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Update app.py
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app.py
CHANGED
@@ -4,6 +4,7 @@ from PIL import Image
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import tempfile
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import requests
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from io import BytesIO
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# Initialize the Hugging Face API clients
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captioning_client = Client("fancyfeast/joy-caption-pre-alpha")
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@@ -31,12 +32,23 @@ def generate_image_from_caption(caption):
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num_inference_steps=28,
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api_name="/infer"
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)
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response = requests.get(image_url)
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return Image.open(BytesIO(response.content))
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# Main function to handle the upload and generate images and captions in a loop
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def process_image(image, iterations):
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generated_images = []
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captions = []
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@@ -47,31 +59,41 @@ def process_image(image, iterations):
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caption = caption_image(current_image)
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captions.append(caption)
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# Generate a new image based on the caption
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new_image = generate_image_from_caption(caption)
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generated_images.append(new_image)
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# Set the newly generated image as the current image for the next iteration
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current_image = new_image
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-
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# Gradio Interface
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with gr.Blocks() as demo:
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with gr.Row():
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image_input = gr.Image(type="pil", label="Upload an Image")
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iterations_input = gr.Number(value=3, label="Number of Iterations")
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with gr.Row():
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output_images = gr.Gallery(label="Generated Images")
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output_captions = gr.Textbox(label="Generated Captions")
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generate_button = gr.Button("Generate")
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generate_button.click(
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fn=process_image,
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inputs=[image_input, iterations_input],
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outputs=[output_images, output_captions]
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)
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# Launch the app
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import tempfile
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import requests
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from io import BytesIO
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import os
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# Initialize the Hugging Face API clients
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captioning_client = Client("fancyfeast/joy-caption-pre-alpha")
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num_inference_steps=28,
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api_name="/infer"
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)
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# Check if the response is a URL or a local path
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image_url = image[0]
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if not image_url.startswith("http"):
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# Handle local file path
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with open(image_url, "rb") as file:
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return Image.open(file)
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# Fetch image from URL
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response = requests.get(image_url)
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return Image.open(BytesIO(response.content))
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# Main function to handle the upload and generate images and captions in a loop
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def process_image(image, iterations):
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# Ensure iterations is an integer
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iterations = int(round(iterations))
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generated_images = []
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captions = []
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caption = caption_image(current_image)
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captions.append(caption)
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# Notify that the caption has been made
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status = f"Caption made: {caption}"
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# Generate a new image based on the caption
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new_image = generate_image_from_caption(caption)
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generated_images.append(new_image)
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# Notify that the image has been generated
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status += f"\nImage generated for iteration {i+1}"
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# Set the newly generated image as the current image for the next iteration
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current_image = new_image
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# Notify that the process is completed
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status += "\nProcessing complete!"
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return generated_images, captions, status
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# Gradio Interface
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with gr.Blocks() as demo:
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with gr.Row():
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image_input = gr.Image(type="pil", label="Upload an Image")
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iterations_input = gr.Number(value=3, label="Number of Iterations", precision=0)
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with gr.Row():
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output_images = gr.Gallery(label="Generated Images")
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output_captions = gr.Textbox(label="Generated Captions")
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status_output = gr.Textbox(label="Status Updates", lines=10)
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generate_button = gr.Button("Generate")
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generate_button.click(
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fn=process_image,
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inputs=[image_input, iterations_input],
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outputs=[output_images, output_captions, status_output]
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)
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# Launch the app
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