import os
import sys
import requests
import time
import pandas as pd
import argilla as rg
from datasets import load_dataset
from argilla.labeling.text_classification import Rule, add_rules


def load_datasets():
    # This is the code that you want to execute when the endpoint is available
    print("Argilla is available! Loading datasets")
    api_key = sys.argv[-1]
    rg.init(api_key=api_key, workspace="team")

    # load dataset from json
    my_dataframe = pd.read_json(
        "https://raw.githubusercontent.com/recognai/datasets/main/sst-sentimentclassification.json")

    # convert pandas dataframe to DatasetForTextClassification
    dataset_rg = rg.DatasetForTextClassification.from_pandas(my_dataframe)

    # Define labeling schema to avoid UI user modification
    settings = rg.TextClassificationSettings(label_schema=["POSITIVE", "NEGATIVE"])
    rg.configure_dataset(name="sst-sentiment-explainability", settings=settings)

    # log the dataset
    rg.log(
        dataset_rg,
        name="sst-sentiment-explainability",
        tags={
            "description": "The sst2 sentiment dataset with predictions from a pretrained pipeline and explanations from Transformers Interpret."
        }
    )

    dataset = load_dataset("argilla/news-summary", split="train").select(range(100))
    dataset_rg = rg.read_datasets(dataset, task="Text2Text")

    # log the dataset
    rg.log(
        dataset_rg,
        name="news-text-summarization",
        tags={
            "description": "A text summarization dataset with news pieces and their predicted summaries."
        }
    )

    # Read dataset from Hub
    dataset_rg = rg.read_datasets(
        load_dataset("argilla/agnews_weak_labeling", split="train"),
        task="TextClassification",
    )

    # Define labeling schema to avoid UI user modification
    settings = rg.TextClassificationSettings(label_schema=["World", "Sports", "Sci/Tech", "Business"])
    rg.configure_dataset(name="news-programmatic-labeling", settings=settings)

    # log the dataset
    rg.log(
        dataset_rg,
        name="news-programmatic-labeling",
        tags={
            "description": "The AG News with programmatic labeling rules (see weak labeling mode in the UI)."
        }
    )

    # define queries and patterns for each category (using ES DSL)
    queries = [
        (["money", "financ*", "dollar*"], "Business"),
        (["war", "gov*", "minister*", "conflict"], "World"),
        (["*ball", "sport*", "game", "play*"], "Sports"),
        (["sci*", "techno*", "computer*", "software", "web"], "Sci/Tech"),
    ]

    # define rules
    rules = [Rule(query=term, label=label) for terms, label in queries for term in terms]

    # add rules to the dataset
    add_rules(dataset="news-programmatic-labeling", rules=rules)

    # load dataset from the hub
    dataset = load_dataset("argilla/gutenberg_spacy-ner", split="train")

    # read in dataset, assuming its a dataset for token classification
    dataset_rg = rg.read_datasets(dataset, task="TokenClassification")

    # Define labeling schema to avoid UI user modification
    labels = ["CARDINAL", "DATE", "EVENT", "FAC", "GPE", "LANGUAGE", "LAW", "LOC", "MONEY", "NORP", "ORDINAL", "ORG",
              "PERCENT", "PERSON", "PRODUCT", "QUANTITY", "TIME", "WORK_OF_ART"]
    settings = rg.TokenClassificationSettings(label_schema=labels)
    rg.configure_dataset(name="gutenberg_spacy-ner-monitoring", settings=settings)

    # log the dataset
    rg.log(
        dataset_rg,
        "gutenberg_spacy-ner-monitoring",
        tags={
            "description": "A dataset containing text from books with predictions from two spaCy NER pre-trained models."
        }
    )


while True:
    try:
        response = requests.get("http://0.0.0.0:6900/")
        if response.status_code == 200:
            load_datasets()
            break
        else:
            time.sleep(10)
    except Exception as e:
        print(e)
        time.sleep(10)
        pass