# img_gen.py
#img_gen_modal.py
# img_gen.py
# img_gen_modal.py
import modal
import random
import io
from config.config import prompts, models  # Indirect import
import os


CACHE_DIR = "/model_cache"

# Define the Modal image
image = (
    #modal.Image.from_registry("nvidia/cuda:12.2.0-devel-ubuntu22.04", add_python="3.9")
    modal.Image.debian_slim(python_version="3.9")  # Base image

    .apt_install(
        "git",
    )
    .pip_install(
        "diffusers",
        "transformers",
        "torch",
        "accelerate",
        "gradio>=4.44.1",
        "safetensors",
        "pillow",
        "sentencepiece",
        "hf_transfer",
        "huggingface_hub[hf_transfer]",
        "aria2",  # aria2 for ultra-fast parallel downloads
        f"git+https://github.com/huggingface/transformers.git"
    )
    .env(
        {
            "HF_HUB_ENABLE_HF_TRANSFER": "1", "HF_HOME": "HF_HOME", "HF_HUB_CACHE": CACHE_DIR
        }
    )
)

# Create a Modal app
app = modal.App("tools-test-dir", image=image)
with image.imports():
    import diffusers
    import os
    import gradio
    from datetime import datetime

flux_model_vol = modal.Volume.from_name("flux-model-vol", create_if_missing=True)  # Reference your volume

@app.function(volumes={"/data": flux_model_vol},
              secrets=[modal.Secret.from_name("huggingface-token")],
              #gpu="a100-80gb"
              )
def test_dir():

    # OS
    # Get the current working directory (should be /root)
    current_directory = os.getcwd()
    print(f"Current working directory: {current_directory}")

    # List the contents of the current directory
    print("Contents of current modal directory:")
    print(os.listdir(current_directory))

    # MODAL
    print ("MODAL")
    print ("MODAL ROOT")
    # List contents of the volume
    file_entries = flux_model_vol.listdir("/")  # Replace "/data" with your volume path
    # Extract and print only the paths
    paths = [entry.path for entry in file_entries]

    print("Paths in volume:")
    print(paths)