Update app.py from anycoder
Browse files
app.py
CHANGED
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@@ -15,6 +15,11 @@ from diffusers.pipelines.glm_image import GlmImagePipeline
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from PIL import Image
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import time
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import random
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# Load the GLM-Image model directly with bfloat16 precision
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print("Loading GLM-Image model... This may take a few minutes.")
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@@ -122,7 +127,8 @@ def process_image(
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progress: Gradio progress tracker (handled automatically by Gradio 6)
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Returns:
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Tuple of (output_image, status_message)
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"""
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try:
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if image is None:
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@@ -159,15 +165,20 @@ def process_image(
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output_image = result.images[0]
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if progress:
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progress(1.0, desc="Complete!")
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status = f"Successfully generated! ({height}x{width}, {num_inference_steps} steps)"
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return output_image, status
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except Exception as e:
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error_msg = f"Error: {str(e)}"
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return None, error_msg
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def update_dimensions_from_image(image: Image.Image) -> tuple:
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"""Update height and width based on uploaded image dimensions."""
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@@ -404,14 +415,24 @@ with gr.Blocks(fill_height=True) as demo:
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guidance_scale,
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seed
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],
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outputs=[output_image, status]
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)
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def enable_download(img):
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return {
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download_btn: gr.DownloadButton(
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value=
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interactive=True,
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variant="secondary"
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)
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@@ -424,13 +445,6 @@ with gr.Blocks(fill_height=True) as demo:
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)
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}
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output_image.change(
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fn=enable_download,
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inputs=output_image,
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outputs=[download_btn],
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api_visibility="private"
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)
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def clear_all():
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"""Clear all inputs and outputs."""
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return {
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from PIL import Image
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import time
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import random
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import os
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import tempfile
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# Create a temp directory for saving images
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TEMP_DIR = tempfile.mkdtemp(prefix="glm_image_")
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# Load the GLM-Image model directly with bfloat16 precision
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print("Loading GLM-Image model... This may take a few minutes.")
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progress: Gradio progress tracker (handled automatically by Gradio 6)
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Returns:
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Tuple of (output_image, status_message, file_path)
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file_path: Path to the saved image file for download (or None if failed)
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"""
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try:
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if image is None:
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output_image = result.images[0]
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# Save image to temp file for download button
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timestamp = int(time.time() * 1000)
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temp_path = os.path.join(TEMP_DIR, f"glm_output_{timestamp}.png")
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output_image.save(temp_path, format="PNG")
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if progress:
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progress(1.0, desc="Complete!")
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status = f"Successfully generated! ({height}x{width}, {num_inference_steps} steps)"
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return output_image, status, temp_path
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except Exception as e:
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error_msg = f"Error: {str(e)}"
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return None, error_msg, None
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def update_dimensions_from_image(image: Image.Image) -> tuple:
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"""Update height and width based on uploaded image dimensions."""
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guidance_scale,
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seed
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],
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outputs=[output_image, status, download_btn]
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)
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def enable_download(img, file_path):
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"""
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Enable the download button with the file path.
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Args:
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img: The PIL Image (unused, kept for compatibility)
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file_path: Path to the saved image file
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Returns:
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Update dict for the download button
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"""
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if file_path is not None and os.path.exists(file_path):
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return {
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download_btn: gr.DownloadButton(
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value=file_path, # <- Use file path, not Image object
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interactive=True,
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variant="secondary"
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)
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)
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}
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def clear_all():
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"""Clear all inputs and outputs."""
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return {
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