app.py

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 plotly.express as px
import requests
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, AllChem
from rdkit.Chem.Scaffolds import MurckoScaffold
import seaborn as sns

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 = 1.5
PandasTools.drawOptions.explicitMethyl = True
PandasTools.drawOptions.singleColourWedgeBonds = True
PandasTools.drawOptions.useCDKAtomPalette()
PandasTools.molSize = (128, 128)

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 = 10000

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(row):
    return sascorer.calculateScore(row['Compound'])


def mw(row):
    return Chem.Descriptors.MolWt(row['Compound'])


def mr(row):
    return Crippen.MolMR(row['Compound'])


def hbd(row):
    return Lipinski.NumHDonors(row['Compound'])


def hba(row):
    return Lipinski.NumHAcceptors(row['Compound'])


def logp(row):
    return Crippen.MolLogP(row['Compound'])


def atom(row):
    return CalcNumAtoms(row['Compound'])


def heavy_atom(row):
    return CalcNumHeavyAtoms(row['Compound'])


def rotatable_bond(row):
    return CalcNumRotatableBonds((row['Compound']))


def tpsa(row):
    return CalcTPSA((row['Compound']))


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


def reos(row):
    """
    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(row) < 500:
        return False
    elif not -5.0 < logp(row) < 5.0:
        return False
    elif not 0 < hbd(row) < 5:
        return False
    elif not 0 < hba(row) < 10:
        return False
    elif not 0 < rotatable_bond(row) < 8:
        return False
    elif not 15 < heavy_atom(row) < 50:
        return False
    else:
        return True


def ghose(row):
    """
    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(row) < 480:
        return False
    elif not -0.4 < logp(row) < 5.6:
        return False
    elif not 20 < atom(row) < 70:
        return False
    elif not 40 < mr(row) < 130:
        return False
    else:
        return True


def veber(row):
    """
    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(row) <= 10:
        return False
    elif not tpsa(row) <= 140:
        return False
    else:
        return True


def rule_of_three(row):
    """
    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(row) <= 300:
        return False
    elif not logp(row) <= 3:
        return False
    elif not hbd(row) <= 3:
        return False
    elif not hba(row) <= 3:
        return False
    elif not rotatable_bond(row) <= 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': 'transfomer_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',
    'Other protein targets': 'others',
}

TARGET_LIBRARY_MAP = {
    'ChEMBL33 (Human)': 'ChEMBL33_human_proteins.csv',
    # 'STITCH': 'stitch.csv',
    # 'Drug Repurposing Hub': 'drug_repurposing_hub.csv',
}

DRUG_LIBRARY_MAP = {
    'DrugBank (Human)': 'drugbank.csv',
}

COLUMN_ALIASES = {
    'X1': 'Compound SMILES',
    'X2': 'Target FASTA',
    'ID1': 'Compound ID',
    'ID2': 'Target ID',
}


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(['X1', 'X2']).strip('[]')}.")
        raise ValueError(error_message)
    else:
        return


def process_target_fasta(sequence):
    lines = sequence.strip().split("\n")
    if lines[0].startswith(">"):
        lines = lines[1:]
    return ''.join(lines).split(">")[0]
    # record = SeqIO.parse(io.StringIO(sequence), "fasta")[0]
    # return str(record.seq)


def send_email(receiver, msg):
    pass


def submit_predict(predict_filepath, task, preset, target_family, flag, 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 = COLUMN_ALIASES | {
                'Y': 'Actual interaction probability' if task == 'binary' else 'Actual binding affinity',
                'Y^': 'Predicted interaction probability' if task == 'binary' 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)])

