app.py

import gradio as gr
import tempfile
from transformers import  MT5ForConditionalGeneration, MT5Tokenizer,VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
import torch
from PIL import Image

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model_name = "SeyedAli/English-to-Persian-Translation-mT5-V1"
translation_tokenizer = MT5Tokenizer.from_pretrained(model_name)
translation_model = MT5ForConditionalGeneration.from_pretrained(model_name)

translation_model=translation_model.to(device)

def run_transaltion_model(input_string, **generator_args):
    input_ids = translation_tokenizer.encode(input_string, return_tensors="pt")
    res = translation_model.generate(input_ids, **generator_args)
    output = translation_tokenizer.batch_decode(res, skip_special_tokens=True)
    return output

model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")

model=model.to(device)

max_length = 32
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def predict_step(image_paths):
  images = []
  for image_path in image_paths:
    i_image = Image.open(image_path)
    if i_image.mode != "RGB":
      i_image = i_image.convert(mode="RGB")

    images.append(i_image)

  pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
  pixel_values = pixel_values.to(device)

  output_ids = model.generate(pixel_values, **gen_kwargs)

  preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
  preds = [pred.strip() for pred in preds]
  return run_transaltion_model(preds[0])[0]

def ImageCaptioning(image):
    with tempfile.NamedTemporaryFile(suffix=".png") as temp_image_file:
        # Copy the contents of the uploaded image file to the temporary file
        Image.fromarray(image).save(temp_image_file.name)
        # Load the image file using Pillow
        caption=predict_step([temp_image_file.name])
        return caption

iface = gr.Interface(fn=ImageCaptioning, inputs="image", outputs="text")
iface.launch(share=False)