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
        
        # 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")
        
        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)

        # Load the generated images
        raw_image = Image.open(generated_raw_image_path)
        embeddings_image = Image.open(generated_embeddings_image_path)

        return raw_image, embeddings_image

    except Exception as e:
        return str(e), str(e)