app.py

import gradio as gr
import subprocess
import os
import shutil
import os
from huggingface_hub import hf_hub_download


# Define paths
MODEL_DIR = "./model"  # Local directory to store the downloaded model
DATASET_DIR = os.path.join(MODEL_DIR, "Dataset004_WML")  # Directory for Dataset004_WML
INPUT_DIR = "/tmp/input"
OUTPUT_DIR = "/tmp/output"

# Hugging Face Model Repository
REPO_ID = "FrancescoLR/FLAMeS-model"  # Replace with your actual model repository ID

# Function to download the Dataset004_WML folder
def download_model():
    if not os.path.exists(DATASET_DIR):
        os.makedirs(DATASET_DIR, exist_ok=True)
        print("Downloading Dataset004_WML.zip...")
        zip_path = hf_hub_download(repo_id=REPO_ID, filename="Dataset004_WML.zip", cache_dir=MODEL_DIR)
        subprocess.run(["unzip", "-o", zip_path, "-d", MODEL_DIR])
        print("Dataset004_WML downloaded and extracted.")

# Function to run nnUNet inference
def run_nnunet_predict(nifti_file):
    # Prepare directories
    os.makedirs(INPUT_DIR, exist_ok=True)
    os.makedirs(OUTPUT_DIR, exist_ok=True)

    # Save the uploaded file to the input directory
    input_path = os.path.join(INPUT_DIR, "image.nii.gz")
    os.rename(nifti_file.name, input_path)  # Move the uploaded file to the expected input location

    # Set environment variables for nnUNet
    os.environ["nnUNet_raw"] = DATASET_DIR
    os.environ["nnUNet_preprocessed"] = DATASET_DIR
    os.environ["nnUNet_results"] = DATASET_DIR

    # Construct and run the nnUNetv2_predict command
    command = [
        "nnUNetv2_predict",
        "-i", INPUT_DIR,
        "-o", OUTPUT_DIR,
        "-d", "004",                  # Dataset ID
        "-c", "3d_fullres",           # Configuration
        "-tr", "nnUNetTrainer_8000epochs",  # Trainer name
    ]
    try:
        subprocess.run(command, check=True)
        # Get the output file
        output_file = os.path.join(OUTPUT_DIR, "image.nii.gz")
        return output_file
    except subprocess.CalledProcessError as e:
        return f"Error: {e}"


# Gradio Interface
interface = gr.Interface(
    fn=run_nnunet_predict,
    inputs=gr.File(label="Upload FLAIR Image (.nii.gz)"),
    outputs=gr.File(label="Download Segmentation Mask"),
    title="FLAMeS: Multiple Sclerosis Lesion Segmentation",
    description="Upload a skull-stripped FLAIR image (.nii.gz) to generate a binary segmentation of MS lesions."
)

# Download model files before launching the app
download_model()

# Launch the app
if __name__ == "__main__":
    interface.launch(share=True)