import time

import gradio as gr

from gradio_molecule3d import Molecule3D
from run_on_seq import run_on_sample_seqs
from env_consts import RUN_CONFIG_PATH, OUTPUT_PROT_PATH, OUTPUT_LIG_PATH


def predict(input_sequence, input_ligand, input_msa, input_protein):
    start_time = time.time()
    # Do inference here
    # return an output pdb file with the protein and ligand with resname LIG or UNK.
    # also return any metrics you want to log, metrics will not be used for evaluation but might be useful for users
    metrics = run_on_sample_seqs(input_sequence, input_protein, input_ligand, OUTPUT_PROT_PATH, OUTPUT_LIG_PATH,
                                 RUN_CONFIG_PATH)
    end_time = time.time()
    run_time = end_time - start_time

    return [OUTPUT_PROT_PATH, OUTPUT_LIG_PATH], metrics, run_time


with gr.Blocks() as app:
    gr.Markdown("DockFormer")

    # gr.Markdown("Title, description, and other information about the model")
    with gr.Row():
        input_sequence = gr.Textbox(lines=3, label="Input Protein sequence (FASTA)")
        input_ligand = gr.Textbox(lines=3, label="Input ligand SMILES")
    with gr.Row():
        input_msa = gr.File(label="Input Protein MSA (A3M)")
        input_protein = gr.File(label="Input protein monomer")

    # define any options here

    # for automated inference the default options are used
    # slider_option = gr.Slider(0,10, label="Slider Option")
    # checkbox_option = gr.Checkbox(label="Checkbox Option")
    # dropdown_option = gr.Dropdown(["Option 1", "Option 2", "Option 3"], label="Radio Option")

    btn = gr.Button("Run Inference")

    gr.Examples(
        [
            [
                "SVKSEYAEAAAVGQEAVAVFNTMKAAFQNGDKEAVAQYLARLASLYTRHEELLNRILEKARREGNKEAVTLMNEFTATFQTGKSIFNAMVAAFKNGDDDSFESYLQALEKVTAKGETLADQIAKAL:SVKSEYAEAAAVGQEAVAVFNTMKAAFQNGDKEAVAQYLARLASLYTRHEELLNRILEKARREGNKEAVTLMNEFTATFQTGKSIFNAMVAAFKNGDDDSFESYLQALEKVTAKGETLADQIAKAL",
                "COc1ccc(cc1)n2c3c(c(n2)C(=O)N)CCN(C3=O)c4ccc(cc4)N5CCCCC5=O",
                "test_out.pdb"
            ],
        ],
        [input_sequence, input_ligand, input_protein],
    )
    reps = [
        {
            "model": 0,
            "style": "cartoon",
            "color": "whiteCarbon",
        },
        {
            "model": 1,
            "style": "stick",
            "color": "greenCarbon",
        }

    ]

    out = Molecule3D(reps=reps)
    metrics = gr.JSON(label="Metrics")
    run_time = gr.Textbox(label="Runtime")

    btn.click(predict, inputs=[input_sequence, input_ligand, input_msa, input_protein],
              outputs=[out, metrics, run_time])

app.launch()