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

                return [predictions_file,
                        False]
        except Exception as e:
            gr.Warning(f"Prediction job failed due to error: {str(e)}")
            return [None,
                    False]

    else:
        return [None,
                False]
        #
        # 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 ([email protected]).')
    #         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 is not None:
        df = pd.read_csv(file)
        if df['X1'].nunique() > 1:
            df['Scaffold SMILES'] = df['X1'].swifter.progress_bar(
                desc=f"Calculating scaffold...").apply(MurckoScaffold.MurckoScaffoldSmilesFromSmiles)
            # Add a new column with RDKit molecule objects
            if 'Compound' not in df.columns or df['Compound'].dtype != 'object':
                PandasTools.AddMoleculeColumnToFrame(df, smilesCol='X1', molCol='Compound',
                                                     includeFingerprints=True)
            PandasTools.AddMoleculeColumnToFrame(df, smilesCol='Scaffold SMILES', molCol='Scaffold',
                                                 includeFingerprints=True)
        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 create_html_report(DF_FOR_REPORT), df  # pie_chart
    else:
        return gr.HTML(), gr.Dataframe()


def create_html_report(df, file=None, progress=gr.Progress(track_tqdm=True)):
    df_html = df.copy()
    cols_left = ['ID1', 'ID2', 'Y', 'Y^', 'Compound', 'Scaffold', 'Scaffold SMILES', ]
    cols_right = ['X1', 'X2']
    cols_left = [col for col in cols_left if col in df_html.columns]
    cols_right = [col for col in cols_right if col in df_html.columns]
    df_html = df_html[cols_left + (df_html.columns.drop(cols_left + cols_right).tolist()) + cols_right]
    df_html['X2'] = df_html['X2'].swifter.apply(wrap_text)
    df_html = df_html.sort_values(
        [col for col in ['Y', 'Y^', 'ID1', 'ID2', 'X1', 'X2'] if col in df.columns], ascending=False
    ).rename(columns=COLUMN_ALIASES)
    # PandasTools.RenderImagesInAllDataFrames(images=True)
    PandasTools.ChangeMoleculeRendering(df_html, renderer='image')
    # Return the DataFrame as HTML
    PandasTools.RenderImagesInAllDataFrames(images=True)

    if not file:
        styled_df = df_html.iloc[:51].style
        # styled_df = df.style.format("{:.2f}")
        colors = sns.color_palette('husl', len(df_html.columns))
        for i, col in enumerate(df_html.columns):
            if pd.api.types.is_numeric_dtype(df_html[col]):
                styled_df = styled_df.background_gradient(subset=col, cmap=sns.light_palette(colors[i], as_cmap=True))
        html = styled_df.to_html()
        return f'Report preview<div style="overflow:auto; height: 300px; font-family: Courier !important;">{html}</div>'
    else:
        import panel as pn
        from bokeh.resources import INLINE
        from bokeh.models import NumberFormatter, BooleanFormatter
        bokeh_formatters = {
            'float': {'type': 'progress', 'legend': True},
            'bool': BooleanFormatter(),
        }
        # html = df.to_html(file)
        # return html
        pn.widgets.Tabulator(df_html, formatters=bokeh_formatters).save(file, resources=INLINE)


# def create_pie_chart(df, category, value, top_k):
#     df.rename(COLUMN_ALIASES, inplace=True)
#     # Select the top_k records based on the value_col
#     top_k_df = df.nlargest(top_k, value)
#
#     # Count the frequency of each unique value in the category_col column
#     category_counts = top_k_df[category].value_counts()
#
#     # Convert the counts to a DataFrame
#     data = pd.DataFrame({category: category_counts.index, 'value': category_counts.values})
#
#     # Calculate the angle for each category
#     data['angle'] = data['value']/data['value'].sum() * 2*pi
#
#     # Assign colors
#     data['color'] = Spectral11[0:len(category_counts)]
#
#     # Create the plot
#     p = figure(height=350, title="Pie Chart", toolbar_location=None,
#                tools="hover", tooltips="@{}: @value".format(category), x_range=(-0.5, 1.0))
#
#     p.wedge(x=0, y=1, radius=0.4,
#             start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
#             line_color="white", fill_color='color', legend_field=category, source=data)
#
#     p.axis.axis_label = None
#     p.axis.visible = False
#     p.grid.grid_line_color = None
#
#     return p

def create_pie_chart(df, category, value, top_k):
    df = df.copy()
    df.rename(COLUMN_ALIASES, inplace=True)
    value = COLUMN_ALIASES.get(value, value)
    # Select the top_k records based on the value_col
    top_k_df = df.nlargest(top_k, value)

