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import os
import uuid
import gradio as gr
from pathlib import Path
from huggingface_hub import hf_hub_download
from your_existing_training_file import create_dataset, start_training # <-- update this import as needed
# Constants
REPO_ID = "rahul7star/ohamlab"
FOLDER_IN_REPO = "filter-demo/upload_20250708_041329_9c5c81"
CONCEPT_SENTENCE = "ohamlab style"
LORA_NAME = "ohami_filter_autorun"
def auto_run_lora_from_repo():
local_dir = Path(f"/tmp/{LORA_NAME}-{uuid.uuid4()}")
os.makedirs(local_dir, exist_ok=True)
# Download at least one file to force HF to pull full folder
hf_hub_download(
repo_id=REPO_ID,
repo_type="dataset",
subfolder=FOLDER_IN_REPO,
local_dir=local_dir,
local_dir_use_symlinks=False,
force_download=False,
etag_timeout=10,
allow_patterns=["*.jpg", "*.png", "*.jpeg"],
)
image_dir = local_dir / FOLDER_IN_REPO
image_paths = list(image_dir.rglob("*.jpg")) + list(image_dir.rglob("*.jpeg")) + list(image_dir.rglob("*.png"))
if not image_paths:
raise gr.Error("No images found in the Hugging Face repo folder.")
# Captions
captions = [
f"Generated image caption for {img.stem} in the {CONCEPT_SENTENCE} [trigger]" for img in image_paths
]
# Create dataset
dataset_path = create_dataset(image_paths, *captions)
# Static prompts
sample_1 = f"A stylized portrait using {CONCEPT_SENTENCE}"
sample_2 = f"A cat in the {CONCEPT_SENTENCE}"
sample_3 = f"A selfie processed in {CONCEPT_SENTENCE}"
# Config
steps = 1000
lr = 4e-4
rank = 16
model_to_train = "dev"
low_vram = True
use_more_advanced_options = True
more_advanced_options = """\
training:
seed: 42
precision: bf16
batch_size: 2
augmentation:
flip: true
color_jitter: true
"""
# Train
return start_training(
lora_name=LORA_NAME,
concept_sentence=CONCEPT_SENTENCE,
steps=steps,
lr=lr,
rank=rank,
model_to_train=model_to_train,
low_vram=low_vram,
dataset_folder=dataset_path,
sample_1=sample_1,
sample_2=sample_2,
sample_3=sample_3,
use_more_advanced_options=use_more_advanced_options,
more_advanced_options=more_advanced_options
)
# Gradio UI
with gr.Blocks(title="LoRA Autorun from HF Repo") as demo:
gr.Markdown("# 🚀 Auto Run LoRA from Hugging Face Repo")
output = gr.Textbox(label="Training Status", lines=3)
run_button = gr.Button("Run Training from HF Repo")
run_button.click(fn=auto_run_lora_from_repo, outputs=output)
if __name__ == "__main__":
demo.launch(share=True)
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