import traceback
from io import StringIO
from typing import Optional

import gradio as gr
import pandas as pd
from loguru import logger

from utils import pipeline
from utils.models import list_models


def read_data(filepath: str) -> Optional[pd.DataFrame]:
    if filepath.endswith('.xlsx'):
        df = pd.read_excel(filepath)
    elif filepath.endswith('.csv'):
        df = pd.read_csv(filepath)
    else:
        raise Exception('File type not supported')
    return df


def process(
        task_name: str,
        model_name: str,
        pooling: str,
        text: str,
        file=None,
) -> (None, pd.DataFrame, str):
    try:
        logger.info(f'Processing {task_name} with {model_name} and {pooling}')
        # load file
        if file:
            df = read_data(file.name)
        elif text:
            string_io = StringIO(text)
            df = pd.read_csv(string_io)
            assert len(df) >= 1, 'No input data'
        else:
            raise Exception('No input data')

        # check
        if len(df) > 10000:
            raise Exception('Data exceeds 10,000 rows')

        # process
        if task_name == 'Originality':
            df = pipeline.p0_originality(df, model_name, pooling)
        elif task_name == 'Flexibility':
            df = pipeline.p1_flexibility(df, model_name, pooling)
        else:
            raise Exception('Task not supported')

        # save
        path = 'output.csv'
        df.to_csv(path, index=False, encoding='utf-8-sig')
        return None, df.iloc[:10], path

    except:
        error = traceback.format_exc()
        logger.warning({
            'error': error,
            'task_name': task_name,
            'model_name': model_name,
            'pooling': pooling,
            'text': text,
            'file': file,
        })
        return {'Info': 'Something wrong', 'Error': traceback.format_exc()}, None, None


# input
task_name_dropdown = gr.components.Dropdown(
    label='Task Name',
    value='Originality',
    choices=['Originality', 'Flexibility']
)
model_name_dropdown = gr.components.Dropdown(
    label='Model Name',
    value=list_models[0],
    choices=list_models
)
pooling_dropdown = gr.components.Dropdown(
    label='Pooling',
    value='mean',
    choices=['mean', 'cls']
)
text_input = gr.components.Textbox(
    value=open('data/example_xlm.csv', 'r').read(),
    lines=10,
)
file_input = gr.components.File(label='Input File', file_types=['.csv', '.xlsx'])

# output
text_output = gr.components.Textbox(label='Output')
dataframe_output = gr.components.Dataframe(label='DataFrame')
file_output = gr.components.File(label='Output File', file_types=['.csv', '.xlsx'])

app = gr.Interface(
    fn=process,
    inputs=[task_name_dropdown, model_name_dropdown, pooling_dropdown, text_input, file_input],
    outputs=[text_output, dataframe_output, file_output],
    description=open('data/description.txt', 'r').read(),
    title='TransDis-CreativityAutoAssessment',
    concurrency_limit=1,
)
app.launch(max_threads=1)