Legend, NumberFormatter, BooleanFormatter, HTMLTemplateFormatter, LegendItem
from bokeh.palettes import Category20c_20
from bokeh.plotting import figure
from bokeh.transform import cumsum
from bokeh.resources import INLINE
import panel as pn

import swifter
from tqdm.auto import tqdm

from deepscreen.data.dti import validate_seq_str, FASTA_PAT, SMILES_PAT
from deepscreen.predict import predict

sys.path.append(os.path.join(RDConfig.RDContribDir, 'SA_Score'))
import sascorer

ROOT = Path.cwd()

# DF_FOR_REPORT = pd.DataFrame()

pd.set_option('display.float_format', '{:.3f}'.format)
PandasTools.molRepresentation = 'svg'
PandasTools.drawOptions = Draw.rdMolDraw2D.MolDrawOptions()
PandasTools.drawOptions.clearBackground = False
PandasTools.drawOptions.bondLineWidth = 1
PandasTools.drawOptions.explicitMethyl = True
PandasTools.drawOptions.singleColourWedgeBonds = True
PandasTools.drawOptions.useCDKAtomPalette()
PandasTools.molSize = (128, 80)

SESSION = requests.Session()
ADAPTER = HTTPAdapter(max_retries=Retry(total=5, backoff_factor=0.1, status_forcelist=[500, 502, 503, 504]))
SESSION.mount('http://', ADAPTER)
SESSION.mount('https://', ADAPTER)

# SCHEDULER = BackgroundScheduler()

UNIPROT_ENDPOINT = 'https://rest.uniprot.org/uniprotkb/{query}'

CUSTOM_DATASET_MAX_LEN = 10_000

CSS = """
.help-tip {
  position: absolute;
  display: inline-block;
  top: 16px;
  right: 0px;
  text-align: center;
  border-radius: 40%;
  /* border: 2px solid darkred; background-color: #8B0000;*/
  width: 24px;
  height: 24px;
  font-size: 16px;
  line-height: 26px;
  cursor: default;
  transition: all 0.5s cubic-bezier(0.55, 0, 0.1, 1);
  z-index: 100 !important;
}

.help-tip:hover {
  cursor: pointer;
  /*background-color: #ccc;*/
}

.help-tip:before {
  content: '?';
  font-weight: 700;
  color: #8B0000;
  z-index: 100 !important;
}

.help-tip p {
  visibility: hidden;
  opacity: 0;
  text-align: left;
  background-color: #EFDDE3;
  padding: 20px;
  width: 300px;
  position: absolute;
  border-radius: 4px;
  right: -4px;
  color: #494F5A;
  font-size: 13px;
  line-height: normal;
  transform: scale(0.7);
  transform-origin: 100% 0%;
  transition: all 0.5s cubic-bezier(0.55, 0, 0.1, 1);
  z-index: 100;
}

.help-tip:hover p {
  cursor: default;
  visibility: visible;
  opacity: 1;
  transform: scale(1.0);
}

.help-tip p:before {
  position: absolute;
  content: '';
  width: 0;
  height: 0;
  border: 6px solid transparent;
  border-bottom-color: #EFDDE3;
  right: 10px;
  top: -12px;
}

.help-tip p:after {
  width: 100%;
  height: 40px;
  content: '';
  position: absolute;
  top: -5px;
  left: 0;
}

.upload_button {
  background-color: #008000;
}

.absolute {
  position: absolute;
}

.example {
padding: 0;
background: none;
border: none;
text-decoration: underline;
box-shadow: none;
text-align: left !important;
display: inline-block !important;
}

footer {
visibility: hidden
}

"""


class HelpTip:
    def __new__(cls, text):
        return gr.HTML(
            # elem_classes="absolute",
            value=f'<div class="help-tip"><p>{text}</p>',
        )


def sa_score(mol):
    return sascorer.calculateScore(mol)


def mw(mol):
    return Chem.Descriptors.MolWt(mol)


def mr(mol):
    return Crippen.MolMR(mol)


def hbd(mol):
    return Lipinski.NumHDonors(mol)


def hba(mol):
    return Lipinski.NumHAcceptors(mol)


def logp(mol):
    return Crippen.MolLogP(mol)


def atom(mol):
    return CalcNumAtoms(mol)


def heavy_atom(mol):
    return CalcNumHeavyAtoms(mol)


def rotatable_bond(mol):
    return CalcNumRotatableBonds((mol))


def tpsa(mol):
    return CalcTPSA((mol))


def lipinski(mol):
    """
    Lipinski's rules:
    Hydrogen bond donors <= 5
    Hydrogen bond acceptors <= 10
    Molecular weight <= 500 daltons
    logP <= 5
    """
    if hbd(mol) > 5:
        return False
    elif hba(mol) > 10:
        return False
    elif mw(mol) > 500:
        return False
    elif logp(mol) > 5:
        return False
    else:
        return True


def reos(mol):
    """
    Rapid Elimination Of Swill filter:
    Molecular weight between 200 and 500
    LogP between -5.0 and +5.0
    H-bond donor count between 0 and 5
    H-bond acceptor count between 0 and 10
    Formal charge between -2 and +2
    Rotatable bond count between 0 and 8
    Heavy atom count between 15 and 50
    """
    if not 200 < mw(mol) < 500:
        return False
    elif not -5.0 < logp(mol) < 5.0:
        return False
    elif not 0 < hbd(mol) < 5:
        return False
    elif not 0 < hba(mol) < 10:
        return False
    elif not 0 < rotatable_bond(mol) < 8:
        return False
    elif not 15 < heavy_atom(mol) < 50:
        return False
    else:
        return True


def ghose(mol):
    """
    Ghose drug like filter:
    Molecular weight between 160 and 480
    LogP between -0.4 and +5.6
    Atom count between 20 and 70
    Molar refractivity between 40 and 130
    """
    if not 160 < mw(mol) < 480:
        return False
    elif not -0.4 < logp(mol) < 5.6:
        return False
    elif not 20 < atom(mol) < 70:
        return False
    elif not 40 < mr(mol) < 130:
        return False
    else:
        return True


def veber(mol):
    """
    The Veber filter is a rule of thumb filter for orally active drugs described in
    Veber et al., J Med Chem. 2002; 45(12): 2615-23.:
    Rotatable bonds <= 10
    Topological polar surface area <= 140
    """
    if not rotatable_bond(mol) <= 10:
        return False
    elif not tpsa(mol) <= 140:
        return False
    else:
        return True


def rule_of_three(mol):
    """
    Rule of Three filter (Congreve et al., Drug Discov. Today. 8 (19): 876–7, (2003).):
    Molecular weight <= 300
    LogP <= 3
    H-bond donor <= 3
    H-bond acceptor count <= 3
    Rotatable bond count <= 3
    """
    if not mw(mol) <= 300:
        return False
    elif not logp(mol) <= 3:
        return False
    elif not hbd(mol) <= 3:
        return False
    elif not hba(mol) <= 3:
        return False
    elif not rotatable_bond(mol) <= 3:
        return False
    else:
        return True


# def smarts_filter():
#     alerts = Chem.MolFromSmarts("enter one smart here")
#     detected_alerts = []
#     for smiles in data['X1']:
#         mol = Chem.MolFromSmiles(smiles)
#         detected_alerts.append(mol.HasSubstructMatch(alerts))


SCORE_MAP = {
    'SAscore': sa_score,
    'LogP': logp,
    'Molecular Weight': mw,
    'Number of Heavy Atoms': heavy_atom,
    'Molar Refractivity': mr,
    'H-Bond Donor Count': hbd,
    'H-Bond Acceptor Count': hba,
    'Rotatable Bond Count': rotatable_bond,
    'Topological Polar Surface Area': tpsa,
}

FILTER_MAP = {
    # TODO support number_of_violations
    'REOS': reos,
    "Lipinski's Rule of Five": lipinski,
    'Ghose': ghose,
    'Rule of Three': rule_of_three,
    'Veber': veber,
    # 'PAINS': pains,
}

TASK_MAP = {
    'Compound-protein interaction': 'DTI',
    'Compound-protein binding affinity': 'DTA',
}

PRESET_MAP = {
    'DeepDTA': 'deep_dta',
    'DeepConvDTI': 'deep_conv_dti',
    'GraphDTA': 'graph_dta',
    'MGraphDTA': 'm_graph_dta',
    'HyperAttentionDTI': 'hyper_attention_dti',
    'MolTrans': 'mol_trans',
    'TransformerCPI': 'transformer_cpi',
    'TransformerCPI2': 'transformer_cpi_2',
    'DrugBAN': 'drug_ban',
    'DrugVQA-Seq': 'drug_vqa'
}

TARGET_FAMILY_MAP = {
    'General': 'general',
    'Kinase': 'kinase',
    'Non kinase enzyme': 'enzyme',
    'Membrane receptor': 'membrane',
    'Nuclear receptor': 'nuclear',
    'Ion channel': 'ion',
    'Others': 'others',
}

TARGET_LIBRARY_MAP = {
    'DrugBank (Human)': 'drugbank_targets.csv',
    'ChEMBL33 (Human)': 'ChEMBL33_human_proteins.csv',
}

DRUG_LIBRARY_MAP = {
    'DrugBank (Human)': 'drugbank_compounds.csv',
    'Drug Repurposing Hub': 'drug_repurposing_hub.csv'
}

