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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")
# Specific values for percentage and complexity
percentage_values = [10, 30, 50, 70, 100]
complexity_values = [16, 32]
# Function to load and display images based on user selection
def display_images(percentage_idx, complexity_idx):
# Map the slider index to the actual value
percentage = percentage_values[percentage_idx]
complexity = complexity_values[complexity_idx]
# 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 beam prediction function
def beam_prediction(input_data):
# Add your beam prediction logic here
return {"Prediction": "Beam X", "Confidence": "95%"}
# Define the LoS/NLoS classification function
def los_nlos_classification(input_data):
# Add your LoS/NLoS classification logic here
return {"Classification": "LoS", "Confidence": "98%"}
# Define the Gradio interface
with gr.Blocks(css="""
.vertical-slider input[type=range] {
writing-mode: bt-lr; /* IE */
-webkit-appearance: slider-vertical; /* WebKit */
width: 8px;
height: 200px;
}
.slider-container {
display: inline-block;
margin-right: 50px;
text-align: center;
}
""") as demo:
gr.Markdown("# Wireless Model Tasks")
# Tabs for Beam Prediction and LoS/NLoS Classification
with gr.Tab("Beam Prediction Task"):
gr.Markdown("### Beam Prediction Task")
beam_input = gr.Textbox(label="Enter Input Data for Beam Prediction", placeholder="Enter data here...")
beam_button = gr.Button("Predict Beam")
beam_output = gr.JSON(label="Beam Prediction Result")
beam_button.click(beam_prediction, inputs=beam_input, outputs=beam_output)
with gr.Tab("LoS/NLoS Classification Task"):
gr.Markdown("### LoS/NLoS Classification Task")
los_input = gr.Textbox(label="Enter Input Data for LoS/NLoS Classification", placeholder="Enter data here...")
los_button = gr.Button("Classify")
los_output = gr.JSON(label="LoS/NLoS Classification Result")
los_button.click(los_nlos_classification, inputs=los_input, outputs=los_output)
with gr.Tab("Raw vs. Embeddings Inference Results"):
gr.Markdown("Use the sliders to adjust the percentage of data for training and task complexity.")
# Layout for vertical side-by-side sliders (using CSS to rotate sliders)
with gr.Row():
# Column for percentage slider
with gr.Column(elem_id="slider-container"):
gr.Markdown("Percentage of Data for Training")
percentage_slider = gr.Slider(minimum=0, maximum=4, step=1, value=0, interactive=True, elem_id="vertical-slider")
# Column for complexity slider
with gr.Column(elem_id="slider-container"):
gr.Markdown("Task Complexity")
complexity_slider = gr.Slider(minimum=0, maximum=1, step=1, value=0, interactive=True, elem_id="vertical-slider")
# 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 sliders change
percentage_slider.change(fn=display_images, inputs=[percentage_slider, complexity_slider], outputs=[raw_img, embeddings_img])
complexity_slider.change(fn=display_images, inputs=[percentage_slider, complexity_slider], outputs=[raw_img, embeddings_img])
# Launch the app
if __name__ == "__main__":
demo.launch()
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