app.py

import gradio as gr
import tensorflow as tf
import numpy as np
import pickle
import torch
from transformers import AutoTokenizer, AutoModel

# добавляем нужные импорты
import re
import string
import emoji
import pymorphy2
import joblib
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords

# ---------------------------
# ЗАГРУЗКА BERT
# ---------------------------
MODEL_NAME = 'sberbank-ai/ruBert-base'
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
bert_model = AutoModel.from_pretrained(MODEL_NAME)

# ---------------------------
# ЗАГРУЗКА SCALER и KERAS-МОДЕЛИ
# ---------------------------
with open("scaler.joblib", "rb") as f:
    scaler = joblib.load(f)

keras_model = tf.keras.models.load_model("tf.keras", compile=False)


EMOTIONS = ["страх", "гнев", "грусть", "радость"]

# ---------------------------------------
# ФУНКЦИИ ДЛЯ ОБРАБОТКИ ЭМОДЗИ (добавь свои)
# ---------------------------------------
def remove_duplicate_emojis(text):
    return text  # заглушка — поставь свою реализацию

def is_emoji_spam(text):
    return False  # заглушка — поставь свою реализацию

def remove_all_emojis(text):
    return text  # заглушка — поставь свою реализацию


# ---------------------------
# ПРЕДОБРАБОТКА ТЕКСТА
# ---------------------------
def preprocess_text(text):

    text = remove_duplicate_emojis(text)
    if is_emoji_spam(text):
        text = remove_all_emojis(text)

    text = str(text).lower()
    text = re.sub(r'http\S+|www\S+|https\S+', '', text)
    text = re.sub(r'@\w+|#\w+', '', text)
    text = text.translate(str.maketrans('', '', string.punctuation))

    text = emoji.demojize(text)

    text = re.sub(r'\d+', '', text)

    try:
        tokens = word_tokenize(text, language="russian")
    except:
        tokens = text.split()

    try:
        stop_words = set(stopwords.words('russian'))
    except:
        stop_words = set()

    tokens = [
        word for word in tokens
        if (word.isalpha() or (word.startswith(':') and word.endswith(':')))
        and word not in stop_words
        and len(word) > 2
    ]

    try:
        lemmatizer = pymorphy2.MorphAnalyzer()
        tokens = [lemmatizer.parse(word)[0].normal_form for word in tokens]
    except:
        pass

    return ' '.join(tokens)