app_gr.py

'''import sys
import asyncio
from aiohttp import web, WSMsgType
import json
from json import JSONEncoder
import numpy as np
import uuid
import logging
import os
import signal
from typing import Dict, Any, List, Optional
import base64
import io
from PIL import Image
import gradio as gr
import cv2

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

def SIGSEGV_signal_arises(signalNum, stack):
    logger.critical(f"{signalNum} : SIGSEGV arises")
    logger.critical(f"Stack trace: {stack}")

signal.signal(signal.SIGSEGV, SIGSEGV_signal_arises)

from loader import initialize_models
from engine import Engine, base64_data_uri_to_PIL_Image
from pathlib import Path

import cv2

# Global constants
DATA_ROOT = os.environ.get('DATA_ROOT', '/tmp/data')
MODELS_DIR = os.path.join(DATA_ROOT, "models")

async def setup():
    live_portrait = await initialize_models()
    engine = Engine(live_portrait=live_portrait)
    
    def get_all_frames(video_path):
      cap = cv2.VideoCapture(video_path)
      
      frames = []
      while True:
          ret, frame = cap.read()
          
          if not ret:
              break
          
          frames.append(frame)
      
      cap.release()
      return frames

    async def return_image(image):
      binary_data = Path(image).read_bytes()
      res = await engine.load_image(binary_data)
      id = res['u']
      _, image = await engine.transform_image(id, {
              "aaa": -10,
              "eee": -10,
              "woo": -12
      })
      
      return image

    async def return_video(video):
      print(video)
      gr.Info("Extracting frames..")
      frames = get_all_frames(video)
      gr.Info("Loading frames..")
      res = await engine.load_frames(frames)
      id = res['u']

      height, width, _ = frames[0].shape
      output_file = "output_video.mp4"
      fourcc = cv2.VideoWriter_fourcc(*'mp4v')
      video_writer = cv2.VideoWriter(output_file, fourcc, 24.0, (width, height))

      gr.Info("Processing..")
      async for image in engine.transform_video(id, {
              "aaa": -10,
              "eee": -10,
              "woo": -12
      }):
        bgr_frame = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
        video_writer.write(cv2.cvtColor(bgr_frame, cv2.COLOR_BGR2RGB))

      video_writer.release()
      return output_file

    with gr.Blocks(title="Retorno de Imagem") as interface:
      gr.Markdown("## 📼 Conversor de Vídeo para Imagem")
      
      with gr.Row():  # Organiza os componentes em uma linha
          video_input = gr.Video(label="Carregue seu vídeo")
          image_output = gr.Video(label="Imagem Processada",)
      
      submit_btn = gr.Button("🔁 Processar", variant="primary")  # Estilo primário
      
      submit_btn.click(
          fn=return_video,  # Sua função de processamento
          inputs=video_input,
          outputs=image_output,
      )

    interface.launch(share=True)


if __name__ == "__main__":
    asyncio.run(setup())'''

file_path = "/home/user/.pyenv/versions/3.10.16/lib/python3.10/site-packages/basicsr/data/degradations.py"

# Ler o conteúdo do arquivo
with open(file_path, "r") as file:
    content = file.read()

# Substituir a linha desejada
new_content = content.replace(
    "from torchvision.transforms.functional_tensor import rgb_to_grayscale",
    "from torchvision.transforms.functional import rgb_to_grayscale"
)

# Salvar o conteúdo atualizado
with open(file_path, "w") as file:
    file.write(new_content)

print("Substituição concluída!")

import sys
import asyncio
from aiohttp import web, WSMsgType
import json
from json import JSONEncoder
import numpy as np
import uuid
import logging
import os
import signal
from typing import Dict, Any, List, Optional
import base64
import io
from PIL import Image
import gradio as gr
import cv2

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

def SIGSEGV_signal_arises(signalNum, stack):
    logger.critical(f"{signalNum} : SIGSEGV arises")
    logger.critical(f"Stack trace: {stack}")

signal.signal(signal.SIGSEGV, SIGSEGV_signal_arises)

from loader import initialize_models
from engine import Engine, base64_data_uri_to_PIL_Image
from pathlib import Path

import cv2

# Global constants
DATA_ROOT = os.environ.get('DATA_ROOT', '/tmp/data')
MODELS_DIR = os.path.join(DATA_ROOT, "models")

async def setup():
    live_portrait = await initialize_models()
    engine = Engine(live_portrait=live_portrait)

    async def return_video(video):
      gr.Info("Processing video..")
      output = await engine.process_video(video, {
              "aaa": -10,
              "eee": -10,
              "woo": -12
      })

      return output

    interface = gr.Interface(
        fn=return_video,  # Your function to process video
        inputs=gr.Video(label="Carregue seu vídeo"),
        outputs=gr.Video(label="Imagem Processada"),
        title="Retorno de Imagem",
        description="📼 Conversor de Vídeo para Imagem"
    )

    interface.launch(share=True)


if __name__ == "__main__":
    asyncio.run(setup())