Create autorun_lora_gradio.py

autorun_lora_gradio.py

@@ -0,0 +1,91 @@
+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)