Update app.py

app.py

@@ -27,15 +27,17 @@ def display_images(percentage_idx, complexity_idx):
     # 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 beam prediction function (template based)
+def beam_prediction(input_data, percentage_idx, complexity_idx):
+    # Add your beam prediction logic here (this is placeholder code)
+    raw_img, embeddings_img = display_images(percentage_idx, complexity_idx)
+    return raw_img, embeddings_img
 
-# 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 LoS/NLoS classification function (template based)
+def los_nlos_classification(input_data, percentage_idx, complexity_idx):
+    # Add your LoS/NLoS classification logic here (this is placeholder code)
+    raw_img, embeddings_img = display_images(percentage_idx, complexity_idx)
+    return raw_img, embeddings_img
 
 # Define the Gradio interface
 with gr.Blocks(css="""
@@ -52,46 +54,61 @@ with gr.Blocks(css="""
     }
 """) as demo:
     
-    gr.Markdown("# Wireless Model Tasks")
+    # 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")
         beam_input = gr.Textbox(label="Enter Input Data for Beam Prediction", placeholder="Enter data here...")
+        
+        # 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)
+
+        # Button to trigger beam prediction
         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)
-    
+        beam_button.click(beam_prediction, inputs=[beam_input, 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")
         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)
+        # Sliders for percentage and complexity
         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
+                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 = gr.Slider(minimum=0, maximum=1, step=1, value=0, interactive=True, elem_id="vertical-slider")
+                complexity_slider_los = 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)
+        # Image 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
+            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)
 
-        # 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])
+        # Button to trigger classification
+        los_button = gr.Button("Classify")
+        los_button.click(los_nlos_classification, inputs=[los_input, percentage_slider_los, complexity_slider_los], outputs=[raw_img_los, embeddings_img_los])
 
 # Launch the app
 if __name__ == "__main__":