Update app.py

app.py

@@ -1,3 +1,34 @@
+import gradio as gr
+import os
+from PIL import Image
+import numpy as np
+
+# Paths to the predefined images folder
+RAW_PATH = os.path.join("images", "raw")
+EMBEDDINGS_PATH = os.path.join("images", "embeddings")
+GENERATED_PATH = os.path.join("images", "generated")
+
+# Specific values for percentage and complexity
+percentage_values = [10, 30, 50, 70, 100]
+complexity_values = [16, 32]
+
+# Function to load and display predefined images based on user selection
+def display_predefined_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
+
 import torch
 from transformers import AutoModel  # Assuming you use a transformer-like model in your LWM repo
 import numpy as np
@@ -50,3 +81,90 @@ def process_python_file(uploaded_file, percentage_idx, complexity_idx):
 
     except Exception as e:
         return str(e), str(e)
+
+
+# Function to handle logic based on whether a file is uploaded or not
+def los_nlos_classification(file, percentage_idx, complexity_idx):
+    if file is not None:
+        # Process the uploaded file and generate new images
+        return process_python_file(file, percentage_idx, complexity_idx)
+    else:
+        # Display predefined images if no file is uploaded
+        return display_predefined_images(percentage_idx, complexity_idx)
+
+# 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:
+    
+    # Contact Section
+    gr.Markdown(
+        """
+        ## Contact
+        <div style="display: flex; align-items: center;">
+            <a target="_blank" href="mailto:info@wirelessmodel.com"><img src="https://img.shields.io/badge/email-info@wirelessmodel.com-blue.svg?logo=gmail " alt="Email"></a>&nbsp;&nbsp;
+            <a target="_blank" href="https://telegram.me/wirelessmodel"><img src="https://img.shields.io/badge/telegram-@wirelessmodel-blue.svg?logo=telegram " alt="Telegram"></a>&nbsp;&nbsp;
+        </div>
+        """
+    )
+    
+    # Tabs for Beam Prediction and LoS/NLoS Classification
+    with gr.Tab("Beam Prediction Task"):
+        gr.Markdown("### Beam Prediction Task")
+        
+        # Sliders for percentage and complexity
+        with gr.Row():
+            with gr.Column(elem_id="slider-container"):
+                gr.Markdown("Percentage of Data for Training")
+                percentage_slider_bp = gr.Slider(minimum=0, maximum=4, step=1, value=0, interactive=True, elem_id="vertical-slider")
+            with gr.Column(elem_id="slider-container"):
+                gr.Markdown("Task Complexity")
+                complexity_slider_bp = gr.Slider(minimum=0, maximum=1, step=1, value=0, interactive=True, elem_id="vertical-slider")
+
+        # Image outputs (display the images side by side and set a smaller size for the images)
+        with gr.Row():
+            raw_img_bp = gr.Image(label="Raw Channels", type="pil", width=300, height=300, interactive=False)
+            embeddings_img_bp = gr.Image(label="Embeddings", type="pil", width=300, height=300, interactive=False)
+
+        # Instant image updates when sliders change
+        percentage_slider_bp.change(fn=display_predefined_images, inputs=[percentage_slider_bp, complexity_slider_bp], outputs=[raw_img_bp, embeddings_img_bp])
+        complexity_slider_bp.change(fn=display_predefined_images, inputs=[percentage_slider_bp, complexity_slider_bp], outputs=[raw_img_bp, embeddings_img_bp])
+
+    with gr.Tab("LoS/NLoS Classification Task"):
+        gr.Markdown("### LoS/NLoS Classification Task")
+        
+        # File uploader for uploading .py file
+        file_input = gr.File(label="Upload .py File", file_types=[".py"])
+
+        # Sliders for percentage and complexity
+        with gr.Row():
+            with gr.Column(elem_id="slider-container"):
+                gr.Markdown("Percentage of Data for Training")
+                percentage_slider_los = gr.Slider(minimum=0, maximum=4, step=1, value=0, interactive=True, elem_id="vertical-slider")
+            with gr.Column(elem_id="slider-container"):
+                gr.Markdown("Task Complexity")
+                complexity_slider_los = gr.Slider(minimum=0, maximum=1, step=1, value=0, interactive=True, elem_id="vertical-slider")
+
+        # Image outputs (display the images side by side and set a smaller size for the images)
+        with gr.Row():
+            raw_img_los = gr.Image(label="Raw Channels", type="pil", width=300, height=300, interactive=False)
+            embeddings_img_los = gr.Image(label="Embeddings", type="pil", width=300, height=300, interactive=False)
+
+        # Instant image updates based on file upload or slider changes
+        file_input.change(fn=los_nlos_classification, inputs=[file_input, percentage_slider_los, complexity_slider_los], outputs=[raw_img_los, embeddings_img_los])
+        percentage_slider_los.change(fn=los_nlos_classification, inputs=[file_input, percentage_slider_los, complexity_slider_los], outputs=[raw_img_los, embeddings_img_los])
+        complexity_slider_los.change(fn=los_nlos_classification, inputs=[file_input, percentage_slider_los, complexity_slider_los], outputs=[raw_img_los, embeddings_img_los])
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()