Update app.py

app.py

@@ -1,118 +1,52 @@
-import gradio as gr
-import os
-from PIL import Image
-
-# Paths to the images folder
-RAW_PATH = os.path.join("images", "raw")
-EMBEDDINGS_PATH = os.path.join("images", "embeddings")
-
-# Specific values for percentage and complexity
-percentage_values = [10, 30, 50, 70, 100]
-complexity_values = [16, 32]
-
-# Function to load and display images based on user selection
-def display_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
-
-# Define the beam prediction function (template based)
-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(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
-
-# 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")
+import torch
+from transformers import AutoModel  # Assuming you use a transformer-like model in your LWM repo
+import numpy as np
+import importlib.util
+
+# Function to load the pre-trained model from Hugging Face
+def load_pretrained_model():
+    # Load the pre-trained model from the Hugging Face repo
+    model = AutoModel.from_pretrained("sadjadalikhani/LWM")
+    model.eval()  # Set model to evaluation mode
+    return model
+
+# Function to process the uploaded .py file and perform inference using the model
+def process_python_file(uploaded_file, percentage_idx, complexity_idx):
+    try:
+        # Step 1: Load the model
+        model = load_pretrained_model()
+
+        # Step 2: Load the uploaded .py file that contains the wireless channel matrix
+        # Import the Python file dynamically
+        spec = importlib.util.spec_from_file_location("uploaded_module", uploaded_file.name)
+        uploaded_module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(uploaded_module)
+
+        # Assuming the uploaded file defines a variable called 'channel_matrix'
+        channel_matrix = uploaded_module.channel_matrix  # This should be defined in the uploaded file
+
+        # Step 3: Perform inference on the channel matrix using the model
+        with torch.no_grad():
+            input_tensor = torch.tensor(channel_matrix).unsqueeze(0)  # Add batch dimension
+            output = model(input_tensor)  # Perform inference
+
+        # Step 4: Generate new images based on the inference results
+        # You can modify this logic depending on how you want to visualize the results
+        generated_raw_img = np.random.rand(300, 300, 3) * 255  # Placeholder: Replace with actual inference result
+        generated_embeddings_img = np.random.rand(300, 300, 3) * 255  # Placeholder: Replace with actual inference result
         
-        # 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=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")
+        # Save the generated images
+        generated_raw_image_path = os.path.join(GENERATED_PATH, f"generated_raw_{percentage_idx}_{complexity_idx}.png")
+        generated_embeddings_image_path = os.path.join(GENERATED_PATH, f"generated_embeddings_{percentage_idx}_{complexity_idx}.png")
         
-        # 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.fromarray(generated_raw_img.astype(np.uint8)).save(generated_raw_image_path)
+        Image.fromarray(generated_embeddings_img.astype(np.uint8)).save(generated_embeddings_image_path)
 
-        # 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)
+        # Load the generated images
+        raw_image = Image.open(generated_raw_image_path)
+        embeddings_image = Image.open(generated_embeddings_image_path)
 
-        # 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])
+        return raw_image, embeddings_image
 
-# Launch the app
-if __name__ == "__main__":
-    demo.launch()
+    except Exception as e:
+        return str(e), str(e)