Update app.py

app.py

@@ -18,6 +18,57 @@ EMBEDDINGS_PATH = os.path.join("images", "embeddings")
 # Specific values for percentage of data for training
 percentage_values = (np.arange(9) + 1)*10
 
+
+
+
+def beam_prediction_task(data_percentage, task_complexity):
+    # Folder naming convention based on input_type, data_percentage, and task_complexity
+    raw_folder = f"images/raw_{data_percentage/100:.1f}_{task_complexity}"
+    embeddings_folder = f"images/embeddings_{data_percentage/100:.1f}_{task_complexity}"
+
+    # Process raw confusion matrix
+    raw_cm = compute_average_confusion_matrix(raw_folder)
+    if raw_cm is not None:
+        raw_cm_path = os.path.join(raw_folder, "confusion_matrix_raw.png")
+        plot_confusion_matrix(raw_cm, classes=np.arange(raw_cm.shape[0]), title=f"Raw Confusion Matrix ({data_percentage}% data, {task_complexity} beams)", save_path=raw_cm_path)
+        raw_img = Image.open(raw_cm_path)
+    else:
+        raw_img = None
+
+    # Process embeddings confusion matrix
+    embeddings_cm = compute_average_confusion_matrix(embeddings_folder)
+    if embeddings_cm is not None:
+        embeddings_cm_path = os.path.join(embeddings_folder, "confusion_matrix_embeddings.png")
+        plot_confusion_matrix(embeddings_cm, classes=np.arange(embeddings_cm.shape[0]), title=f"Embeddings Confusion Matrix ({data_percentage}% data, {task_complexity} beams)", save_path=embeddings_cm_path)
+        embeddings_img = Image.open(embeddings_cm_path)
+    else:
+        embeddings_img = None
+
+    return raw_img, embeddings_img
+
+
+# Function to compute the average confusion matrix across CSV files in a folder
+def compute_average_confusion_matrix(folder):
+    confusion_matrices = []
+    for file in os.listdir(folder):
+        if file.endswith(".csv"):
+            data = pd.read_csv(os.path.join(folder, file))
+            y_true = data["Target"]
+            y_pred = data["Top-1 Prediction"]
+            num_labels = len(np.unique(y_true))
+            cm = confusion_matrix(y_true, y_pred, labels=np.arange(num_labels))
+            confusion_matrices.append(cm)
+
+    if confusion_matrices:
+        avg_cm = np.mean(confusion_matrices, axis=0)
+        return avg_cm
+    else:
+        return None
+
+
+
+
+
 # Custom class to capture print output
 class PrintCapture(io.StringIO):
     def __init__(self):
@@ -263,12 +314,6 @@ def process_hdf5_file(uploaded_file, percentage_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;
@@ -276,55 +321,39 @@ with gr.Blocks(css="""
     }
 """) as demo:
     
-    # Contact Section
-    gr.Markdown("""
-        <div style="text-align: center;">
-            <a target="_blank" href="https://www.wi-lab.net">
-                <img src="https://www.wi-lab.net/wp-content/uploads/2021/08/WI-name.png" alt="Wireless Model" style="height: 30px;">
-            </a>
-            <a target="_blank" href="mailto:alikhani@asu.edu" style="margin-left: 10px;">
-                <img src="https://img.shields.io/badge/email-alikhani@asu.edu-blue.svg?logo=gmail" alt="Email">
-            </a>
-        </div>
-    """)
-    
-    # Tabs for Beam Prediction and LoS/NLoS Classification
+    # Tab for Beam Prediction Task
     with gr.Tab("Beam Prediction Task"):
         gr.Markdown("### Beam Prediction Task")
         
         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():
+                data_percentage_slider = gr.Slider(label="Data Percentage for Training", minimum=10, maximum=100, step=10, value=10)
+                task_complexity_slider = gr.Slider(label="Task Complexity (Number of Beams)", minimum=16, maximum=256, value=16, choices=[16, 32, 64, 128, 256])
 
         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)
+            raw_img_bp = gr.Image(label="Raw Channels", type="pil", width=300, height=300)
+            embeddings_img_bp = gr.Image(label="Embeddings", type="pil", width=300, height=300)
 
-        percentage_slider_bp.change(fn=display_predefined_images, inputs=[percentage_slider_bp], outputs=[raw_img_bp, embeddings_img_bp])
+        # Update the confusion matrices whenever sliders change
+        data_percentage_slider.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_slider], outputs=[raw_img_bp, embeddings_img_bp])
+        task_complexity_slider.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_slider], outputs=[raw_img_bp, embeddings_img_bp])
 
+    # Separate Tab for LoS/NLoS Classification Task
     with gr.Tab("LoS/NLoS Classification Task"):
         gr.Markdown("### LoS/NLoS Classification Task")
-        
         file_input = gr.File(label="Upload HDF5 Dataset", file_types=[".h5"])
 
         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")
-                percentage_dropdown_los = gr.Dropdown(choices=[0, 1, 2, 3, 4, 5, 6, 7, 8],
-                                                      value=0, 
-                                                      label="Percentage of Data for Training", 
-                                                      interactive=True)
-
+            percentage_dropdown_los = gr.Dropdown(choices=[0, 1, 2, 3, 4, 5, 6, 7, 8], value=0, label="Percentage of Data for Training")
         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)
+            raw_img_los = gr.Image(label="Raw Channels", type="pil", width=300, height=300)
+            embeddings_img_los = gr.Image(label="Embeddings", type="pil", width=300, height=300)
             output_textbox = gr.Textbox(label="Console Output", lines=10)
 
+        # Placeholder for LoS/NLoS classification function (already implemented in your previous code)
         file_input.change(fn=los_nlos_classification, inputs=[file_input, percentage_dropdown_los], outputs=[raw_img_los, embeddings_img_los, output_textbox])
-        percentage_dropdown_los.change(fn=los_nlos_classification, inputs=[file_input, percentage_dropdown_los], outputs=[raw_img_los, embeddings_img_los, output_textbox])
 
 # Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()
+