    # Count the frequency of each unique value in the category_col column
    category_counts = top_k_df[category].value_counts()

    # Convert the counts to a DataFrame
    data = pd.DataFrame({category: category_counts.index, 'value': category_counts.values})

    # Create the plot
    fig = px.pie(data, values='value', names=category, title=f'Top-{top_k} {category} in {value}')
    fig.update_traces(textposition='inside', textinfo='percent+label')

    return fig


def submit_report(score_list, filter_list, progress=gr.Progress(track_tqdm=True)):
    df = DF_FOR_REPORT.copy()
    try:
        for filter_name in filter_list:
            df[filter_name] = df.swifter.progress_bar(desc=f"Calculating {filter_name}").apply(
                FILTER_MAP[filter_name], axis=1)

        for score_name in score_list:
            df[score_name] = df.swifter.progress_bar(desc=f"Calculating {score_name}").apply(
                SCORE_MAP[score_name], axis=1)

        # 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), df  # pie_chart

    except Exception as e:
        raise gr.Error(str(e))


# 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):
    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)


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


def smiles_from_sdf(sdf_path):
    with Chem.SDMolSupplier(sdf_path) as suppl:
        return Chem.MolToSmiles(suppl[0])


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


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

    with gr.Tabs() as tabs:
        with gr.TabItem(label='Drug hit screening', id=0):
            gr.Markdown('''
# <center>DeepSEQreen Drug Hit Screening</center>
<center>
To predict interactions/binding affinities of a single target against a library of compounds.
</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."
                            )
                            with gr.Row():
                                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
                                )
                                target_id = gr.Textbox(show_label=False, visible=False,
                                                       interactive=True, scale=4,
                                                       info='Query a sequence on UniProt with a UniProt ID.')
                                target_gene = gr.Textbox(
                                    show_label=False, visible=False,
                                    interactive=True, scale=4,
                                    info='Query a sequence on UniProt with a gene symbol.')
                                target_organism = gr.Textbox(
                                    info='Organism scientific name (default: Homo sapiens).',
                                    placeholder='Homo sapiens', show_label=False,
                                    visible=False, interactive=True, scale=4, )

                    with gr.Row():
                        with gr.Column():
                            target_upload_btn = gr.UploadButton(label='Upload a FASTA file', type='binary',
                                                                visible=True, variant='primary',
                                                                size='lg')
                            target_query_btn = gr.Button(value='Query the sequence', variant='primary',
                                                         visible=False)

                    target_fasta = gr.Code(label='Input or Display FASTA', interactive=True, lines=5)
                    # with gr.Row():
                    #     with gr.Column():
                    example_fasta = gr.Button(value='Example: Human MAPK14', elem_id='example')
                        # 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 Input Protein 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='Auto-detect', 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."
                            )
                            drug_library = gr.Dropdown(label='Step 3. Select or Upload a 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='Upload a custom 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 a Prediction Task',
                                                           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='Recommend a model', variant='primary')
                    with gr.Row():
                        with gr.Column():
                            drug_screen_email = gr.Textbox(
                                label='Step 6. Email (Optional)',
                                info="If an email is provided, a notification email will be sent to you when your job is completed."
                            )

                    with gr.Row(visible=True):
                        with gr.Column():
                        # drug_screen_clr_btn = gr.ClearButton(size='lg')
                            drug_screen_btn = gr.Button(value='SCREEN', 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>DeepSEQreen Target Protein Identification</center>

<center>
To predict interactions/binding affinities of a single compound against a library of protein targets.
</center>

ℹ️ A custom target library can be a FASTA file with a single or multiple amino acid sequences, 
or a CSV file has a required FASTA string column and optionally an ID column:

