Update app.py

app.py

@@ -47,15 +47,6 @@ def create_random_image(size=(300, 300)):
     random_image = np.random.rand(*size, 3) * 255
     return Image.fromarray(random_image.astype('uint8'))
 
-# Function to load the pre-trained model from your cloned repository
-def load_custom_model():
-    from lwm_model import LWM  # Assuming the model is defined in lwm_model.py
-    model = LWM()  # Modify this according to your model initialization
-    model.eval()
-    return model
-
-import importlib.util
-
 # Function to dynamically load a Python module from a given file path
 def load_module_from_path(module_name, file_path):
     spec = importlib.util.spec_from_file_location(module_name, file_path)
@@ -68,42 +59,19 @@ def split_dataset(channels, labels, percentage_idx):
     percentage = percentage_values[percentage_idx] / 100
     num_samples = channels.shape[0]
     train_size = int(num_samples * percentage)
-    print(f'Number of Training Samples: {train_size}')
-    
     indices = np.arange(num_samples)
     np.random.shuffle(indices)
     
     train_idx, test_idx = indices[:train_size], indices[train_size:]
-    
     train_data, test_data = channels[train_idx], channels[test_idx]
     train_labels, test_labels = labels[train_idx], labels[test_idx]
     
     return train_data, test_data, train_labels, test_labels
 
-# Function to calculate Euclidean distance between a point and a centroid
-def euclidean_distance(x, centroid):
-    return np.linalg.norm(x - centroid)
-
-import torch
-
-def classify_based_on_distance(train_data, train_labels, test_data):
-    # Compute the centroids for the two classes
-    centroid_0 = train_data[train_labels == 0].mean(dim=0)  # Use torch.mean
-    centroid_1 = train_data[train_labels == 1].mean(dim=0)  # Use torch.mean
-    
-    predictions = []
-    for test_point in test_data:
-        # Compute Euclidean distance between the test point and each centroid
-        dist_0 = euclidean_distance(test_point, centroid_0)
-        dist_1 = euclidean_distance(test_point, centroid_1)
-        predictions.append(0 if dist_0 < dist_1 else 1)
-    
-    return torch.tensor(predictions)  # Return predictions as a PyTorch tensor
-
 # Function to generate confusion matrix plot
 def plot_confusion_matrix(y_true, y_pred, title):
     cm = confusion_matrix(y_true, y_pred)
-    plt.figure(figsize=(5, 5))
+    plt.figure(figsize=(4, 4))
     plt.imshow(cm, cmap='Blues')
     plt.title(title)
     plt.xlabel('Predicted')
@@ -116,101 +84,76 @@ def plot_confusion_matrix(y_true, y_pred, title):
     return Image.open(f"{title}.png")
 
 def identical_train_test_split(output_emb, output_raw, labels, percentage):
-    N = output_emb.shape[0]  # Get the total number of samples
-    
-    # Generate the indices for shuffling and splitting
-    indices = torch.randperm(N)  # Randomly shuffle the indices
-    
-    # Calculate the split index
+    N = output_emb.shape[0]  
+    indices = torch.randperm(N)  
     split_index = int(N * percentage)
-    
-    # Split indices into train and test
-    train_indices = indices[:split_index]  # First 80% for training
-    test_indices = indices[split_index:]   # Remaining 20% for testing
-    
-    # Select the same indices from both output_emb and output_raw
+    train_indices = indices[:split_index]
+    test_indices = indices[split_index:]
     train_emb = output_emb[train_indices]
     test_emb = output_emb[test_indices]
-    
     train_raw = output_raw[train_indices]
     test_raw = output_raw[test_indices]
-
     train_labels = labels[train_indices]
     test_labels = labels[test_indices]
 
     return train_emb, test_emb, train_raw, test_raw, train_labels, test_labels
 
+# Function to classify test data based on distance to class centroids
+def classify_based_on_distance(train_data, train_labels, test_data):
+    centroid_0 = train_data[train_labels == 0].mean(dim=0)  
+    centroid_1 = train_data[train_labels == 1].mean(dim=0)  
+    
+    predictions = []
+    for test_point in test_data:
+        dist_0 = torch.norm(test_point - centroid_0)
+        dist_1 = torch.norm(test_point - centroid_1)
+        predictions.append(0 if dist_0 < dist_1 else 1)
+    
+    return torch.tensor(predictions)  
+
 # Store the original working directory when the app starts
 original_dir = os.getcwd()
 
 def process_hdf5_file(uploaded_file, percentage_idx):
     capture = PrintCapture()
-    sys.stdout = capture  # Redirect print statements to capture
+    sys.stdout = capture  
     
     try:
         model_repo_url = "https://huggingface.co/sadjadalikhani/LWM"
         model_repo_dir = "./LWM"
-
-        # Step 1: Clone the repository if not already done
         if not os.path.exists(model_repo_dir):
-            print(f"Cloning model repository from {model_repo_url}...")
             subprocess.run(["git", "clone", model_repo_url, model_repo_dir], check=True)
-
-        # Step 2: Verify the repository was cloned and change the working directory
         repo_work_dir = os.path.join(original_dir, model_repo_dir)
         if os.path.exists(repo_work_dir):
-            os.chdir(repo_work_dir)  # Change the working directory only once
-            print(f"Changed working directory to {os.getcwd()}")
-            print(f"Directory content: {os.listdir(os.getcwd())}")  # Debugging: Check repo content
-        else:
-            print(f"Directory {repo_work_dir} does not exist.")
-            return
-            
-        # Step 3: Dynamically load lwm_model.py, input_preprocess.py, and inference.py
+            os.chdir(repo_work_dir)  
         lwm_model_path = os.path.join(os.getcwd(), 'lwm_model.py')
         input_preprocess_path = os.path.join(os.getcwd(), 'input_preprocess.py')
         inference_path = os.path.join(os.getcwd(), 'inference.py')
 