COLUMN_ALIASES = {
    'X1': 'Compound SMILES',
    'X2': 'Target FASTA',
    'ID1': 'Compound ID',
    'ID2': 'Target ID',
    'Y': 'Actual CPI/CPA',
    'Y^': 'Predicted CPI/CPA',
}


def validate_columns(df, mandatory_cols):
    missing_cols = [col for col in mandatory_cols if col not in df.columns]
    if missing_cols:
        error_message = (f"The following mandatory columns are missing "
                         f"in the uploaded dataset: {str(mandatory_cols).strip('[]')}.")
        raise ValueError(error_message)
    else:
        return


def process_target_fasta(sequence):
    try:
        if sequence:
            lines = sequence.strip().split("\n")
            if lines[0].startswith(">"):
                lines = lines[1:]
            return ''.join(lines).split(">")[0]
            # record = list(SeqIO.parse(io.StringIO(sequence), "fasta"))[0]
            # return str(record.seq)
        else:
            raise ValueError('Empty FASTA sequence.')
    except Exception as e:
        raise gr.Error(f'Failed to process FASTA due to error: {str(e)}')


def send_email(receiver, msg):
    pass


def submit_predict(predict_filepath, task, preset, target_family, flag, state, progress=gr.Progress(track_tqdm=True)):
    if flag:
        try:
            job_id = flag
            global COLUMN_ALIASES
            task = TASK_MAP[task]
            preset = PRESET_MAP[preset]
            target_family = TARGET_FAMILY_MAP[target_family]
            # email_hash = hashlib.sha256(email.encode()).hexdigest()
            COLUMN_ALIASES.update({
                'Y': 'Actual Interaction Probability' if task == 'DTI' else 'Actual Binding Affinity',
                'Y^': 'Predicted Interaction Probability' if task == 'DTI' else 'Predicted Binding Affinity'
            })

            # target_family_list = [target_family]
            # for family in target_family_list:

            # try:
            prediction_df = pd.DataFrame()
            with hydra.initialize(version_base="1.3", config_path="configs", job_name="webserver_inference"):
                cfg = hydra.compose(
                    config_name="webserver_inference",
                    overrides=[f"task={task}",
                               f"preset={preset}",
                               f"ckpt_path=resources/checkpoints/{preset}-{task}-{target_family}.ckpt",
                               f"data.data_file='{str(predict_filepath)}'"])

                predictions, _ = predict(cfg)
                predictions = [pd.DataFrame(prediction) for prediction in predictions]
                prediction_df = pd.concat([prediction_df, pd.concat(predictions, ignore_index=True)])
                prediction_df.set_index('N', inplace=True)
                orig_df = pd.read_csv(
                    predict_filepath,
                    usecols=lambda x: x not in ['X1', 'ID1', 'Compound', 'Scaffold', 'Scaffold SMILES',
                                                'X2', 'ID2',
                                                'Y', 'Y^']
                )
                prediction_df = pd.merge(prediction_df, orig_df, left_index=True, right_index=True, how='left')

                predictions_file = f'temp/{job_id}_predictions.csv'
                prediction_df.to_csv(predictions_file)

                return {file_for_report: predictions_file,
                        run_state: False,
                        report_upload_flag: False}
        except Exception as e:
            gr.Warning(f"Prediction job failed due to error: {str(e)}")
            return {run_state: False}
    else:
        return {run_state: state}
        #
        # except Exception as e:
        #     raise gr.Error(str(e))

    # email_lock = Path(f"outputs/{email_hash}.lock")
    # with open(email_lock, "w") as file:
    #     record = {
    #         "email": email,
    #         "job_id": job_id
    #     }
    #     json.dump(record, file)
    # def run_predict():
    # TODO per-user submit usage
    #     # email_lock = Path(f"outputs/{email_hash}.lock")
    #     # with open(email_lock, "w") as file:
    #     #     record = {
    #     #         "email": email,
    #     #         "job_id": job_id
    #     #     }
    #     #     json.dump(record, file)
    #
    #     job_lock = DATA_PATH / f"outputs/{job_id}.lock"
    #     with open(job_lock, "w") as file:
    #         pass
    #
    #     try:
    #         prediction_df = pd.DataFrame()
    #         for family in target_family_list:
    #             with hydra.initialize(version_base="1.3", config_path="configs", job_name="webserver_inference"):
    #                 cfg = hydra.compose(
    #                     config_name="webserver_inference",
    #                     overrides=[f"task={task}",
    #                                f"preset={preset}",
    #                                f"ckpt_path=resources/checkpoints/{preset}-{task}-{family}.ckpt",
    #                                f"data.data_file='{str(predict_dataset)}'"])
    #
    #             predictions, _ = predict(cfg)
    #             predictions = [pd.DataFrame(prediction) for prediction in predictions]
    #             prediction_df = pd.concat([prediction_df, pd.concat(predictions, ignore_index=True)])
    #         prediction_df.to_csv(f'outputs/{job_id}.csv')
    #         # email_lock.unlink()
    #         job_lock.unlink()
    #
    #         msg = (f'Your DeepSEQcreen prediction job (id: {job_id}) completed successfully. You may retrieve the '
    #                f'results and generate an analytical report at {URL} using the job id within 48 hours.')
    #         gr.Info(msg)
    #     except Exception as e:
    #         msg = (f'Your DeepSEQcreen prediction job (id: {job_id}) failed due to an error: "{str(e)}." You may '
    #                f'reach out to the author about the error through email (DeepSEQreen@xjtlu.edu.cn).')
    #         raise gr.Error(str(e))
    #     finally:
    #         send_email(email, msg)
    #
    # # Run "predict" asynchronously
    # threading.Thread(target=run_predict).start()
    #
    # msg = (f'Your DeepSEQcreen prediction job (id: {job_id}) started running. You may retrieve the results '
    #        f'and generate an analytical report at {URL} using the job id once the job is done. Only one job '
    #        f'per user is allowed at the same time.')
    # send_email(email, msg)

    # # Return the job id first
    # return [
    #     gr.Blocks(visible=False),
    #     gr.Markdown(f"Your prediction job is running... "
    #                 f"You may stay on this page or come back later to retrieve the results "
    #                 f"Once you receive our email notification."),
    # ]


def update_df(file, progress=gr.Progress(track_tqdm=True)):
    # global DF_FOR_REPORT
    if file and Path(file).is_file():
        df = pd.read_csv(file)
        if 'N' in df.columns:
            df.set_index('N', inplace=True)
        if not any(col in ['X1', 'X2'] for col in df.columns):
            gr.Warning("At least one of columns `X1` and `X2` must be in the uploaded dataset.")
            return {analyze_btn: gr.Button(interactive=False)}
        # if df['X1'].nunique() > 1:
        if 'X1' in df.columns:
            df['Scaffold SMILES'] = df['X1'].swifter.progress_bar(
                desc=f"Calculating scaffold...").apply(MurckoScaffold.MurckoScaffoldSmilesFromSmiles)
            df['Scaffold'] = df['Scaffold SMILES'].swifter.progress_bar(
                desc='Generating scaffold graphs...').apply(
                lambda smiles: _MolPlusFingerprint(Chem.MolFromSmiles(smiles)))
            # Add a new column with RDKit molecule objects
            if 'Compound' not in df.columns or df['Compound'].dtype != 'object':
                df['Compound'] = df['X1'].swifter.progress_bar(
                    desc='Generating molecular graphs...').apply(
                    lambda smiles: _MolPlusFingerprint(Chem.MolFromSmiles(smiles)))

        # DF_FOR_REPORT = df.copy()

        # pie_chart = None
        # value = None
        # if 'Y^' in DF_FOR_REPORT.columns:
        #     value = 'Y^'
        # elif 'Y' in DF_FOR_REPORT.columns:
        #     value = 'Y'

        # if value:
        #     if DF_FOR_REPORT['X1'].nunique() > 1 >= DF_FOR_REPORT['X2'].nunique():
        #         pie_chart = create_pie_chart(DF_FOR_REPORT, category='Scaffold SMILES', value=value, top_k=100)
        #     elif DF_FOR_REPORT['X2'].nunique() > 1 >= DF_FOR_REPORT['X1'].nunique():
        #         pie_chart = create_pie_chart(DF_FOR_REPORT, category='Target family', value=value, top_k=100)

        return {html_report: create_html_report(df),
                raw_df: df,
                report_df: df.copy(),
                analyze_btn: gr.Button(interactive=True)}  # pie_chart
    else:
        return {analyze_btn: gr.Button(interactive=False)}


def create_html_report(df, file=None, task=None, progress=gr.Progress(track_tqdm=True)):
    df_html = df.copy(deep=True)
    # email_hash = hashlib.sha256(email.encode()).hexdigest()

    cols_left = list(pd.Index(
        ['ID1', 'Compound', 'Scaffold', 'Scaffold SMILES', 'ID2', 'Y', 'Y^']).intersection(df_html.columns))
    cols_right = list(pd.Index(['X1', 'X2']).intersection(df_html.columns))
    df_html = df_html[cols_left + (df_html.columns.drop(cols_left + cols_right).tolist()) + cols_right]

    if isinstance(task, str):
        task = TASK_MAP[task]
        COLUMN_ALIASES.update({
            'Y': 'Actual Interaction Probability' if task == 'DTI' else 'Actual Binding Affinity',
            'Y^': 'Predicted Interaction Probability' if task == 'DTI' else 'Predicted Binding Affinity'
        })

    ascending = True if COLUMN_ALIASES['Y^'] == 'Predicted Binding Affinity' else False
    df_html = df_html.sort_values(
        [col for col in ['Y', 'Y^'] if col in df_html.columns], ascending=ascending
    )

    if not file:
        df_html = df_html.iloc[:31]