<b>X2</b>: the FASTA sequence of a target\n
<b>ID2</b> (optional): the ID (PubChem or any arbitrary unique identifier) of a compound\n

Example CSV target library:

| X2            | ID2    |
|---------------|--------|
| MVQKSRNGGV... | O88943 |
| MTSPSSSPVF... | Q9Y5S1 |
                ''')
            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.',
                                value='SMILES',
                                interactive=True)
                            compound_upload_btn = gr.UploadButton(label='Upload', variant='primary', type='binary')

                    compound_smiles = gr.Code(label='Input or Display Compound SMILES', interactive=True, lines=5)
                    example_drug = gr.Button(value='Example: Aspirin', elem_id='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=list(TARGET_FAMILY_MAP.keys()),
                                                                        value='General',
                                                                        label='Step 2. Select Target Protein 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 tareget protein sequences, or use an FASTA file."
                            )
                            target_library = gr.Dropdown(label='Step 3. Select or Upload a 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='Upload a custom 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 a Prediction Task',
                                                               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='Recommend a model', variant='primary')

                    with gr.Row():
                        with gr.Column():
                            target_identify_email = gr.Textbox(
                                label='Step 6. Email (Optional)',
                                info="If an email is provided, a notification email will be sent to you when your job is completed."
                            )

                    with gr.Row(visible=True):
                        # target_identify_clr_btn = gr.ClearButton(size='lg')
                        target_identify_btn = gr.Button(value='IDENTIFY', 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>DeepSEQreen Interaction Pair Inference</center>
<center>To predict interactions/binding affinities between any compound-protein pairs.</center>

ℹ️ A custom interaction pair dataset can be generated from a FASTA file containing multiple sequences 
and a SDF file containing multiple compounds (for predicting CPI/CPA of all possible combinations of 
compound-protein pairs), or a CSV file with 2 required string columns and optionally 2 ID columns:

<b>X1</b>: the SMILES string of a compound\n
<b>X2</b>: the FASTA sequence of a target\n
<b>ID1</b> (optional): the ID (PubChem or any arbitrary unique identifier) of a compound\n
<b>ID2</b> (optional): the ID (UniProt or any arbitrary unique identifier) of a protein

Example CSV interaction pair dataset:

| X1                                      | X2            | ID1          | ID2    |
|---------------------------------------- |---------------|--------------|--------|
| CCOC(=O)Nc1ccc(NCc2ccc(F)cc2)cc1N       | MVQKSRNGGV... | CHEMBL41355  | O88943 |
| CCCCCc1cc(O)c(C/C=C(\C)CCC=C(C)C)c(O)c1 | MTSPSSSPVF... | CHEMBL497318 | Q9Y5S1 |
''')
            with gr.Blocks() as infer_block:
                with gr.Column() as infer_page:
                    infer_type = gr.Dropdown(choices=['Upload a compound library and a target library',
                                                      'Upload a CSV interaction pair dataset'],
                                             value='Upload a compound library and a target library')
                    with gr.Column() as pair_upload:
                        gr.File(label="Example custom dataset",
                                value="data/examples/interaction_pair_inference.csv",
                                interactive=False)
                        with gr.Column():
                            infer_data_for_predict = gr.File(
                                label='Upload a custom dataset', file_count="single", type='filepath', visible=True)
                    with gr.Column() 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_drug = gr.File(label='SDF/CSV file containing multiple compounds',
                                                 file_count="single", type='filepath')
                            infer_target = gr.File(label='FASTA/CSV file containing multiple targets',
                                                   file_count="single", type='filepath')

                    with gr.Row(visible=True):
                        pair_infer_task = gr.Dropdown(list(TASK_MAP.keys()), label='Task')
                        pair_infer_preset = gr.Dropdown(list(PRESET_MAP.keys()), label='Preset')
                        pair_infer_target_family = gr.Dropdown(choices=['General'],
                                                               label='Target family',
                                                               value='General')

