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import gradio as gr
import os
from PIL import Image
# Paths to the images folder
RAW_PATH = os.path.join("images", "raw")
EMBEDDINGS_PATH = os.path.join("images", "embeddings")
# Function to load and display images based on user selection
def display_images(percentage, complexity):
# Generate the paths to the images
raw_image_path = os.path.join(RAW_PATH, f"percentage_{percentage}_complexity_{complexity}.png")
embeddings_image_path = os.path.join(EMBEDDINGS_PATH, f"percentage_{percentage}_complexity_{complexity}.png")
# Load images using PIL
raw_image = Image.open(raw_image_path)
embeddings_image = Image.open(embeddings_image_path)
# Return the loaded images
return raw_image, embeddings_image
# Define the Gradio interface
data_percentage_options = [10, 30, 50, 70, 100]
task_complexity_options = [16, 32]
# Define the layout and appearance of the UI
with gr.Blocks() as demo:
gr.Markdown("# Raw vs. Embeddings Inference Results")
gr.Markdown("Select a data percentage and task complexity to view the corresponding inference result for raw channels and embeddings.")
# Add scrollable area for configurations
with gr.Scroll(height=150): # Scrollable area for the configurations
with gr.Row():
percentage_dropdown = gr.Dropdown(data_percentage_options, label="Percentage of Data for Training", value=10)
complexity_radio = gr.Radio(task_complexity_options, label="Task Complexity", value=16)
# Outputs (display the images side by side and set a smaller size for the images)
with gr.Row():
raw_img = gr.Image(label="Raw Channels", type="pil", width=300, height=300, interactive=False) # Smaller image size
embeddings_img = gr.Image(label="Embeddings", type="pil", width=300, height=300, interactive=False) # Smaller image size
# Trigger image updates when inputs change
percentage_dropdown.change(fn=display_images, inputs=[percentage_dropdown, complexity_radio], outputs=[raw_img, embeddings_img])
complexity_radio.change(fn=display_images, inputs=[percentage_dropdown, complexity_radio], outputs=[raw_img, embeddings_img])
# Launch the app
if __name__ == "__main__":
demo.launch()
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