Update app.py

app.py

@@ -28,14 +28,15 @@ def display_images(percentage_idx, complexity_idx):
     return raw_image, embeddings_image
 
 # Define the beam prediction function (template based)
-def beam_prediction(input_data, percentage_idx, complexity_idx):
+def beam_prediction(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 (template based)
-def los_nlos_classification(input_data, percentage_idx, complexity_idx):
-    # Add your LoS/NLoS classification logic here (this is placeholder code)
+def los_nlos_classification(uploaded_file, percentage_idx, complexity_idx):
+    # Placeholder code for processing the uploaded .py file (can be extended)
+    # Add your LoS/NLoS classification logic here
     raw_img, embeddings_img = display_images(percentage_idx, complexity_idx)
     return raw_img, embeddings_img
 
@@ -68,7 +69,6 @@ with gr.Blocks(css="""
     # 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():
@@ -84,13 +84,15 @@ with gr.Blocks(css="""
             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_button.click(beam_prediction, inputs=[beam_input, percentage_slider_bp, complexity_slider_bp], outputs=[raw_img_bp, embeddings_img_bp])
+        # Instant image updates when sliders change
+        percentage_slider_bp.change(fn=beam_prediction, inputs=[percentage_slider_bp, complexity_slider_bp], outputs=[raw_img_bp, embeddings_img_bp])
+        complexity_slider_bp.change(fn=beam_prediction, 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")
-        los_input = gr.Textbox(label="Enter Input Data for LoS/NLoS Classification", placeholder="Enter data here...")
+        
+        # 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():
@@ -106,9 +108,10 @@ with gr.Blocks(css="""
             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)
 
-        # 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])
+        # Instant image updates when sliders or file input change
+        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__":