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Create app.py
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app.py
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import gradio as gr
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from transformers import pipeline
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from diffusers import StableDiffusionPipeline
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import torch
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import wget
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# Define the device to use (either "cuda" for GPU or "cpu" for CPU)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Load the models
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# Image captioning model to generate captions from uploaded images
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caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", device=device)
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# Stable Diffusion model for generating new images based on captions
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sd_pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to(device)
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# Load the translation model (English to Arabic)
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translator = pipeline(
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task="translation",
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model="facebook/nllb-200-distilled-600M",
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torch_dtype=torch.bfloat16,
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device=device
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)
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# Download the image
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url1 = "https://github.com/Shahad-b/Image-database/blob/main/sea.jpg?raw=true"
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sea = wget.download(url1)
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url2 = "https://github.com/Shahad-b/Image-database/blob/main/Cat.jpeg?raw=true"
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Cat = wget.download(url2)
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url3 = "https://github.com/Shahad-b/Image-database/blob/main/Car.jpeg?raw=true"
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Car = wget.download(url3)
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# Function to generate images based on the image's caption
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def generate_image_and_translate(image, num_images=1):
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# Generate caption in English from the uploaded image
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caption_en = caption_image(image)[0]['generated_text']
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# Translate the English caption to Arabic
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caption_ar = translator(caption_en, src_lang="eng_Latn", tgt_lang="arb_Arab")[0]['translation_text']
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generated_images = []
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# Generate the specified number of images based on the English caption
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for _ in range(num_images):
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generated_image = sd_pipeline(prompt=caption_en).images[0]
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generated_images.append(generated_image)
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# Return the generated images along with both captions
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return generated_images, caption_en, caption_ar
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# Set up the Gradio interface
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interface = gr.Interface(
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fn=generate_image_and_translate, # Function to call when processing input
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inputs=[
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gr.Image(type="pil", label="π€ Upload Image"), # Input for image upload
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gr.Slider(minimum=1, maximum=10, label="π’ Number of Images", value=1, step=1) # Slider to select number of images
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],
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outputs=[
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gr.Gallery(label="πΌοΈ Generated Images"),
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gr.Textbox(label="π Generated Caption (English)", interactive=False),
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gr.Textbox(label="π Translated Caption (Arabic)", interactive=False)
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],
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title="Image Generation and Captioning", # Title of the interface
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description="Upload an image to extract a caption and display it in both Arabic and English. Then, a new image will be generated based on that caption.", # Description
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examples=[ # Example input
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["sea.jpg", 3],
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["Cat.jpeg", 4],
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["Car.jpeg", 2]
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],
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theme='freddyaboulton/dracula_revamped' # Determine theme
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)
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# Launch the Gradio application
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interface.launch()
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