app.py

import gradio as gr
import os
from PIL import Image
import numpy as np
import pickle
import io
import sys
import torch
import torch
import subprocess

# 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

# 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

# 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
        <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;
        </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.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()