from dash import Dash, Input, Output, callback, dcc, html
import copy
import plotly.graph_objects as go
import pandas as pd


# # open a file, where you stored the pickled data
file = open('./sankey_data_y145.pkl', 'rb')

# # dump information to that file
data = pd.read_pickle(file)

df = pd.read_csv("./chatgpt_explanation.csv")

external_script = ["https://tailwindcss.com/", {"src": "https://cdn.tailwindcss.com"}]

app = Dash(
    __name__,
    external_scripts=external_script,
)
app.scripts.config.serve_locally = True

app.layout = html.Div(
    [
        html.Div(
            [
                html.Div(
                [
                    html.H1("BioXNet: a biologically inspired neural network for deciphering anti-cancer drug response in precision medicine", className="text-black text-4xl font-bold pb-8 pt-32"),
                    html.H2("https://doi.org/10.1101/2024.01.29.576766", className="text-black flex text-left text-lg font-semibold pb-8"),
                    html.H3("For an improved experience, it is recommended to use either Chrome or Edge.", className="text-black flex text-left text-lg font-semibold pb-8"),
                    html.Hr(className="border-t-2 border-slate-500")
                ],className="max-w-6xl flex flex-col justify-center mx-auto px-8 mx-8 pb-4 bg"
                )
            ],id="background-container"
        ),
        html.Div(
            [


                html.Div(
                    [
                        html.Div(
                            [
                                html.H5("Drug Name", className="text-lg font-medium pb-2 text-slate-500"),
                                dcc.Dropdown(
                                    id="drugname",
                                    options=sorted(df["drug_name"].unique()),
                                    value='bevacizumab',
                                    className="w-80"
                                ),
                            ], className="flex flex-col"
                        ),

                        html.Div(
                            [
                                html.H5("Cancer type in TCGA", className="text-lg font-medium pb-2 text-slate-500"),
                                dcc.Dropdown(
                                    id="tcga", options=sorted(df["tcga_id"].unique()),
                                    value='BRCA',
                                    className="w-80"
                                ),
                            ], className="flex flex-col"
                        ),
                    ], className="flex flex-row max-md:flex-col gap-20 items-center mx-auto"
                ),

            ],className="max-w-6xl flex flex-col justify-center mx-auto mt-8 px-8 mx-8"
        ),
        html.H3("Interpretable paths", className="px-8 mx-8 flex mx-auto max-w-6xl font-extrabold text-transparent text-4xl bg-clip-text bg-gradient-to-r from-purple-400 to-pink-600 pb-8 text-left pt-12"),

        html.Div(
            [
                dcc.Graph(id="graph", style={'width': '60vw', 'height': '70vh','align':'justify-center'}),
            ],className="max-w-6xl flex flex-col items-center mx-auto  mt-8 px-8 mx-8"
        ),
        html.Div(
            [
                html.H3("Readable interpretations generate by ChatGPT", className="text-left font-extrabold text-transparent text-4xl max-md:text-2xl  bg-clip-text bg-gradient-to-r from-purple-400 to-pink-600  pb-12"),
                html.P(id="explaination", className="text-lg max-md:text-base leading-10 text-justify")
            ],className="max-w-6xl flex flex-col mx-auto  mt-8 px-8 mx-8 pb-32"
        )
    ]
)


@callback(
    Output("drugname", "options"),
    Input("tcga", "value"),
)
def chained_callback_drugname(tcga):

    dff = copy.deepcopy(df)

    if tcga is not None:
        dff = dff.query("tcga_id == @tcga")

    return sorted(dff["drug_name"].unique())



@callback(
    Output("tcga", "options"),
    Input("drugname", "value"),
)
def chained_callback_tcga(drugname):

    dff = copy.deepcopy(df)

    if drugname is not None:
        dff = dff.query("drug_name == @drugname")

    return sorted(dff["tcga_id"].unique())


@callback(
    Output("graph", "figure"),
    Input("drugname", "value"),
    Input("tcga", "value"),
)
def graph_chart(drugname, tcga):

    comb_name= tcga+'_'+drugname

    node = data[comb_name]['node']
    # node["y"]=[ x * 1.45 for x in node["y"] ]
    link = data[comb_name]['link']

    fig = go.Figure(go.Sankey(link=link, node=node,))
    # fig.update_layout(data[comb_name]['layout'])
    fig.update_layout(
            margin=dict( l= 0, r=0, t=8,b= 0, autoexpand=False,),
            font= {
                "size": 12,
                "family": "Helvetica"
            },
            paper_bgcolor='rgba(0,0,0,0)',
            plot_bgcolor='rgba(0,0,0,0)'
        )

    return fig

@callback(
    Output("explaination", "children"),
    [Input("tcga", "value"), Input("drugname", "value")]
)
def update_explanation(tcga, drugname):
    dff = copy.deepcopy(df)

    if tcga is not None:
        dff = dff.query("tcga_id == @tcga")

    if drugname is not None:
        dff = dff.query("drug_name == @drugname")

    explanation = dff["explanation"].values[0] if not dff.empty else ""

    return explanation


if __name__ == "__main__":
    app.run(debug=False, host="0.0.0.0", port=7860)