app.py

import torch
from PIL import Image
from RealESRGAN import RealESRGAN
import gradio as gr
import logging
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackContext
from telegram import Update


logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
                    level=logging.INFO)

# Your API Token
TOKEN = "6231949511:AAH7-oU213cfrGYcfIMeaYOQUDf9kZoXc_0"

# Initialize the Updater
updater = Updater(token=TOKEN, use_context=True)
dispatcher = updater.dispatcher

# Define a command handler
def start(update: Update, context: CallbackContext):
    context.bot.send_message(chat_id=update.effective_chat.id, text="Hello! I'm your bot. How can I help you?")

# Register the command handler
start_handler = CommandHandler('start', start)
dispatcher.add_handler(start_handler)

# Define a function to handle user messages
def echo(update: Update, context: CallbackContext):
    context.bot.send_message(chat_id=update.effective_chat.id, text=update.message.text)

# Register the message handler
message_handler = MessageHandler(Filters.text & ~Filters.command, echo)
dispatcher.add_handler(message_handler)

# Start the bot
updater.start_polling()

# Run the bot until you send a signal to stop
updater.idle()












device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model2 = RealESRGAN(device, scale=2)
model2.load_weights('weights/RealESRGAN_x2.pth', download=True)
model4 = RealESRGAN(device, scale=4)
model4.load_weights('weights/RealESRGAN_x4.pth', download=True)
model8 = RealESRGAN(device, scale=8)
model8.load_weights('weights/RealESRGAN_x8.pth', download=True)


def inference(image, size):
    if size == '2x':
        result = model2.predict(image.convert('RGB'))
    elif size == '4x':
        result = model4.predict(image.convert('RGB'))
    else:
        result = model8.predict(image.convert('RGB'))
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    return result


title = "Face Real ESRGAN UpScale: 2x 4x 8x"
description = "This is an unofficial demo for Real-ESRGAN. Scales the resolution of a photo. This model shows better results on faces compared to the original version.<br>Telegram BOT: https://t.me/restoration_photo_bot"
article = "<div style='text-align: center;'>Twitter <a href='https://twitter.com/DoEvent' target='_blank'>Max Skobeev</a> | <a href='https://huggingface.co/sberbank-ai/Real-ESRGAN' target='_blank'>Model card</a>/<div>"


gr.Interface(inference,
    [gr.Image(type="pil"), 
    gr.Radio(['2x', '4x', '8x'], 
    type="value",
    value='2x',
    label='Resolution model')], 
    gr.Image(type="pil", label="Output"),
    title=title,
    description=description,
    article=article,
    examples=[['groot.jpeg', "2x"]],
    allow_flagging='never',
    cache_examples=False,
    ).queue(concurrency_count=1).launch(show_error=True)