import gradio as gr import os from PIL import Image import numpy as np import pickle import io import sys # Paths to the predefined images folder RAW_PATH = os.path.join("images", "raw") EMBEDDINGS_PATH = os.path.join("images", "embeddings") GENERATED_PATH = os.path.join("images", "generated") # Specific values for percentage and complexity percentage_values = [10, 30, 50, 70, 100] complexity_values = [16, 32] # Custom class to capture print output class PrintCapture(io.StringIO): def __init__(self): super().__init__() self.output = [] def write(self, txt): self.output.append(txt) super().write(txt) def get_output(self): return ''.join(self.output) # Function to load and display predefined images based on user selection def display_predefined_images(percentage_idx, complexity_idx): percentage = percentage_values[percentage_idx] complexity = complexity_values[complexity_idx] 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") raw_image = Image.open(raw_image_path) embeddings_image = Image.open(embeddings_image_path) return raw_image, embeddings_image import torch import subprocess # 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 sys import subprocess import os import pickle import torch import io # Function to process the uploaded .p file and perform inference using the custom model def process_p_file(uploaded_file, percentage_idx, complexity_idx): capture = PrintCapture() sys.stdout = capture # Redirect print statements to capture try: model_repo_url = "https://huggingface.co/sadjadalikhani/LWM" model_repo_dir = "./LWM" # Step 1: Clone the model repository if not already cloned 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) # Debugging: Check if the directory exists and print contents if os.path.exists(model_repo_dir): os.chdir(model_repo_dir) print(f"Changed working directory to {os.getcwd()}") print(f"Directory content: {os.listdir(os.getcwd())}") # Debugging: Check repo content else: print(f"Directory {model_repo_dir} does not exist.") return # Step 2: Add the cloned repo to sys.path for imports if model_repo_dir not in sys.path: sys.path.append(model_repo_dir) # Debugging: Print sys.path to ensure the cloned repo is in the path print(f"sys.path: {sys.path}") # Step 3: Dynamically import the model after cloning try: from lwm_model import LWM # Custom model in the cloned repo print("Successfully imported LWM model.") except ImportError as e: print(f"Error importing LWM model: {e}") print("Make sure lwm_model.py exists in the cloned repository.") return # Step 4: Check if GPU is available and set the device device = 'cuda' if torch.cuda.is_available() else 'cpu' print(f"Using device: {device}") # Load the model from the cloned repository model = LWM.from_pretrained(device=device) # Step 5: Import the tokenizer try: from input_preprocess import tokenizer except ImportError as e: print(f"Error importing tokenizer: {e}") return # Step 6: Load the uploaded .p file (wireless channel matrix) with open(uploaded_file.name, 'rb') as f: manual_data = pickle.load(f) # Step 7: Tokenize the data if needed (or perform any necessary preprocessing) preprocessed_chs = tokenizer(manual_data=manual_data) # Step 8: Perform inference using the model from inference import lwm_inference, create_raw_dataset output_emb = lwm_inference(preprocessed_chs, 'channel_emb', model) output_raw = create_raw_dataset(preprocessed_chs, device) print(f"Output Embeddings Shape: {output_emb.shape}") print(f"Output Raw Shape: {output_raw.shape}") # Return the embeddings, raw output, and captured output return output_emb, output_raw, capture.get_output() except Exception as e: # Handle exceptions and return the captured output return str(e), str(e), capture.get_output() finally: sys.stdout = sys.__stdout__ # Reset stdout # Function to handle logic based on whether a file is uploaded or not def los_nlos_classification(file, percentage_idx, complexity_idx): if file is not None: return process_p_file(file, percentage_idx, complexity_idx) else: return display_predefined_images(percentage_idx, complexity_idx), None # 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
Wireless Model   Email  
""" ) # 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.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") 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.change(fn=display_predefined_images, inputs=[percentage_slider_bp, complexity_slider_bp], outputs=[raw_img_bp, embeddings_img_bp]) complexity_slider_bp.change(fn=display_predefined_images, 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") file_input = gr.File(label="Upload .p File", file_types=[".p"]) 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") 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) file_input.change(fn=los_nlos_classification, inputs=[file_input, percentage_slider_los, complexity_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, complexity_slider_los], outputs=[raw_img_los, embeddings_img_los, output_textbox]) complexity_slider_los.change(fn=los_nlos_classification, inputs=[file_input, percentage_slider_los, complexity_slider_los], outputs=[raw_img_los, embeddings_img_los, output_textbox]) # Launch the app if __name__ == "__main__": demo.launch()