-        # Load lwm_model
         lwm_model = load_module_from_path("lwm_model", lwm_model_path)
-
-        # Load input_preprocess
         input_preprocess = load_module_from_path("input_preprocess", input_preprocess_path)
-
-        # Load inference
         inference = load_module_from_path("inference", inference_path)
 
-        # Step 4: Load the model from lwm_model module
         device = 'cpu'
-        print(f"Loading the LWM model on {device}...")
         model = lwm_model.LWM.from_pretrained(device=device)
 
-        # Step 5: Load the HDF5 file and extract the channels and labels
         with h5py.File(uploaded_file.name, 'r') as f:
-            channels = np.array(f['channels'])  # Assuming 'channels' dataset in the HDF5 file
-            labels = np.array(f['labels'])  # Assuming 'labels' dataset in the HDF5 file
-        print(f"Loaded dataset with {channels.shape[0]} samples.")
+            channels = np.array(f['channels'])
+            labels = np.array(f['labels'])
 
-        # Step 7: Tokenize the data using the tokenizer from input_preprocess
         preprocessed_chs = input_preprocess.tokenizer(manual_data=channels)
-
-        # Step 7: Perform inference using the functions from inference.py
         output_emb = inference.lwm_inference(preprocessed_chs, 'channel_emb', model)
         output_raw = inference.create_raw_dataset(preprocessed_chs, device)
 
-        print(f"Output Embeddings Shape: {output_emb.shape}")
-        print(f"Output Raw Shape: {output_raw.shape}")
-
-        train_data_emb, test_data_emb, train_data_raw, test_data_raw, train_labels, test_labels = identical_train_test_split(output_emb.view(len(output_emb),-1),
-                                                                                                                             output_raw.view(len(output_raw),-1),
-                                                                                                                             labels,
-                                                                                                                             percentage_idx)
+        train_data_emb, test_data_emb, train_data_raw, test_data_raw, train_labels, test_labels = identical_train_test_split(
+            output_emb.view(len(output_emb),-1),
+            output_raw.view(len(output_raw),-1),
+            labels,
+            percentage_idx)
         
-        # Step 8: Perform classification using the Euclidean distance for both raw and embeddings
         pred_raw = classify_based_on_distance(train_data_raw, train_labels, test_data_raw)
         pred_emb = classify_based_on_distance(train_data_emb, train_labels, test_data_emb)
 
-        # Step 9: Generate confusion matrices for both raw and embeddings
         raw_cm_image = plot_confusion_matrix(test_labels, pred_raw, title="Confusion Matrix (Raw Channels)")
         emb_cm_image = plot_confusion_matrix(test_labels, pred_emb, title="Confusion Matrix (Embeddings)")
 
@@ -220,73 +163,64 @@ def process_hdf5_file(uploaded_file, percentage_idx):
         return str(e), str(e), capture.get_output()
 
     finally:
-        # Always return to the original working directory after processing
         os.chdir(original_dir)
-        sys.stdout = sys.__stdout__  # Reset print statements
+        sys.stdout = sys.__stdout__  
 
-# Function to handle logic based on whether a file is uploaded or not
 def los_nlos_classification(file, percentage_idx):
     if file is not None:
         return process_hdf5_file(file, percentage_idx)
     else:
         return display_predefined_images(percentage_idx), None
 
-# Define the Gradio interface
+# Define the Gradio interface with a compact, minimal layout
 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;
+        margin-bottom: 20px;
+    }
+    .image-row {
+        justify-content: center;
+        margin-top: 10px;
+    }
+    .output-box {
+        max-width: 600px;
+        margin: 0 auto;
     }
 """) as demo:
     
     # Contact Section
-    gr.Markdown(
-        """
-        ## Contact
-        <div style="display: flex; align-items: 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>&nbsp;&nbsp;
-            <a target="_blank" href="mailto:alikhani@asu.edu"><img src="https://img.shields.io/badge/email-alikhani@asu.edu-blue.svg?logo=gmail " alt="Email"></a>&nbsp;&nbsp;
+    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
     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.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)
-
+                percentage_slider_bp = gr.Slider(minimum=0, maximum=4, step=1, value=0, label="Training Data (%)")
+        with gr.Row(elem_id="image-row"):
+            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])
 
     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")
-
-        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)
-            output_textbox = gr.Textbox(label="Console Output", lines=10)
-
+                percentage_slider_los = gr.Slider(minimum=0, maximum=4, step=1, value=0, label="Training Data (%)")
+        with gr.Row(elem_id="image-row"):
+            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=8, elem_classes="output-box")
         file_input.change(fn=los_nlos_classification, inputs=[file_input, percentage_slider_los], outputs=[raw_img_los, embeddings_img_los, output_textbox])
         percentage_slider_los.change(fn=los_nlos_classification, inputs=[file_input, percentage_slider_los], outputs=[raw_img_los, embeddings_img_los, output_textbox])