import json
import logging
import os
import re
import sys
from pathlib import Path

import numpy as np
from datasets import Value

REGEX_YAML_BLOCK = re.compile(r"---[\n\r]+([\S\s]*?)[\n\r]+---[\n\r]")


def infer_gradio_input_types(feature_types):
    """
    Maps metric feature types to input types for gradio Dataframes:
        - float/int -> numbers
        - string -> strings
        - any other -> json
    Note that json is not a native gradio type but will be treated as string that
    is then parsed as a json.
    """
    input_types = []
    for feature_type in feature_types:
        input_type = "json"
        if isinstance(feature_type, Value):
            if feature_type.dtype.startswith(
                "int"
            ) or feature_type.dtype.startswith("float"):
                input_type = "number"
            elif feature_type.dtype == "string":
                input_type = "str"
        input_types.append(input_type)
    return input_types


def json_to_string_type(input_types):
    """Maps json input type to str."""
    return ["str" if i == "json" else i for i in input_types]


def parse_readme(filepath):
    """Parses a repositories README and removes"""
    if not os.path.exists(filepath):
        return "No README.md found."
    with open(filepath, "r") as f:
        text = f.read()
        match = REGEX_YAML_BLOCK.search(text)
        if match:
            text = text[match.end() :]
    return text


def parse_gradio_data(data, input_types):
    """Parses data from gradio Dataframe for use in metric."""
    metric_inputs = {}
    data.replace("", np.nan, inplace=True)
    data.dropna(inplace=True)
    for feature_name, input_type in zip(data, input_types):
        if input_type == "json":
            metric_inputs[feature_name] = [
                json.loads(d) for d in data[feature_name].to_list()
            ]
        elif input_type == "str":
            metric_inputs[feature_name] = [
                d.strip('"') for d in data[feature_name].to_list()
            ]
        else:
            metric_inputs[feature_name] = data[feature_name]
    return metric_inputs


def parse_test_cases(test_cases, feature_names, input_types):
    """
    Parses test cases to be used in gradio Dataframe. Note that an apostrophe is added
    to strings to follow the format in json.
    """
    if len(test_cases) == 0:
        return None
    examples = []
    for test_case in test_cases:
        parsed_cases = []
        for feat, input_type in zip(feature_names, input_types):
            if input_type == "json":
                parsed_cases.append(
                    [str(element) for element in test_case[feat]]
                )
            elif input_type == "str":
                parsed_cases.append(
                    ['"' + element + '"' for element in test_case[feat]]
                )
            else:
                parsed_cases.append(test_case[feat])
        examples.append([list(i) for i in zip(*parsed_cases)])
    return examples


def launch_gradio_widget2(metric):
    """Launches `metric` widget with Gradio."""

    try:
        import gradio as gr
    except ImportError as error:
        logging.error(
            "To create a metric widget with Gradio make sure gradio is installed."
        )
        raise error

    local_path = Path(sys.path[0])
    # if there are several input types, use first as default.
    if isinstance(metric.features, list):
        (feature_names, feature_types) = zip(*metric.features[0].items())
    else:
        (feature_names, feature_types) = zip(*metric.features.items())
    gradio_input_types = infer_gradio_input_types(feature_types)

    def compute(data):
        return metric.compute(**parse_gradio_data(data, gradio_input_types))

    test_cases = [
        {
            "samples": [[0.1, 0.2, 0.3], [0.7, 0.6, 0.9], [0.8, 0.95, 0.85]],
            "predictions": [0, 1, 1],
        }
    ]
    iface = gr.Interface(
        fn=compute,
        inputs=gr.Dataframe(
            headers=feature_names,
            col_count=len(feature_names),
            row_count=1,
            datatype=json_to_string_type(gradio_input_types),
        ),
        outputs=gr.Textbox(label=metric.name),
        description=(
            metric.info.description
            + "\nIf this is a text-based metric, make sure to wrap you input in double quotes."
            " Alternatively you can use a JSON-formatted list as input."
        ),
        title=f"Metric: {metric.name}",
        article=parse_readme(local_path / "README.md"),
        examples=[
            parse_test_cases(test_cases, feature_names, gradio_input_types)
        ],
    )

    iface.launch(share=True)