    # Remove repeated info for one-against-N tasks to save visual and physical space
    job = 'Chemical Property'
    unique_entity = 'Unique Entity'
    unique_df = None
    category = None
    columns_unique = None
    if 'X1' in df_html.columns and 'X2' in df_html.columns:
        n_compound = df_html['X1'].nunique()
        n_protein = df_html['X2'].nunique()

        if n_compound == 1 and n_protein >= 2:
            unique_entity = 'Compound of Interest'
            if any(col in df_html.columns for col in ['Y^', 'Y']):
                job = 'Target Protein Identification'
                category = 'Target Family'
            columns_unique = df_html.columns.isin(['X1', 'ID1', 'Scaffold', 'Compound', 'Scaffold SMILES']
                                                  + list(FILTER_MAP.keys()) + list(SCORE_MAP.keys()))

        elif n_compound >= 2 and n_protein == 1:
            unique_entity = 'Target of Interest'
            if any(col in df_html.columns for col in ['Y^', 'Y']):
                job = 'Drug Hit Screening'
                category = 'Scaffold SMILES'
            columns_unique = df_html.columns.isin(['X2', 'ID2'])

        elif 'Y^' in df_html.columns:
            job = 'Interaction Pair Inference'
    if 'Compound' in df_html.columns:
        df_html['Compound'] = df_html['Compound'].swifter.progress_bar(
            desc='Generating compound graph...').apply(
            lambda x: PandasTools.PrintAsImageString(x) if not pd.isna(x) else x)
    if 'Scaffold' in df_html.columns:
        df_html['Scaffold'] = df_html['Scaffold'].swifter.progress_bar(
            desc='Generating scaffold graph...').apply(
            lambda x: PandasTools.PrintAsImageString(x) if not pd.isna(x) else x)

    df_html.rename(columns=COLUMN_ALIASES, inplace=True)
    df_html.index.name = 'Index'
    if 'Target FASTA' in df_html.columns:
        df_html['Target FASTA'] = df_html['Target FASTA'].swifter.progress_bar(
            desc='Processing FASTA...').apply(
            lambda x: wrap_text(x) if not pd.isna(x) else x)

    num_cols = df_html.select_dtypes('number').columns
    num_col_colors = sns.color_palette('husl', len(num_cols))
    bool_cols = df_html.select_dtypes(bool).columns
    bool_col_colors = {True: 'lightgreen', False: 'lightpink'}

    if columns_unique is not None:
        unique_df = df_html.loc[:, columns_unique].iloc[[0]].copy()
        df_html = df_html.loc[:, ~columns_unique]

    if not file:
        if 'Compound ID' in df_html.columns:
            df_html.drop(['Compound SMILES'], axis=1, inplace=True)
        if 'Target ID' in df_html.columns:
            df_html.drop(['Target FASTA'], axis=1, inplace=True)
        if 'Target FASTA' in df_html.columns:
            df_html['Target FASTA'] = df_html['Target FASTA'].swifter.progress_bar(
                desc='Processing FASTA...').apply(
                lambda x: wrap_text(x) if not pd.isna(x) else x)
        if 'Scaffold SMILES' in df_html.columns:
            df_html.drop(['Scaffold SMILES'], axis=1, inplace=True)
        styled_df = df_html.style.format(precision=3)

        for i, col in enumerate(num_cols):
            if col in df_html.columns:
                if col not in ['Predicted Binding Affinity', 'Actual Binding Affinity']:
                    styled_df = styled_df.background_gradient(
                        subset=[col], cmap=sns.light_palette(num_col_colors[i], as_cmap=True))
                else:
                    styled_df = styled_df.background_gradient(
                        subset=[col], cmap=sns.light_palette(num_col_colors[i], as_cmap=True).reversed())

        if any(df_html.columns.isin(bool_cols)):
            styled_df.applymap(lambda val: f'background-color: {bool_col_colors[val]}', subset=bool_cols)

        table_html = styled_df.to_html()
        unique_html = ''
        if unique_df is not None:
            if 'Target FASTA' in unique_df.columns:
                unique_df['Target FASTA'] = unique_df['Target FASTA'].str.replace('\n', '<br>')
            if any(unique_df.columns.isin(bool_cols)):
                unique_df = unique_df.style.applymap(
                    lambda val: f"background-color: {bool_col_colors[val]}", subset=bool_cols)
            unique_html = (f'<div style="font-family: Courier !important;">'
                           f'{unique_df.to_html(escape=False, index=False)}</div>')

        return (f'<div style="font-size: 16px; font-weight: bold;">{job} Report Preview (Top 30 Records)</div>'
                f'<div style="overflow-x:auto; font-family: Courier !important;">{unique_html}</div>'
                f'<div style="overflow:auto; height: 300px; font-family: Courier !important;">{table_html}</div>')

    else:
        bool_formatters = {col: BooleanFormatter() for col in bool_cols}
        float_formatters = {col: NumberFormatter(format='0.000') for col in df_html.select_dtypes('floating').columns}
        other_formatters = {
            'Predicted Interaction Probability': {'type': 'progress', 'max': 1.0, 'legend': True},
            'Actual Interaction Probability': {'type': 'progress', 'max': 1.0, 'legend': True},
            'Compound': HTMLTemplateFormatter(template='<div class="image-zoom-viewer"><%= value %></div>'),
            'Scaffold': HTMLTemplateFormatter(template='<div class="image-zoom-viewer"><%= value %></div>'),
            'Target FASTA': {'type': 'textarea', 'width': 60},
            'Target ID': HTMLTemplateFormatter(
                template='<a href="<% '
                         'if (/^[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}$/.test(value)) '
                         '{ %>https://www.uniprot.org/uniprotkb/<%= value %><% } '
                         'else { %>https://www.uniprot.org/uniprotkb?query=<%= value %><% } '
                         '%>" target="_blank"><%= value %></a>'),
            'Compound ID': HTMLTemplateFormatter(
                template='<a href="https://pubchem.ncbi.nlm.nih.gov/compound/<%= value %>" '
                         'target="_blank"><%= value %></a>')
        }
        formatters = {**bool_formatters, **float_formatters, **other_formatters}

        # html = df.to_html(file)
        # return html

        report_table = pn.widgets.Tabulator(
            df_html, formatters=formatters,
            frozen_columns=['Index', 'Target ID', 'Compound ID', 'Compound', 'Scaffold'],
            disabled=True, sizing_mode='stretch_both', pagination='local', page_size=30)

        for i, col in enumerate(num_cols):
            if col not in ['Predicted Binding Affinity', 'Actual Binding Affinity']:
                if col not in ['Predicted Interaction Probability', 'Actual Interaction Probability']:
                    report_table.style.background_gradient(
                        subset=df_html.columns == col, cmap=sns.light_palette(num_col_colors[i], as_cmap=True))
                else:
                    continue
            else:
                report_table.style.background_gradient(
                    subset=df_html.columns == col, cmap=sns.light_palette(num_col_colors[i], as_cmap=True).reversed())

        pie_charts = {}
        for y in df_html.columns.intersection(['Predicted Interaction Probability', 'Actual Interaction Probability',
                                               'Predicted Binding Affinity', 'Actual Binding Affinity']):
            pie_charts[y] = []
            for k in [10, 30, 100]:
                if k < len(df_html):
                    pie_charts[y].append(create_pie_chart(df_html, category=category, value=y, top_k=k))
            pie_charts[y].append(create_pie_chart(df_html, category=category, value=y, top_k=len(df_html)))

        # Remove keys with empty values
        pie_charts = {k: v for k, v in pie_charts.items() if any(v)}

        pn_css = """
        .tabulator {
            font-family: Courier New !important;
            font-weight: normal !important;
            font-size: 12px !important;
        }
        
        .tabulator-cell {
            overflow: visible !important;
        }
        
        .tabulator-cell:hover {
            z-index: 1000 !important;
        }
        
        .tabulator-cell.tabulator-frozen:hover {
            z-index: 1000 !important;
        }
        
        .image-zoom-viewer {
            display: inline-block;
            overflow: visible; 
            z-index: 1000;
        }
        
        .image-zoom-viewer::after {
            content: "";
            top: 0;
            left: 0;
            width: 100%;
            height: 100%;
            pointer-events: none;
        }
        
        .image-zoom-viewer:hover::after {
            pointer-events: all;
        }
        
        /* When hovering over the container, scale its child (the SVG) */
        .tabulator-cell:hover .image-zoom-viewer svg {
            padding: 3px;
            position: absolute; 
            background-color: rgba(250, 250, 250, 0.854);
            box-shadow: 0 0 10px rgba(0, 0, 0, 0.618);
            border-radius: 3px;
            transform: scale(3); /* Scale up the SVG */
            transition: transform 0.3s ease;
            pointer-events: none; /* Prevents the SVG from blocking mouse interactions */
            z-index: 1000;
        }
        
        .image-zoom-viewer svg {
            display: block; /* SVG is a block-level element for proper scaling */
            z-index: 1000;
        }
        
