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from flask import Flask, request, jsonify, send_file
import os
import subprocess
import time
import threading

app = Flask(__name__)

directory = os.path.dirname(os.path.abspath(__file__))
directory = directory.replace('\\', '/')

def delete_file(file_path):
    time.sleep(300)  # 5 минут ожидания
    os.remove(file_path)

@app.route('/api/process_audio', methods=['POST'])
def process_audio():
    try:
        start_time = time.time()

        # Получаем данные
        audio_file = request.files['audio']
        audio_id = request.form['audio_id']
        audio_name = request.form['audio_name']

        # Сохраняем аудио
        audio_path = os.path.join('program_audio/audio', f'{audio_name}.mp3')
        audio_file.save(audio_path)

        # Запуск обработки аудио
        subprocess.run('starter.bat', shell=True)

        # Ждем результата
        text_file_path = os.path.join('program_audio/final_text', f'{audio_name}_final.txt')
        timeout = time.time() + 60
        while not os.path.exists(text_file_path):
            if time.time() > timeout:
                return jsonify({'error': 'Timeout'}), 500
            time.sleep(1)

        # Формируем ответ
        end_time = time.time()
        elapsed_time = end_time - start_time

        response_data = {
            'audio_id': audio_id,
            'elapsed_time': elapsed_time
        }

        # Отправляем результат
        response = send_file(text_file_path, as_attachment=True)
        
        # Запускаем удаление файла через 5 минут
        delete_thread = threading.Thread(target=delete_file, args=(text_file_path,))
        delete_thread.start()

        return response, response_data

    except Exception as e:
        return jsonify({'error': str(e)}), 500

if __name__ == '__main__':
    app.run(debug=True, port=39588, host='0.0.0.0')

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from telegram import ReplyKeyboardMarkup, ReplyKeyboardRemove
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters
def start(update, context):
    context.bot.send_message(chat_id=update.effective_chat.id, text="Привет! Я бот, который выдаёт мемы и шутки про Юлианну Караулову. Напиши /mem или /joke, чтобы получить контент.")
def send_mem(update, context):
    
    context.bot.send_photo(chat_id=update.effective_chat.id, photo=meme)
def send_joke(update, context):
   

    context.bot.send_message(chat_id=update.effective_chat.id, text=joke)
def main():
    updater = Updater(token='YOUR_TOKEN', use_context=True)
    dispatcher = updater.dispatcher

    start_handler = CommandHandler('start', start)
    mem_handler = CommandHandler('mem', send_mem)
    joke_handler = CommandHandler('joke', send_joke)

    dispatcher.add_handler(start_handler)
    dispatcher.add_handler(mem_handler)
    dispatcher.add_handler(joke_handler)

    updater.start_polling()

    updater.idle()

if __name__ == '__main__':
    main()

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import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from yellowbrick.features import Rank2D
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import r2_score
data = pd.read_csv('data.csv')
features = data[['y', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6']]
visualizer = Rank2D(features=features, algorithm='pearson')
visualizer.fit(features)
visualizer.transform(features)
visualizer.show()
plt.figure(figsize=(10, 8))
sns.heatmap(data.corr(), annot=True, cmap='coolwarm')
plt.show()
scaler = StandardScaler()
data[['y', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6']] = scaler.fit_transform(data[['y', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6']])
X = data[['x1', 'x2', 'x3', 'x4', 'x5', 'x6']]
y = data['y']
model = LinearRegression()
model.fit(X, y)
predicted_values = model.predict(X)
selector = SelectKBest(score_func=f_regression, k=3)
X_new = selector.fit_transform(X, y)
selected_features = X.columns[selector.get_support()]
model_knn = KNeighborsRegressor()
model_knn.fit(X, y)
predicted_values_knn = model_knn.predict(X)
model_tree = DecisionTreeRegressor()
model_tree.fit(X, y)
predicted_values_tree = model_tree.predict(X)
r2_linear = r2_score(y, predicted_values)
r2_knn = r2_score(y, predicted_values_knn)
r2_tree = r2_score(y, predicted_values_tree)
best_model = max(r2_linear, r2_knn, r2_tree)
plt.figure(figsize=(10, 8))
plt.****ter(y, y - predicted_values, color='blue', label='Linear Regression')
plt.axhline(y=0, color='r', linestyle='-')
plt.xlabel('Actual values')
plt.ylabel('Residuals')
plt.legend()
plt.show()
if best_model == r2_linear:
    print("Best model: Linear Regression")
elif best_model == r2_knn:
    print("Best model: K-Nearest Neighbors")
else:
    print("Best model: Decision Tree")

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@dp.message_handler(commands=['start'])
async def start(message: types.Message):
     print(message)