import pandas as pd
import numpy as np
from simpletransformers.classification import ClassificationModel
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from collections import Counter
import nltk
from nltk.corpus import stopwords
import re
import string
import gradio as gr


nltk.download('stopwords')
stop_words_list = stopwords.words('turkish')
false_text = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']


def preprocess_text(text):
    # Küçük harflere çevirme
    text = text.lower()
    # Satır sonu karakterlerini kaldırma
    import re
    text = re.sub(r'\n', ' ', text)
    # Rakamları kaldırma
    text = re.sub(r'\d', '', text)
    # Noktalama işaretlerini kaldırma
    import string
    text = text.translate(str.maketrans("", "", string.punctuation))
    # Stop-words'leri kaldırma
    words = text.split()
    words = [word for word in words if not word in stop_words_list]
    # Veri setindeki hatalı verilerin kaldırılması
    words = [word for word in words if not word in false_text]
    # Tekrarlanan karakterlerin kaldırılması
    words = [re.sub(r'(.)\1{1,}', r'\1\1', word) for word in words]
    # Tekrarlanan boşlukların kaldırılması
    words = [word.strip() for word in words if len(word.strip()) > 1]

    text = " ".join(words)
    return text


def predict(texts):
    model_path = "bert_model"
    model = ClassificationModel('bert', model_path, use_cuda=False)
    predictions, _ = model.predict(texts)
    return [result_predict(prediction) for prediction in predictions]

def result_predict(num):
    if num == 4:
        return 'OTHER'
    elif num == 1:
        return 'RACIST'
    elif num == 0:
        return 'INSULT'
    elif num == 3:
        return 'PROFANITY'
    elif num == 2:
        return 'SEXIST'



# Gradio Interface oluşturun
iface = gr.Interface(
    fn=predict, 
    inputs="text",
    outputs=["text", "text"],
    live=True,
    layout="horizontal",
    title="BERT Text Classification"
)

iface.launch()