        .image-zoom-viewer:hover {
            z-index: 1000;
        }
        
        """

        pn.extension(raw_css=[pn_css])

        template = pn.template.VanillaTemplate(
            title=f'DeepSEQreen {job} Report',
            sidebar=[],
            favicon='deepseqreen.svg',
            logo='deepseqreen.svg',
            header_background='#F3F5F7',
            header_color='#4372c4',
            busy_indicator=None,
        )

        stats_pane = pn.Row()
        if unique_df is not None:
            unique_table = pn.widgets.Tabulator(unique_df, formatters=formatters, sizing_mode='stretch_width',
                                                show_index=False, disabled=True,
                                                frozen_columns=['Compound ID', 'Compound', 'Scaffold'])
            # if pie_charts:
            #     unique_table.width = 640
            stats_pane.append(pn.Column(f'### {unique_entity}', unique_table))
        if pie_charts:
            for score_name, figure_list in pie_charts.items():
                stats_pane.append(
                    pn.Column(f'### {category} by Top {score_name}',
                              pn.Tabs(*figure_list, tabs_location='above'))
                    # pn.Card(pn.Row(v), title=f'{category} by Top {k}')
                )

        if stats_pane:
            template.main.append(pn.Card(stats_pane,
                                         sizing_mode='stretch_width', title='Summary Statistics', margin=10))

        template.main.append(
            pn.Card(report_table, title=f'{job} Results',  # width=1200,
                    margin=10)
        )

        template.save(file, resources=INLINE)
        return file


def create_pie_chart(df, category, value, top_k):
    if category not in df or value not in df:
        return
    top_k_df = df.nlargest(top_k, value)
    category_counts = top_k_df[category].value_counts()
    data = pd.DataFrame({category: category_counts.index, 'value': category_counts.values})

    data['proportion'] = data['value'] / data['value'].sum()
    # Merge rows with proportion less than 0.2% into one row
    mask = data['proportion'] < 0.002
    if any(mask):
        merged_row = data[mask].sum()
        merged_row[category] = '...'
        data = pd.concat([data[~mask], pd.DataFrame(merged_row).T])
    data['angle'] = data['proportion'] * 2 * pi

    color_dict = {cat: color for cat, color in
                  zip(df[category].unique(),
                      (Category20c_20 * (len(df[category].unique()) // 20 + 1))[:len(df[category].unique())])}
    color_dict['...'] = '#636363'
    data['color'] = data[category].map(color_dict)

    tooltips = [
        (f"{category}", f"@{{{category}}}"),
        ("Count", "@value"),
        ("Percentage", "@proportion{0.0%}")
    ]

    if category == 'Scaffold SMILES':
        data = data.merge(top_k_df[['Scaffold SMILES', 'Scaffold']].drop_duplicates(), how='left',
                          left_on='Scaffold SMILES', right_on='Scaffold SMILES')
        tooltips.append(("Scaffold", "<div>@{Scaffold}{safe}</div>"))
    p = figure(height=384, width=960, name=f"Top {top_k}" if top_k < len(df) else 'All', sizing_mode='stretch_height',
               toolbar_location=None, tools="hover", tooltips=tooltips, x_range=(-0.4, 0.4))

    def truncate_label(label, max_length=60):
        return label if len(label) <= max_length else label[:max_length] + "..."

    data['legend_field'] = data[category].apply(truncate_label)

    p.add_layout(Legend(padding=0, margin=0), 'right')
    p.wedge(x=0, y=1, radius=0.3,
            start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
            line_color="white", fill_color='color', legend_field='legend_field', source=data)

    # Limit the number of legend items to 20 and add "..." if there are more than 20 items
    if len(p.legend.items) > 20:
        new_legend_items = p.legend.items[:20]
        new_legend_items.append(LegendItem(label="..."))
        p.legend.items = new_legend_items

    p.legend.label_text_font_size = "10pt"
    p.legend.label_text_font="courier"
    p.axis.axis_label = None
    p.axis.visible = False
    p.grid.grid_line_color = None
    p.outline_line_width = 0
    p.min_border = 0
    p.margin = 0

    return p


def submit_report(df, score_list, filter_list, task, progress=gr.Progress(track_tqdm=True)):
    df_report = df.copy()
    try:
        for filter_name in filter_list:
            df_report[filter_name] = df_report['Compound'].swifter.progress_bar(
                desc=f"Calculating {filter_name}").apply(
                lambda x: FILTER_MAP[filter_name](x) if not pd.isna(x) else x)

        for score_name in score_list:
            df_report[score_name] = df_report['Compound'].swifter.progress_bar(
                desc=f"Calculating {score_name}").apply(
                lambda x: SCORE_MAP[score_name](x) if not pd.isna(x) else x)

        # pie_chart = None
        # value = None
        # if 'Y^' in df.columns:
        #     value = 'Y^'
        # elif 'Y' in df.columns:
        #     value = 'Y'
        #
        # if value:
        #     if df['X1'].nunique() > 1 >= df['X2'].nunique():
        #         pie_chart = create_pie_chart(df, category='Scaffold SMILES', value=value, top_k=100)
        #     elif df['X2'].nunique() > 1 >= df['X1'].nunique():
        #         pie_chart = create_pie_chart(df, category='Target family', value=value, top_k=100)

        return (create_html_report(df_report, file=None, task=task), df_report,
                gr.File(visible=False), gr.File(visible=False))

    except Exception as e:
        gr.Warning(f'Failed to report results due to error: {str(e)}')
        return None, None, None, None


# def check_job_status(job_id):
#     job_lock = DATA_PATH / f"{job_id}.lock"
#     job_file = DATA_PATH / f"{job_id}.csv"
#     if job_lock.is_file():
#         return {gr.Markdown(f"Your job ({job_id}) is still running... "
#                             f"You may stay on this page or come back later to retrieve the results "
#                             f"Once you receive our email notification."),
#                 None,
#                 None
#                 }
#     elif job_file.is_file():
#         return {gr.Markdown(f"Your job ({job_id}) is done! Redirecting you to generate reports..."),
#                 gr.Tabs(selected=3),
#                 gr.File(str(job_lock))}


def wrap_text(text, line_length=60):
    if isinstance(text, str):
        wrapper = textwrap.TextWrapper(width=line_length)
        if text.startswith('>'):
            sections = text.split('>')
            wrapped_sections = []
            for section in sections:
                if not section:
                    continue
                lines = section.split('\n')
                seq_header = lines[0]
                wrapped_seq = wrapper.fill(''.join(lines[1:]))
                wrapped_sections.append(f">{seq_header}\n{wrapped_seq}")
            return '\n'.join(wrapped_sections)
        else:
            return wrapper.fill(text)
    else:
        return text


def unwrap_text(text):
    return text.strip.replece('\n', '')


def drug_library_from_sdf(sdf_path):
    return PandasTools.LoadSDF(
        sdf_path,
        smilesName='X1', molColName='Compound', includeFingerprints=True
    )


def process_target_library_upload(library_upload):
    if library_upload.endswith('.csv'):
        df = pd.read_csv(library_upload)
    elif library_upload.endswith('.fasta'):
        df = target_library_from_fasta(library_upload)
    else:
        raise gr.Error('Currently only CSV and FASTA files are supported as target libraries.')
    validate_columns(df, ['X2'])
    return df


def process_drug_library_upload(library_upload):
    if library_upload.endswith('.csv'):
        df = pd.read_csv(library_upload)
    elif library_upload.endswith('.sdf'):
        df = drug_library_from_sdf(library_upload)
    else:
        raise gr.Error('Currently only CSV and SDF files are supported as drug libraries.')
    validate_columns(df, ['X1'])
    return df


def target_library_from_fasta(fasta_path):
    records = list(SeqIO.parse(fasta_path, "fasta"))
    id2 = [record.id for record in records]
    seq = [str(record.seq) for record in records]
    df = pd.DataFrame({'ID2': id2, 'X2': seq})
    return df


theme = gr.themes.Base(spacing_size="sm", text_size='md').set(
    background_fill_primary='#dfe6f0',
    background_fill_secondary='#dfe6f0',
    checkbox_label_background_fill='#dfe6f0',
    checkbox_label_background_fill_hover='#dfe6f0',
    checkbox_background_color='white',
    checkbox_border_color='#4372c4',
    border_color_primary='#4372c4',
    border_color_accent='#4372c4',
    button_primary_background_fill='#4372c4',
    button_primary_text_color='white',
    button_secondary_border_color='#4372c4',
    body_text_color='#4372c4',
    block_title_text_color='#4372c4',
    block_label_text_color='#4372c4',
    block_info_text_color='#505358',
    block_border_color=None,
    input_border_color='#4372c4',
    panel_border_color='#4372c4',
    input_background_fill='white',
    code_background_fill='white',
)

with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS) as demo:
    run_state = gr.State(value=False)
    screen_flag = gr.State(value=False)
    identify_flag = gr.State(value=False)
    infer_flag = gr.State(value=False)
    report_upload_flag = gr.State(value=False)

    with gr.Tabs() as tabs:
        with gr.TabItem(label='Drug Hit Screening', id=0):
            gr.Markdown('''
                # <center>Drug Hit Screening</center>
                <center>
                To predict interactions or binding affinities of a single target against a compound library.
                </center>
                    ''')
            with gr.Blocks() as screen_block:
                with gr.Column() as screen_page:
                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "Enter (paste) a amino acid sequence below manually or upload a FASTA file. "
                                "If multiple entities are in the FASTA, only the first will be used. "
                                "Alternatively, enter a Uniprot ID or gene symbol with organism and click Query for "
                                "the sequence."
                            )
                            target_input_type = gr.Dropdown(
                                label='Step 1. Select Target Input Type and Input',
                                choices=['Sequence', 'UniProt ID', 'Gene symbol'],
                                info='Enter (paste) a FASTA string below manually or upload a FASTA file.',
                                value='Sequence',
                                scale=4, interactive=True
                            )

                    with gr.Row():
                        target_id = gr.Textbox(show_label=False, visible=False,
                                               interactive=True, scale=4,
                                               info='Enter a UniProt ID and query.')
                        target_gene = gr.Textbox(
                            show_label=False, visible=False,
                            interactive=True, scale=4,
                            info='Enter a gene symbol and query.')
                        target_organism = gr.Textbox(
                            info='Organism scientific name (default: Homo sapiens).',
                            placeholder='Homo sapiens', show_label=False,
                            visible=False, interactive=True, scale=4, )
                        target_upload_btn = gr.UploadButton(label='Upload a FASTA File', type='binary',
                                                            visible=True, variant='primary',
                                                            size='lg')
                        target_paste_markdown = gr.Button(value='OR Paste Your Sequence Below', visible=True)
                        target_query_btn = gr.Button(value='Query the Sequence', variant='primary',
                                                     visible=False, scale=4)
                    # with gr.Row():
                    #     example_uniprot = gr.Button(value='Example: Q16539', elem_classes='example', visible=False)
                    #     example_gene = gr.Button(value='Example: MAPK14', elem_classes='example', visible=False)
                    example_fasta = gr.Button(value='Example: MAPK14 (Q16539)', elem_classes='example')
                    target_fasta = gr.Code(label='Input or Display FASTA', interactive=True, lines=5)
                    # with gr.Row():
                    #     with gr.Column():
                    # with gr.Column():
                    #     gr.File(label='Example FASTA file',
                    #             value='data/examples/MAPK14.fasta', interactive=False)