                    # with gr.Row():
                    #     pair_infer_email = gr.Textbox(
                    #         label='Email (optional)',
                    #         info="Your email will be used to send you notifications when your job finishes."
                    #     )

                    with gr.Row(visible=True):
                        # pair_infer_clr_btn = gr.ClearButton(size='lg')
                        pair_infer_btn = gr.Button(value='INFER', 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>DeepSEQreen Chemical Property Report</center>
                <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. The page shows only a preview report displaying at most 30 records 
                (with top predicted CPI/CPA if reporting results from a prediction job). For a full report, please 
                generate and download a raw data CSV or interactive table HTML file below.
                </center>
                ''')
                with gr.Row():
                    file_for_report = gr.File(interactive=True, type='filepath')
                    df_raw = gr.Dataframe(type="pandas", interactive=False, visible=False)
                    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('REPORT', variant='primary', size='lg')

                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 raw data (CSV)', interactive=True)
                        csv_download_file = gr.File(label='Download raw data (CSV)', visible=False)
                    with gr.Column():
                        html_generate = gr.Button(value='Generate report (HTML)', interactive=True)
                        html_download_file = gr.File(label='Download report (HTML)', 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='')]
            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='')]
            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='')]


    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
        ],
        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


    target_upload_btn.upload(fn=lambda x: x.decode(), 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)


    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 fasta not in 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.Dropdown(info='Input an SMILES string or upload an SMI file')
            case 'SDF':
                return gr.Dropdown(info='Convert the first molecule in an SDF file to SMILES')


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


    def compound_upload_process(input_type, input_upload):
        match input_type:
            case 'SMILES':
                return input_upload.decode()
            case 'SDF':
                suppl = Chem.ForwardSDMolSupplier(io.BytesIO(input_upload))
                return Chem.MolToSmiles(next(suppl))


    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 smiles not in 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
                }
        match upload_type:
            case "Upload a CSV interaction pair dataset":
                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_type.select(fn=infer_type_change, inputs=infer_type,
                      outputs=[pair_upload, pair_generate, infer_data_for_predict, infer_drug, infer_target])


    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)
                    print(screen_df.shape)
                    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],  # , drug_screen_email],
        outputs=[file_for_report, run_state]
    ).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), gr.Tabs(selected=3)],
        outputs=[identify_page, identify_waiting, tabs]
    ).then(
        fn=submit_predict,
        inputs=[identify_data_for_predict, target_identify_task, target_identify_preset,
                target_identify_target_family, identify_flag],  # , target_identify_email],
        outputs=[file_for_report, run_state]
    ).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],  # , pair_infer_email],
        outputs=[file_for_report, run_state]
    ).then(
        fn=lambda: [gr.Column(visible=True), gr.Markdown(visible=False)],
        outputs=[infer_page, infer_waiting]
    )

    # TODO background job from these 3 pipelines to update file_for_report

    file_for_report.change(fn=update_df, inputs=file_for_report, outputs=[
        html_report,
        df_raw,
        # ranking_pie_chart
    ])
    analyze_btn.click(fn=submit_report, inputs=[scores, filters], outputs=[
        html_report,
        df_raw,
        # ranking_pie_chart
    ])


    def create_csv_raw_file(df, file_report):
        from datetime import datetime
        now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
        filename = f"reports/{Path(file_report.name).stem}_DeepSEQreen_report_{now}.csv"
        df.drop(['Compound', 'Scaffold']).to_csv(filename, index=False)
        return gr.File(filename, visible=True)


    def create_html_report_file(df, file_report):
        from datetime import datetime
        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)


    csv_generate.click(fn=create_csv_raw_file, inputs=[df_raw, file_for_report], outputs=csv_download_file)
    html_generate.click(fn=create_html_report_file, inputs=[df_raw, file_for_report], outputs=html_download_file)

    # 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=2)
    identify_block.queue(max_size=2)
    infer_block.queue(max_size=2)
    report.queue(max_size=20)

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

    demo.launch(
        show_api=False,
    )

#%%