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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)
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