                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "Click Auto-detect to identify the protein family using sequence alignment. "
                                "This optional step allows applying a family-specific model instead of a all-family "
                                "model (general). "
                                "Manually select general if the alignment results are unsatisfactory."
                            )
                            drug_screen_target_family = gr.Dropdown(
                                choices=list(TARGET_FAMILY_MAP.keys()),
                                value='General',
                                label='Step 2. Select Target Family (Optional)', interactive=True)
                            # with gr.Column(scale=1, min_width=24):

                    with gr.Row():
                        with gr.Column():
                            target_family_detect_btn = gr.Button(value='OR Let Us Auto-Detect for You',
                                                                 variant='primary')

                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "Select a preset compound library (e.g., DrugBank). "
                                "Alternatively, upload a CSV file with a column named X1 containing compound SMILES, "
                                "or use an SDF file (Max. 10,000 compounds per task). Example CSV and SDF files are "
                                "provided below and can be downloaded by clicking the lower right corner."
                            )
                            drug_library = gr.Dropdown(label='Step 3. Select a Preset Compound Library',
                                                       choices=list(DRUG_LIBRARY_MAP.keys()))
                            with gr.Row():
                                gr.File(label='Example SDF compound library',
                                        value='data/examples/compound_library.sdf', interactive=False)
                                gr.File(label='Example CSV compound library',
                                        value='data/examples/compound_library.csv', interactive=False)
                            drug_library_upload_btn = gr.UploadButton(
                                label='OR Upload Your Own Library', variant='primary')
                            drug_library_upload = gr.File(label='Custom compound library file', visible=False)
                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "Interaction prediction provides you binding probability score between the target of "
                                "interest and each compound in the library, "
                                "while affinity prediction directly estimates their binding strength measured using "
                                "IC50."
                            )
                            drug_screen_task = gr.Dropdown(
                                list(TASK_MAP.keys()),
                                label='Step 4. Select the Prediction Task You Want to Conduct',
                                value='Compound-protein interaction')

                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "Select your preferred model, or click Recommend for the best-performing model based "
                                "on the selected task, family, and whether the target was trained. "
                                "Please refer to documentation for detailed benchamrk results."
                            )
                            drug_screen_preset = gr.Dropdown(list(PRESET_MAP.keys()),
                                                             label='Step 5. Select a Preset Model')
                            screen_preset_recommend_btn = gr.Button(
                                value='OR Let Us Recommend for You', variant='primary')
                    with gr.Row():
                        with gr.Column():
                            drug_screen_email = gr.Textbox(
                                label='Step 6. Input Your Email Address (Optional)',
                                info="Your email address will be used to notify you about the completion of your job."
                            )

                    with gr.Row(visible=True):
                        with gr.Column():
                            # drug_screen_clr_btn = gr.ClearButton(size='lg')
                            drug_screen_btn = gr.Button(value='SUBMIT THE SCREENING JOB', variant='primary', size='lg')
                    # TODO Modify the pd df directly with df['X2'] = target

            screen_data_for_predict = gr.File(visible=False, file_count="single", type='filepath')
            screen_waiting = gr.Markdown("""
            <center>Your job is running... It might take a few minutes.
            When it's done, you will be redirected to the report page.
            Meanwhile, please leave the page on.</center>
            """, visible=False)

        with gr.TabItem(label='Target protein identification', id=1):
            gr.Markdown('''
                # <center>Target Protein Identification</center>
                
                <center>
                To predict interactions or binding affinities of a single compound against a protein library.
                </center>
                ''')
            with gr.Blocks() as identify_block:
                with gr.Column() as identify_page:
                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "Enter (paste) a compound SMILES below manually or upload a SDF file. "
                                "If multiple entities are in the SDF, only the first will be used. "
                                "SMILES can be obtained by searching for the compound of interest in databases such "
                                "as NCBI, PubChem and and ChEMBL."
                            )
                            compound_type = gr.Dropdown(
                                label='Step 1. Select Compound Input Type and Input',
                                choices=['SMILES', 'SDF'],
                                info='Enter (paste) an SMILES string or upload an SDF file to convert to SMILES.',
                                value='SMILES',
                                interactive=True)
                            compound_upload_btn = gr.UploadButton(label='OR Upload a SDF File', variant='primary',
                                                                  type='binary', visible=False)

                    compound_smiles = gr.Code(label='Input or Display Compound SMILES', interactive=True, lines=5)
                    example_drug = gr.Button(value='Example: Aspirin', elem_classes='example')

                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "By default, models trained on all protein families (general) will be applied. "
                                # "If the proteins in the target library of interest all belong to the same protein "
                                # "family, manually selecting the family is supported."
                            )
                            target_identify_target_family = gr.Dropdown(choices=['General'],
                                                                        value='General',
                                                                        label='Step 2. Select Target Family ('
                                                                              'Optional)')

                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "Select a preset target library (e.g., ChEMBL33_human_proteins). "
                                "Alternatively, upload a CSV file with a column named X2 containing target protein "
                                "sequences, or use an FASTA file (Max. 10,000 targets per task). "
                                "Example CSV and SDF files are provided below "
                                "and can be downloaded by clicking the lower right corner."
                            )
                            target_library = gr.Dropdown(label='Step 3. Select a Preset Target Library',
                                                         choices=list(TARGET_LIBRARY_MAP.keys()))
                            with gr.Row():
                                gr.File(label='Example FASTA target library',
                                        value='data/examples/target_library.fasta', interactive=False)
                                gr.File(label='Example CSV target library',
                                        value='data/examples/target_library.csv', interactive=False)
                            target_library_upload_btn = gr.UploadButton(
                                label='OR Upload Your Own Library', variant='primary')
                            target_library_upload = gr.File(label='Custom target library file', visible=False)

                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "Interaction prediction provides you binding probability score between the target of "
                                "interest and each compound in the library, "
                                "while affinity prediction directly estimates their binding strength measured using "
                                "IC50."
                            )
                            target_identify_task = gr.Dropdown(
                                list(TASK_MAP.keys()),
                                label='Step 4. Select the Prediction Task You Want to Conduct',
                                value='Compound-protein interaction')

                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "Select your preferred model, or click Recommend for the best-performing model based "
                                "on the selected task, family, and whether the compound was trained. "
                                "Please refer to documentation for detailed benchamrk results."
                            )
                            target_identify_preset = gr.Dropdown(list(PRESET_MAP.keys()),
                                                                 label='Step 5. Select a Preset Model')
                            identify_preset_recommend_btn = gr.Button(value='OR Let Us Recommend for You',
                                                                      variant='primary')

                    with gr.Row():
                        with gr.Column():
                            target_identify_email = gr.Textbox(
                                label='Step 6. Input Your Email Address (Optional)',
                                info="Your email address will be used to notify you about the completion of your job."
                            )

                    with gr.Row(visible=True):
                        # target_identify_clr_btn = gr.ClearButton(size='lg')
                        target_identify_btn = gr.Button(value='SUBMIT THE IDENTIFICATION JOB', variant='primary',
                                                        size='lg')

            identify_data_for_predict = gr.File(visible=False, file_count="single", type='filepath')
            identify_waiting = gr.Markdown(f"Your job is running... It might take a few minutes."
                                           f"When it's done, you will be redirected to the report page. "
                                           f"Meanwhile, please leave the page on.",
                                           visible=False)
        with gr.TabItem(label='Interaction pair inference', id=2):
            gr.Markdown('''
# <center>Interaction Pair Inference</center>
<center>To predict interactions or binding affinities between up to 10,000 paired compound-protein data.</center>
''')
            with gr.Blocks() as infer_block:
                with gr.Column() as infer_page:
                    HelpTip(
                        "A custom interation pair dataset can be a CSV file with 2 required columns "
                        "(X1 for smiles and X2 for sequences) "
                        "and optionally 2 ID columns (ID1 for compound ID and ID2 for target ID), "
                        "or generated from a FASTA file containing multiple "
                        "sequences and a SDF file containing multiple compounds. "
                        "Currently, a maximum of 10,000 pairs is supported, "
                        "which means that the size of CSV file or "
                        "the product of the two library sizes should not exceed 10,000."
                    )
                    infer_type = gr.Dropdown(
                        choices=['Upload a CSV file containing paired compound-protein data',
                                 'Upload a compound library and a target library'],
                        label='Step 1. Select Pair Input Type and Input',
                        value='Upload a CSV file containing paired compound-protein data')
                    with gr.Column() as pair_upload:
                        gr.File(label="Example CSV dataset",
                                value="data/examples/interaction_pair_inference.csv",
                                interactive=False)
                        with gr.Row():
                            infer_csv_prompt = gr.Button(value="Upload Your Own Dataset Below",
                                                         visible=True)
                        with gr.Column():
                            infer_data_for_predict = gr.File(
                                label='Upload CSV File Containing Paired Records',
                                file_count="single", type='filepath', visible=True)
                    with gr.Column(visible=False) as pair_generate:
                        with gr.Row():
                            gr.File(label='Example SDF compound library',
                                    value='data/examples/compound_library.sdf', interactive=False)
                            gr.File(label='Example FASTA target library',
                                    value='data/examples/target_library.fasta', interactive=False)
                        with gr.Row():
                            gr.File(label='Example CSV compound library',
                                    value='data/examples/compound_library.csv', interactive=False)
                            gr.File(label='Example CSV target library',
                                    value='data/examples/target_library.csv', interactive=False)
                        with gr.Row():
                            infer_library_prompt = gr.Button(value="Upload Your Own Libraries Below",
                                                             visible=False)
                        with gr.Row():
                            infer_drug = gr.File(label='Upload SDF/CSV File Containing Multiple Compounds',
                                                 file_count="single", type='filepath')
                            infer_target = gr.File(label='Upload FASTA/CSV File Containing Multiple Targets',
                                                   file_count="single", type='filepath')

