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import threading
import queue
import time
import os
import nltk
import re
import json
from flask import Flask
from flask_cors import CORS
from api import *
from extensions import *
from constants import *
from configs import *
from tokenxxx import *
from models import *
from model_loader import *
from utils import *
from background_tasks import generate_and_queue_text, background_training, background_reasoning_queue
from text_generation import *
from sadtalker_utils import *
import torch

state_dict = None
enc = None
config = None
model_gpt2 = None
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
news_clf = None
tfidf_vectorizer = None
text_queue = queue.Queue()
categories = None
background_threads = []
feedback_queue = queue.Queue()
reasoning_queue = queue.Queue()
seen_responses = set()
dialogue_history = []
vocabulary = set()
word_to_index = {}
index_to_word = []
translation_model = None
sp = None
codegen_model = None
codegen_tokenizer = None
codegen_vocabulary = None
codegen_index_to_word = None
codegen_word_to_index = None
summarization_model = None
summarization_vocabulary = set()
summarization_word_to_index = {}
summarization_index_to_word = []
sadtalker_instance = None
imagegen_model = None
image_to_3d_model = None
text_to_video_model = None
stream_type = "text"
sentiment_model = None
stt_model = None
tts_model = None
musicgen_model = None

def load_models():
    global model_gpt2, enc, translation_model, codegen_model, codegen_tokenizer, codegen_vocabulary, codegen_index_to_word, codegen_word_to_index, summarization_model, imagegen_model, image_to_3d_model, text_to_video_model, sadtalker_instance, sentiment_model, stt_model, tts_model, musicgen_model, checkpoint_path, gfpgan_model_file, restoreformer_model_file, codeformer_model_file, realesrgan_model_file, kp_file, aud_file, wav_file, gen_file, mapx_file, den_file
    model_gpt2, enc = initialize_gpt2_model(GPT2_FOLDER, {MODEL_FILE: MODEL_URL, ENCODER_FILE: ENCODER_URL, VOCAB_FILE: VOCAB_URL, CONFIG_FILE: GPT2CONFHG})
    translation_model = initialize_translation_model(TRANSLATION_FOLDER, TRANSLATION_MODEL_FILES_URLS)
    codegen_model, codegen_tokenizer, codegen_vocabulary, codegen_index_to_word, codegen_word_to_index = initialize_codegen_model(CODEGEN_FOLDER, CODEGEN_FILES_URLS)
    summarization_model, _, _, _ = initialize_summarization_model(SUMMARIZATION_FOLDER, SUMMARIZATION_FILES_URLS)
    imagegen_model = initialize_imagegen_model(IMAGEGEN_FOLDER, IMAGEGEN_FILES_URLS)
    image_to_3d_model = initialize_image_to_3d_model(IMAGE_TO_3D_FOLDER, IMAGE_TO_3D_FILES_URLS)
    text_to_video_model = initialize_text_to_video_model(TEXT_TO_VIDEO_FOLDER, TEXT_TO_VIDEO_FILES_URLS)
    sentiment_model = initialize_sentiment_model(SENTIMENT_FOLDER, SENTIMENT_FILES_URLS)
    stt_model = initialize_stt_model(STT_FOLDER, STT_FILES_URLS)
    tts_model = initialize_tts_model(TTS_FOLDER, TTS_FILES_URLS)
    musicgen_model = initialize_musicgen_model(MUSICGEN_FOLDER, MUSICGEN_FILES_URLS)

class SimpleClassifier(torch.nn.Module):
    def __init__(self, vocab_size, num_classes):
        super(SimpleClassifier, self).__init__()
        self.embedding = torch.nn.Embedding(vocab_size, 128)
        self.linear = torch.nn.Linear(128, num_classes)
    def forward(self, x):
        embedded = self.embedding(x)
        pooled = torch.mean(embedded, dim=1)
        return self.linear(pooled)

def tokenize_text(text):
    global vocabulary, word_to_index, index_to_word
    tokens = text.lower().split()
    for token in tokens:
        if token not in vocabulary:
            vocabulary.add(token)
            word_to_index[token] = len(index_to_word)
            index_to_word.append(token)
    return tokens

def text_to_vector(text):
    global vocabulary, word_to_index
    tokens = tokenize_text(text)
    vector = torch.zeros(len(vocabulary))
    for token in tokens:
        if token in word_to_index:
            vector[word_to_index[token]] += 1
    return vector

if __name__ == "__main__":
    nltk.download('punkt')
    load_models()
    categories = ['Category1', 'Category2', 'Category3', 'Category4', 'Category5']
    import background_tasks
    background_tasks.categories = categories
    background_tasks.text_queue = text_queue
    background_tasks.reasoning_queue = reasoning_queue
    background_threads.append(threading.Thread(target=generate_and_queue_text, args=('en',), daemon=True))
    background_threads.append(threading.Thread(target=generate_and_queue_text, args=('es',), daemon=True))
    background_threads.append(threading.Thread(target=background_training, daemon=True))
    background_threads.append(threading.Thread(target=background_reasoning_queue, daemon=True))
    for thread in background_threads:
        thread.start()
    app.run(host='0.0.0.0', port=7860)