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41b9a92 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration, MarianMTModel, MarianTokenizer
from PIL import Image
import torch
# Load translation model
translator_model_ar = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-ar")
translator_tokenizer_ar = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ar")
# Load BLIP model (fine-tuned)
model_path = "saja003/MuniVis"
processor_en = BlipProcessor.from_pretrained(model_path)
model_en = BlipForConditionalGeneration.from_pretrained(model_path)
model_en.eval()
# Function to describe image
def describe_image(image, language):
if language == "Arabic":
inputs = processor_en(image, return_tensors="pt")
with torch.no_grad():
out = model_en.generate(**inputs)
description = processor_en.decode(out[0], skip_special_tokens=True)
inputs_ar = translator_tokenizer_ar(description, return_tensors="pt")
with torch.no_grad():
translated_tokens = translator_model_ar.generate(**inputs_ar)
arabic_description = translator_tokenizer_ar.decode(translated_tokens[0], skip_special_tokens=True)
return arabic_description
elif language == "English":
inputs_en = processor_en(image, return_tensors="pt")
with torch.no_grad():
out_en = model_en.generate(**inputs_en)
description_en = processor_en.decode(out_en[0], skip_special_tokens=True)
return description_en
# Gradio UI
iface = gr.Interface(
fn=describe_image,
inputs=[
gr.Image(type="pil", label="Upload an Image"),
gr.Dropdown(choices=["Arabic", "English"], label="Select Language")
],
outputs="text",
title="Image Captioning with Arabic Translation",
description="Select the language and upload an image to get a description."
)
if __name__ == "__main__":
iface.launch()
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