                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "By default, models trained on all protein families (general) will be applied. "
                                "If the proteins in the target library of interest "
                                "all belong to the same protein family, manually selecting the family is supported."
                            )
                            pair_infer_target_family = gr.Dropdown(choices=list(TARGET_FAMILY_MAP.keys()),
                                                                   value='General',
                                                                   label='Step 2. Select Target Family (Optional)')

                    with gr.Row():
                        with gr.Column():
                            HelpTip(
                                "Interaction prediction provides you binding probability score "
                                "between the target of interest and each compound in the library, "
                                "while affinity prediction directly estimates their binding strength "
                                "measured using IC50."
                            )
                            pair_infer_task = gr.Dropdown(
                                list(TASK_MAP.keys()),
                                label='Step 3. Select the Prediction Task You Want to Conduct',
                                value='Compound-protein interaction')

                    with gr.Row():
                        with gr.Column():
                            HelpTip("Select your preferred model. "
                                    "Please refer to documentation for detailed benchmark results."
                                    )
                            pair_infer_preset = gr.Dropdown(list(PRESET_MAP.keys()),
                                                            label='Step 4. Select a Preset Model')
                            # infer_preset_recommend_btn = gr.Button(value='OR Let Us Recommend for You',
                            #                                        variant='primary')

                    with gr.Row():
                        pair_infer_email = gr.Textbox(
                            label='Step 5. Input Your Email Address (Optional)',
                            info="Your email address will be used to notify you about the completion of your job."
                        )

                    with gr.Row(visible=True):
                        # pair_infer_clr_btn = gr.ClearButton(size='lg')
                        pair_infer_btn = gr.Button(value='SUBMIT THE INFERENCE JOB', variant='primary', size='lg')

            infer_waiting = gr.Markdown(f"Your job is running... It might take a few minutes."
                                        f"When it's done, you will be redirected to the report page. "
                                        f"Meanwhile, please leave the page on.",
                                        visible=False)

        with gr.TabItem(label='Chemical property report', id=3):
            with gr.Blocks() as report:
                gr.Markdown('''
                # <center>Chemical Property Report</center>
          
                To compute chemical properties for the predictions of drug hit screening, 
                target protein identification, and interaction pair inference. 
                
                You may also upload your own dataset using a CSV file containing 
                one required column `X1` for compound SMILES. 
                
                The page shows only a preview report displaying at most 30 records 
                (with top predicted CPI/CPA if reporting results from a prediction job). 
                
                Please first `Preview` the report, then `Generate` and download a CSV report 
                or an interactive HTML report below if you wish to access the full report.
                ''')
                with gr.Row():
                    with gr.Column():
                        file_for_report = gr.File(interactive=True, type='filepath')
                        report_task = gr.Dropdown(list(TASK_MAP.keys()), visible=False, value=None,
                                                  label='Specify the Task for the Labels in the Upload Dataset')
                    raw_df = gr.State(value=pd.DataFrame())
                    report_df = gr.State(value=pd.DataFrame())
                    scores = gr.CheckboxGroup(list(SCORE_MAP.keys()), label='Scores')
                    filters = gr.CheckboxGroup(list(FILTER_MAP.keys()), label='Filters')

                with gr.Row():
                    # clear_btn = gr.ClearButton(size='lg')
                    analyze_btn = gr.Button('Preview Top 30 Records', variant='primary', size='lg',
                                            interactive=False)

                with gr.Row():
                    with gr.Column(scale=3):
                        html_report = gr.HTML()  # label='Results', visible=True)
                    ranking_pie_chart = gr.Plot(visible=False)

                with gr.Row():
                    with gr.Column():
                        csv_generate = gr.Button(value='Generate CSV Report',
                                                 interactive=False, variant='primary')
                        csv_download_file = gr.File(label='Download CSV Report', visible=False)
                    with gr.Column():
                        html_generate = gr.Button(value='Generate HTML Report',
                                                  interactive=False, variant='primary')
                        html_download_file = gr.File(label='Download HTML Report', visible=False)


    def target_input_type_select(input_type):
        match input_type:
            case 'UniProt ID':
                return [gr.Dropdown(info=''),
                        gr.UploadButton(visible=False),
                        gr.Textbox(visible=True, value=''),
                        gr.Textbox(visible=False, value=''),
                        gr.Textbox(visible=False, value=''),
                        gr.Button(visible=True),
                        gr.Code(value=''),
                        gr.Button(visible=False)]
            case 'Gene symbol':
                return [gr.Dropdown(info=''),
                        gr.UploadButton(visible=False),
                        gr.Textbox(visible=False, value=''),
                        gr.Textbox(visible=True, value=''),
                        gr.Textbox(visible=True, value=''),
                        gr.Button(visible=True),
                        gr.Code(value=''),
                        gr.Button(visible=False)]
            case 'Sequence':
                return [gr.Dropdown(info='Enter (paste) a FASTA string below manually or upload a FASTA file.'),
                        gr.UploadButton(visible=True),
                        gr.Textbox(visible=False, value=''),
                        gr.Textbox(visible=False, value=''),
                        gr.Textbox(visible=False, value=''),
                        gr.Button(visible=False),
                        gr.Code(value=''),
                        gr.Button(visible=True)]


    target_input_type.select(
        fn=target_input_type_select,
        inputs=target_input_type,
        outputs=[
            target_input_type, target_upload_btn,
            target_id, target_gene, target_organism, target_query_btn,
            target_fasta, target_paste_markdown
        ],
        show_progress=False
    )


    def uniprot_query(input_type, uid, gene, organism='Human'):
        fasta_seq = ''

        match input_type:
            case 'UniProt ID':
                query = f"{uid.strip()}.fasta"
            case 'Gene symbol':
                organism = organism if organism else 'Human'
                query = f'search?query=organism_name:{organism.strip()}+AND+gene:{gene.strip()}&format=fasta'

        try:
            fasta = SESSION.get(UNIPROT_ENDPOINT.format(query=query))
            fasta.raise_for_status()
            fasta_seq = fasta.text
        except Exception as e:
            raise gr.Warning(f"Failed to query FASTA from UniProt database due to {str(e)}")
        finally:
            return fasta_seq

    def process_fasta_upload(fasta_upload):
        fasta = ''
        try:
            fasta = fasta_upload.decode()
        except Exception as e:
            gr.Warning(f"Please upload a valid FASTA file. Error: {str(e)}")
            return fasta

    target_upload_btn.upload(fn=process_fasta_upload, inputs=target_upload_btn, outputs=target_fasta)
    target_query_btn.click(uniprot_query,
                           inputs=[target_input_type, target_id, target_gene, target_organism],
                           outputs=target_fasta)


    def target_family_detect(fasta, progress=gr.Progress(track_tqdm=True)):
        aligner = PairwiseAligner(scoring='blastp', mode='local')
        alignment_df = pd.read_csv('data/target_libraries/ChEMBL33_all_spe_single_prot_info.csv')

        def align_score(query):
            return aligner.align(process_target_fasta(fasta), query).score

        alignment_df['score'] = alignment_df['X2'].swifter.progress_bar(
            desc="Detecting protein family of the target...").apply(align_score)
        row = alignment_df.loc[alignment_df['score'].idxmax()]
        return gr.Dropdown(value=row['protein_family'].capitalize(),
                           info=f"Reason: Best BLASTP score ({row['score']}) "
                                f"with {row['ID2']} from family {row['protein_family']}")


    target_family_detect_btn.click(fn=target_family_detect, inputs=target_fasta, outputs=drug_screen_target_family)

    # target_fasta.focus(fn=wrap_text, inputs=target_fasta, outputs=target_fasta, show_progress=False)
    target_fasta.blur(fn=wrap_text, inputs=target_fasta, outputs=target_fasta, show_progress=False)

    drug_library_upload_btn.upload(fn=lambda x: [
        x.name, gr.Dropdown(value=Path(x.name).name, choices=list(DRUG_LIBRARY_MAP.keys()) + [Path(x.name).name])
    ], inputs=drug_library_upload_btn, outputs=[drug_library_upload, drug_library])


    def example_fill(input_type):
        return {target_id: 'Q16539',
                target_gene: 'MAPK14',
                target_organism: 'Human',
                target_fasta: """
>sp|Q16539|MK14_HUMAN Mitogen-activated protein kinase 14 OS=Homo sapiens OX=9606 GN=MAPK14 PE=1 SV=3
MSQERPTFYRQELNKTIWEVPERYQNLSPVGSGAYGSVCAAFDTKTGLRVAVKKLSRPFQ
SIIHAKRTYRELRLLKHMKHENVIGLLDVFTPARSLEEFNDVYLVTHLMGADLNNIVKCQ
KLTDDHVQFLIYQILRGLKYIHSADIIHRDLKPSNLAVNEDCELKILDFGLARHTDDEMT
GYVATRWYRAPEIMLNWMHYNQTVDIWSVGCIMAELLTGRTLFPGTDHIDQLKLILRLVG
TPGAELLKKISSESARNYIQSLTQMPKMNFANVFIGANPLAVDLLEKMLVLDSDKRITAA
QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
"""}


    example_fasta.click(fn=example_fill, inputs=target_input_type, outputs=[
        target_id, target_gene, target_organism, target_fasta], show_progress=False)


