import os
import gradio as gr
from gradio import FlaggingCallback
from gradio.components import IOComponent

from transformers import pipeline

from typing import List, Optional, Any

import argilla as rg

import os



nlp = pipeline("ner", model="deprem-ml/deprem-ner")

examples = [
  ["Lütfen yardım Akevler mahallesi Rüzgar sokak Tuncay apartmanı zemin kat Antakya akrabalarım göçük altında #hatay #Afad"]
]

def create_record(input_text, feedback):
    # define the record status based on feedback
    # default means it needs to be reviewed --> "Incorrect" or "Ambiguous"
    # validated means it's correct and has been checked --> "Correct"
    status = "Validated" if feedback == "Doğru" else "Default"
    
    # Making the prediction
    predictions = nlp(input_text, aggregation_strategy="first")
    
    # Creating the predicted entities as a list of tuples (entity, start_char, end_char, score)
    prediction = [(pred["entity_group"], pred["start"], pred["end"], pred["score"]) for pred in predictions]
    
    # Create word tokens
    batch_encoding = nlp.tokenizer(input_text)
    word_ids = sorted(set(batch_encoding.word_ids()) - {None})
    words = []
    for word_id in word_ids:
        char_span = batch_encoding.word_to_chars(word_id)
        words.append(input_text[char_span.start:char_span.end])
    
    # Building a TokenClassificationRecord
    record = rg.TokenClassificationRecord(
        text=input_text,
        tokens=words,
        prediction=prediction,
        prediction_agent="deprem-ml/deprem-ner",
        status=status,
        metadata={"feedback": feedback}
    )
    print(record)
    return record

class ArgillaLogger(FlaggingCallback):
    def __init__(self, api_url, api_key, dataset_name):
        rg.init(api_url=api_url, api_key=api_key)
        self.dataset_name = dataset_name
    def setup(self, components: List[IOComponent], flagging_dir: str):
        pass
    def flag(
        self,
        flag_data: List[Any],
        flag_option: Optional[str] = None,
        flag_index: Optional[int] = None,
        username: Optional[str] = None,
    ) -> int:
        text = flag_data[0]
        inference = flag_data[1]
        rg.log(name=self.dataset_name, records=create_record(text, flag_option))


        
gr.Interface.load(
    "models/deprem-ml/deprem-ner",
    examples=examples,
    title = "NER Adres Aktif Öğrenme Arayüzü",
    description = "Aşağıda veri girişi yapıp modelin çıktısına göre Doğru/Yanlış/Belirsiz olarak işaretleyerek modelimizi değerlendirmemize yardımcı olabilirsiniz. Not: flag'lere bir kez tıklamanız yeterlidir. Şu an arayüzü flag alındığında size feedback verecek şekilde düzeltiyoruz. ",
    allow_flagging="manual",
    flagging_callback=ArgillaLogger(
        api_url="https://sandbox.argilla.io", 
        api_key=os.getenv("TEAM_API_KEY"), 
        dataset_name="ner-flags"
    ),
    flagging_options=["Doğru", "Yanlış", "Belirsiz"]
).launch()