    # example_uniprot.click(fn=example_fill, inputs=target_input_type, outputs=target_fasta, show_progress=False)
    # example_gene.click(fn=example_fill, inputs=target_input_type, outputs=target_fasta, show_progress=False)

    def screen_recommend_model(fasta, family, task):
        task = TASK_MAP[task]
        if task == 'DTI':
            train = pd.read_csv('data/benchmarks/all_families_reduced_dti_train.csv')
            score = 'AUROC'
        elif task == 'DTA':
            train = pd.read_csv('data/benchmarks/all_families_reduced_dta_train.csv')
            score = 'CI'

        if not np.isin(process_target_fasta(fasta), train['X2']):
            scenario = "Unseen target"
        else:
            scenario = "Seen target"
        benchmark_df = pd.read_csv('data/benchmarks/compound_screen.csv')

        if family == 'General':
            filtered_df = benchmark_df[(benchmark_df[f'Task'] == task)
                                       & (benchmark_df['Target.family'] == 'All families reduced')
                                       & (benchmark_df['Scenario'] == 'Random split')
                                       & (benchmark_df['all'] == True)]
        else:
            filtered_df = benchmark_df[(benchmark_df['Task'] == task)
                                       & (benchmark_df['Target.family'] == family)
                                       & (benchmark_df['Scenario'] == scenario)
                                       & (benchmark_df['all'] == False)]
        row = filtered_df.loc[filtered_df[score].idxmax()]

        return gr.Dropdown(value=row['preset'],
                           info=f"Reason: {scenario} in the training dataset; we recommend the model "
                                f"with the best {score} ({float(row[score]):.3f}) "
                                f"in the {scenario.lower()} scenario on {family.lower()} family.")


    screen_preset_recommend_btn.click(fn=screen_recommend_model,
                                      inputs=[target_fasta, drug_screen_target_family, drug_screen_task],
                                      outputs=drug_screen_preset)


    def compound_input_type_select(input_type):
        match input_type:
            case 'SMILES':
                return gr.Button(visible=False)
            case 'SDF':
                return gr.Button(visible=True)


    compound_type.select(fn=compound_input_type_select,
                         inputs=compound_type, outputs=compound_upload_btn, show_progress=False)


    def compound_upload_process(input_type, input_upload):
        smiles = ''
        try:
            match input_type:
                case 'SMILES':
                    smiles = input_upload.decode()
                case 'SDF':
                    suppl = Chem.ForwardSDMolSupplier(io.BytesIO(input_upload))
                    smiles = Chem.MolToSmiles(next(suppl))
        except Exception as e:
            gr.Warning(f"Please upload a valid {input_type} file. Error: {str(e)}")
        return smiles


    compound_upload_btn.upload(fn=compound_upload_process,
                               inputs=[compound_type, compound_upload_btn],
                               outputs=compound_smiles)

    example_drug.click(fn=lambda: 'CC(=O)Oc1ccccc1C(=O)O', outputs=compound_smiles, show_progress=False)

    target_library_upload_btn.upload(fn=lambda x: [
        x.name, gr.Dropdown(value=Path(x.name).name, choices=list(TARGET_LIBRARY_MAP.keys()) + [Path(x.name).name])
    ], inputs=target_library_upload_btn, outputs=[target_library_upload, target_library])


    def identify_recommend_model(smiles, task):
        task = TASK_MAP[task]
        if task == 'DTI':
            train = pd.read_csv('data/benchmarks/all_families_reduced_dti_train.csv')
            score = 'AUROC'
        elif task == 'DTA':
            train = pd.read_csv('data/benchmarks/all_families_reduced_dta_train.csv')
            score = 'CI'
        if not np.isin(smiles, train['X1']):
            scenario = "Unseen drug"
        else:
            scenario = "Seen drug"
        benchmark_df = pd.read_csv('data/benchmarks/target_identification.csv')

        filtered_df = benchmark_df[(benchmark_df['Task'] == task)
                                   & (benchmark_df['Scenario'] == scenario)]
        row = filtered_df.loc[filtered_df[score].idxmax()]

        return gr.Dropdown(value=row['preset'],
                           info=f"Reason: {scenario} in the training dataset; choosing the model "
                                f"with the best {score} ({float(row[score]):3f}) "
                                f"in the {scenario.lower()} scenario.")


    identify_preset_recommend_btn.click(fn=identify_recommend_model,
                                        inputs=[compound_smiles, target_identify_task],
                                        outputs=target_identify_preset)


    def infer_type_change(upload_type):
        match upload_type:
            case "Upload a compound library and a target library":
                return {
                    pair_upload: gr.Column(visible=False),
                    pair_generate: gr.Column(visible=True),
                    infer_data_for_predict: None,
                    infer_drug: None,
                    infer_target: None,
                    infer_csv_prompt: gr.Button(visible=False),
                    infer_library_prompt: gr.Button(visible=True),
                }
        match upload_type:
            case "Upload a CSV file containing paired compound-protein data":
                return {
                    pair_upload: gr.Column(visible=True),
                    pair_generate: gr.Column(visible=False),
                    infer_data_for_predict: None,
                    infer_drug: None,
                    infer_target: None,
                    infer_csv_prompt: gr.Button(visible=True),
                    infer_library_prompt: gr.Button(visible=False),
                }


    infer_type.select(fn=infer_type_change, inputs=infer_type,
                      outputs=[pair_upload, pair_generate, infer_data_for_predict, infer_drug, infer_target,
                               infer_csv_prompt, infer_library_prompt])


    def drug_screen_validate(fasta, library, library_upload, state, progress=gr.Progress(track_tqdm=True)):
        if not state:
            try:
                fasta = process_target_fasta(fasta)
                err = validate_seq_str(fasta, FASTA_PAT)
                if err:
                    raise ValueError(f'Found error(s) in your target fasta input: {err}')
                if library in DRUG_LIBRARY_MAP.keys():
                    screen_df = pd.read_csv(Path('data/drug_libraries', DRUG_LIBRARY_MAP[library]))
                else:
                    screen_df = process_drug_library_upload(library_upload)
                    if len(screen_df) >= CUSTOM_DATASET_MAX_LEN:
                        raise gr.Error(f'The uploaded compound library has more records '
                                       f'than the allowed maximum (CUSTOM_DATASET_MAX_LEN).')

                screen_df['X2'] = fasta

                job_id = uuid4()
                temp_file = Path(f'temp/{job_id}_input.csv').resolve()
                screen_df.to_csv(temp_file, index=False)
                if temp_file.is_file():
                    return {screen_data_for_predict: str(temp_file),
                            screen_flag: job_id,
                            run_state: job_id}
                else:
                    raise SystemError('Failed to create temporary files. Please try again later.')
            except Exception as e:
                gr.Warning(f'Failed to submit the job due to error: {str(e)}')
                return {screen_flag: False,
                        run_state: False}
        else:
            gr.Warning('You have another prediction job '
                       '(drug hit screening, target protein identification, or interation pair inference) '
                       'running in the session right now. '
                       'Please submit another job when your current job has finished.')
            return {screen_flag: False,
                    run_state: state}


    def target_identify_validate(smiles, library, library_upload, state, progress=gr.Progress(track_tqdm=True)):
        if not state:
            try:
                smiles = smiles.strip()
                err = validate_seq_str(smiles, SMILES_PAT)
                if err:
                    raise ValueError(f'Found error(s) in your target fasta input: {err}')
                if library in TARGET_LIBRARY_MAP.keys():
                    identify_df = pd.read_csv(Path('data/target_libraries', TARGET_LIBRARY_MAP[library]))
                else:
                    identify_df = process_target_library_upload(library_upload)
                    if len(identify_df) >= CUSTOM_DATASET_MAX_LEN:
                        raise gr.Error(f'The uploaded target library has more records '
                                       f'than the allowed maximum (CUSTOM_DATASET_MAX_LEN).')
                identify_df['X1'] = smiles

                job_id = uuid4()
                temp_file = Path(f'temp/{job_id}_input.csv').resolve()
                identify_df.to_csv(temp_file, index=False)
                if temp_file.is_file():
                    return {identify_data_for_predict: str(temp_file),
                            identify_flag: job_id,
                            run_state: job_id}
                else:
                    raise SystemError('Failed to create temporary files. Please try again later.')
            except Exception as e:
                gr.Warning(f'Failed to submit the job due to error: {str(e)}')
                return {identify_flag: False,
                        run_state: False}
        else:
            gr.Warning('You have another prediction job '
                       '(drug hit screening, target protein identification, or interation pair inference) '
                       'running in the session right now. '
                       'Please submit another job when your current job has finished.')
            return {identify_flag: False,
                    run_state: state}
            # return {identify_flag: False}


    def pair_infer_validate(drug_target_pair_upload, drug_upload, target_upload, state,
                            progress=gr.Progress(track_tqdm=True)):
        if not state:
            try:
                job_id = uuid4()
                if drug_target_pair_upload:
                    infer_df = pd.read_csv(drug_target_pair_upload)
                    validate_columns(infer_df, ['X1', 'X2'])

                    infer_df['X1_ERR'] = infer_df['X1'].swifter.progress_bar(desc="Validating SMILES...").apply(
                        validate_seq_str, regex=SMILES_PAT)
                    if not infer_df['X1_ERR'].isna().all():
                        raise ValueError(
                            f"Encountered invalid SMILES:\n{infer_df[~infer_df['X1_ERR'].isna()][['X1', 'X1_ERR']]}")

                    infer_df['X2_ERR'] = infer_df['X2'].swifter.progress_bar(desc="Validating FASTA...").apply(
                        validate_seq_str, regex=FASTA_PAT)
                    if not infer_df['X2_ERR'].isna().all():
                        raise ValueError(
                            f"Encountered invalid FASTA:\n{infer_df[~infer_df['X2_ERR'].isna()][['X2', 'X2_ERR']]}")

                    return {infer_data_for_predict: str(drug_target_pair_upload),
                            infer_flag: job_id,
                            run_state: job_id}

                elif drug_upload and target_upload:
                    drug_df = process_drug_library_upload(drug_upload)
                    target_df = process_target_library_upload(target_upload)

                    drug_df.drop_duplicates(subset=['X1'], inplace=True)
                    target_df.drop_duplicates(subset=['X2'], inplace=True)

                    infer_df = pd.DataFrame(list(itertools.product(drug_df['X1'], target_df['X2'])),
                                            columns=['X1', 'X2'])
                    infer_df = infer_df.merge(drug_df, on='X1').merge(target_df, on='X2')

                    temp_file = Path(f'temp/{job_id}_input.csv').resolve()
                    infer_df.to_csv(temp_file, index=False)
                    if temp_file.is_file():
                        return {infer_data_for_predict: str(temp_file),
                                infer_flag: job_id,
                                run_state: job_id}

                else:
                    raise gr.Error('Should upload a compound-protein pair dataset,or '
                                   'upload both a compound library and a target library.')

                if len(infer_df) >= CUSTOM_DATASET_MAX_LEN:
                    raise gr.Error(f'The uploaded/generated compound-protein pair dataset has more records '
                                   f'than the allowed maximum (CUSTOM_DATASET_MAX_LEN).')

            except Exception as e:
                gr.Warning(f'Failed to submit the job due to error: {str(e)}')
                return {infer_flag: False,
                        run_state: False}

        else:
            gr.Warning('You have another prediction job '
                       '(drug hit screening, target protein identification, or interation pair inference) '
                       'running in the session right now. '
                       'Please submit another job when your current job has finished.')
            return {infer_flag: False,
                    run_state: state}


    drug_screen_btn.click(
        fn=drug_screen_validate,
        inputs=[target_fasta, drug_library, drug_library_upload, run_state],  # , drug_screen_email],
        outputs=[screen_data_for_predict, screen_flag, run_state]
    ).then(
        fn=lambda: [gr.Column(visible=False), gr.Markdown(visible=True)],
        outputs=[screen_page, screen_waiting]
    ).then(
        fn=submit_predict,
        inputs=[screen_data_for_predict, drug_screen_task, drug_screen_preset,
                drug_screen_target_family, screen_flag, run_state],  # , drug_screen_email],
        outputs=[file_for_report, run_state, report_upload_flag]
    ).then(
        fn=lambda: [gr.Column(visible=True), gr.Markdown(visible=False), gr.Tabs(selected=3)],
        outputs=[screen_page, screen_waiting, tabs]
    )

    target_identify_btn.click(
        fn=target_identify_validate,
        inputs=[compound_smiles, target_library, target_library_upload, run_state],  # , drug_screen_email],
        outputs=[identify_data_for_predict, identify_flag, run_state]
    ).then(
        fn=lambda: [gr.Column(visible=False), gr.Markdown(visible=True)],
        outputs=[identify_page, identify_waiting]
    ).then(
        fn=submit_predict,
        inputs=[identify_data_for_predict, target_identify_task, target_identify_preset,
                target_identify_target_family, identify_flag, run_state],  # , target_identify_email],
        outputs=[file_for_report, run_state, report_upload_flag]
    ).then(
        fn=lambda: [gr.Column(visible=True), gr.Markdown(visible=False), gr.Tabs(selected=3)],
        outputs=[identify_page, identify_waiting, tabs]
    )

    pair_infer_btn.click(
        fn=pair_infer_validate,
        inputs=[infer_data_for_predict, infer_drug, infer_target, run_state],  # , drug_screen_email],
        outputs=[infer_data_for_predict, infer_flag, run_state]
    ).then(
        fn=lambda: [gr.Column(visible=False), gr.Markdown(visible=True)],
        outputs=[infer_page, infer_waiting]
    ).then(
        fn=submit_predict,
        inputs=[infer_data_for_predict, pair_infer_task, pair_infer_preset,
                pair_infer_target_family, infer_flag, run_state],  # , pair_infer_email],
        outputs=[file_for_report, run_state, report_upload_flag]
    ).then(
        fn=lambda: [gr.Column(visible=True), gr.Markdown(visible=False), gr.Tabs(selected=3)],
        outputs=[infer_page, infer_waiting, tabs]
    )

    # TODO background job from these 3 pipelines to update file_for_report
    def inquire_task(df, upload_flag):
        if upload_flag:
            if 'Y' in df.columns:
                label = 'actual CPI/CPA labels (`Y`)'
            elif 'Y^' in df.columns:
                label = 'predicted CPI/CPA labels (`Y^`)'
            else:
                return {analyze_btn: gr.Button(interactive=True),
                        csv_generate: gr.Button(interactive=True),
                        html_generate: gr.Button(interactive=True)}

            return {report_task: gr.Dropdown(visible=True,
                                             info=f'Found {label} in your uploaded dataset. '
                                                  'Is it compound-target interaction or binding affinity?'),
                    html_report: '',
                    analyze_btn: gr.Button(interactive=False),
                    csv_generate: gr.Button(interactive=False),
                    html_generate: gr.Button(interactive=False)}
        else:
            return {report_task: gr.Dropdown(visible=False)}

    file_for_report.upload(
        fn=lambda: True, outputs=report_upload_flag
    )
    file_for_report.change(fn=update_df, inputs=file_for_report, outputs=[
        html_report, raw_df, report_df, analyze_btn]).success(
        fn=lambda: [gr.Button(interactive=False)]*2 + [gr.File(visible=False)]*2 + [gr.Dropdown(visible=False)],
        outputs=[csv_generate, html_generate, csv_download_file, html_download_file, report_task]
    ).then(
        fn=inquire_task, inputs=[raw_df, report_upload_flag],
        outputs=[report_task, html_report, analyze_btn, csv_generate, html_generate]
    )
    file_for_report.clear(fn=lambda: [gr.Dropdown(visible=False, value=None), False],
                          outputs=[report_task, report_upload_flag])

    analyze_btn.click(fn=submit_report, inputs=[raw_df, scores, filters, report_task], outputs=[
        html_report, report_df, csv_download_file, html_download_file
    ]).success(fn=lambda: [gr.Button(interactive=True)] * 2,
               outputs=[csv_generate, html_generate])

    report_task.select(fn=lambda: gr.Button(interactive=True),
                       outputs=analyze_btn)


    def create_csv_report_file(df, file_report, progress=gr.Progress(track_tqdm=True)):
        try:
            now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
            filename = f"reports/{Path(file_report.name).stem}_DeepSEQreen_report_{now}.csv"
            df.drop(labels=['Compound', 'Scaffold'], axis=1).to_csv(filename, index=False)

            return gr.File(filename)
        except Exception as e:
            gr.Warning(f"Failed to generate CSV due to error: {str(e)}")
            return None


    def create_html_report_file(df, file_report, progress=gr.Progress(track_tqdm=True)):
        try:
            now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
            filename = f"reports/{Path(file_report.name).stem}_DeepSEQreen_report_{now}.html"
            create_html_report(df, filename)
            return gr.File(filename, visible=True)
        except Exception as e:
            gr.Warning(f"Failed to generate HTML due to error: {str(e)}")
            return None


    html_report.change(lambda: [gr.Button(visible=True)] * 2, outputs=[csv_generate, html_generate])
    csv_generate.click(
        lambda: [gr.Button(visible=False), gr.File(visible=True)], outputs=[csv_generate, csv_download_file],
    ).then(fn=create_csv_report_file, inputs=[report_df, file_for_report],
           outputs=csv_download_file, show_progress='full')
    html_generate.click(
        lambda: [gr.Button(visible=False), gr.File(visible=True)], outputs=[html_generate, html_download_file],
    ).then(fn=create_html_report_file, inputs=[report_df, file_for_report],
           outputs=html_download_file, show_progress='full')

    # screen_waiting.change(fn=check_job_status, inputs=run_state, outputs=[pair_waiting, tabs, file_for_report],
    #                       every=5)
    # identify_waiting.change(fn=check_job_status, inputs=run_state, outputs=[identify_waiting, tabs, file_for_report],
    #                         every=5)
    # pair_waiting.change(fn=check_job_status, inputs=run_state, outputs=[pair_waiting, tabs, file_for_report],
    #                     every=5)

    # demo.load(None, None, None, js="() => {document.body.classList.remove('dark')}")

if __name__ == "__main__":
    screen_block.queue(max_size=3)
    identify_block.queue(max_size=3)
    infer_block.queue(max_size=3)
    report.queue(max_size=3)

    # SCHEDULER.add_job(func=file_cleanup(), trigger="interval", seconds=60)
    # SCHEDULER.start()

    demo.launch(
        show_api=False,
    )