Update app.py

app.py

@@ -48,432 +48,432 @@ def health_check():
 def healthz():
     return {"ok": True}
 
-@app.get("/docs", include_in_schema=False)
-def custom_docs():
-    return JSONResponse(get_openapi(title="LoRA Autorun API", version="1.0.0", routes=app.routes))
-
-
-
-REPO_ID = "rahul7star/ohamlab"
-FOLDER = "demo"
-BASE_URL = f"https://huggingface.co/{REPO_ID}/resolve/main/"
-
-#show all images in a DIR at UI FE
-@app.get("/images")
-def list_images():
-    try:
-        all_files = list_repo_files(REPO_ID)
-
-        folder_prefix = FOLDER.rstrip("/") + "/"
-
-        files_in_folder = [
-            f for f in all_files
-            if f.startswith(folder_prefix)
-            and "/" not in f[len(folder_prefix):]  # no subfolder files
-            and f.lower().endswith((".png", ".jpg", ".jpeg", ".webp"))
-        ]
-
-        urls = [BASE_URL + f for f in files_in_folder]
-
-        return {"images": urls}
-
-    except Exception as e:
-        return {"error": str(e)}
-
-from datetime import datetime
-import tempfile
-import uuid
-
-# upload zip from UI 
-@app.post("/upload-zip")
-async def upload_zip(file: UploadFile = File(...)):
-    if not file.filename.endswith(".zip"):
-        return {"error": "Please upload a .zip file"}
-
-    # Save the ZIP to /tmp
-    temp_zip_path = f"/tmp/{file.filename}"
-    with open(temp_zip_path, "wb") as f:
-        f.write(await file.read())
-
-    # Create a unique subfolder name inside 'demo/'
-    timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
-    unique_id = uuid.uuid4().hex[:6]
-    folder_name = f"upload_{timestamp}_{unique_id}"
-    hf_folder_prefix = f"demo/{folder_name}"
-
-    try:
-        with tempfile.TemporaryDirectory() as extract_dir:
-            # Extract zip
-            with zipfile.ZipFile(temp_zip_path, 'r') as zip_ref:
-                zip_ref.extractall(extract_dir)
-
-            uploaded_files = []
-
-            # Upload all extracted files
-            for root_dir, _, files in os.walk(extract_dir):
-                for name in files:
-                    file_path = os.path.join(root_dir, name)
-                    relative_path = os.path.relpath(file_path, extract_dir)
-                    repo_path = f"{hf_folder_prefix}/{relative_path}".replace("\\", "/")
-
-                    upload_file(
-                        path_or_fileobj=file_path,
-                        path_in_repo=repo_path,
-                        repo_id="rahul7star/ohamlab",
-                        repo_type="model",
-                        commit_message=f"Upload {relative_path} to {folder_name}",
-                        token=True,
-                    )
-                    uploaded_files.append(repo_path)
-
-        return {
-            "message": f"✅ Uploaded {len(uploaded_files)} files",
-            "folder": folder_name,
-            "files": uploaded_files,
-        }
-
-    except Exception as e:
-        return {"error": f"❌ Failed to process zip: {str(e)}"}
+# @app.get("/docs", include_in_schema=False)
+# def custom_docs():
+#     return JSONResponse(get_openapi(title="LoRA Autorun API", version="1.0.0", routes=app.routes))
+
+
+
+# REPO_ID = "rahul7star/ohamlab"
+# FOLDER = "demo"
+# BASE_URL = f"https://huggingface.co/{REPO_ID}/resolve/main/"
+
+# #show all images in a DIR at UI FE
+# @app.get("/images")
+# def list_images():
+#     try:
+#         all_files = list_repo_files(REPO_ID)
+
+#         folder_prefix = FOLDER.rstrip("/") + "/"
+
+#         files_in_folder = [
+#             f for f in all_files
+#             if f.startswith(folder_prefix)
+#             and "/" not in f[len(folder_prefix):]  # no subfolder files
+#             and f.lower().endswith((".png", ".jpg", ".jpeg", ".webp"))
+#         ]
+
+#         urls = [BASE_URL + f for f in files_in_folder]
+
+#         return {"images": urls}
+
+#     except Exception as e:
+#         return {"error": str(e)}
+
+# from datetime import datetime
+# import tempfile
+# import uuid
+
+# # upload zip from UI 
+# @app.post("/upload-zip")
+# async def upload_zip(file: UploadFile = File(...)):
+#     if not file.filename.endswith(".zip"):
+#         return {"error": "Please upload a .zip file"}
+
+#     # Save the ZIP to /tmp
+#     temp_zip_path = f"/tmp/{file.filename}"
+#     with open(temp_zip_path, "wb") as f:
+#         f.write(await file.read())
+
+#     # Create a unique subfolder name inside 'demo/'
+#     timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+#     unique_id = uuid.uuid4().hex[:6]
+#     folder_name = f"upload_{timestamp}_{unique_id}"
+#     hf_folder_prefix = f"demo/{folder_name}"
+
+#     try:
+#         with tempfile.TemporaryDirectory() as extract_dir:
+#             # Extract zip
+#             with zipfile.ZipFile(temp_zip_path, 'r') as zip_ref:
+#                 zip_ref.extractall(extract_dir)
+
+#             uploaded_files = []
+
+#             # Upload all extracted files
+#             for root_dir, _, files in os.walk(extract_dir):
+#                 for name in files:
+#                     file_path = os.path.join(root_dir, name)
+#                     relative_path = os.path.relpath(file_path, extract_dir)
+#                     repo_path = f"{hf_folder_prefix}/{relative_path}".replace("\\", "/")
+
+#                     upload_file(
+#                         path_or_fileobj=file_path,
+#                         path_in_repo=repo_path,
+#                         repo_id="rahul7star/ohamlab",
+#                         repo_type="model",
+#                         commit_message=f"Upload {relative_path} to {folder_name}",
+#                         token=True,
+#                     )
+#                     uploaded_files.append(repo_path)
+
+#         return {
+#             "message": f"✅ Uploaded {len(uploaded_files)} files",
+#             "folder": folder_name,
+#             "files": uploaded_files,
+#         }
+
+#     except Exception as e:
+#         return {"error": f"❌ Failed to process zip: {str(e)}"}
 
     
-# upload a single file from UI
-from typing import List
-from fastapi import UploadFile, File, APIRouter
-import os
-from fastapi import UploadFile, File, APIRouter
-from typing import List
-from datetime import datetime
-import uuid, os
-
-
-@app.post("/upload")
-async def upload_images(
-    background_tasks: BackgroundTasks,
-    files: List[UploadFile] = File(...)
-):
-    # Step 1: Generate dynamic folder name
-    timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
-    unique_id = uuid.uuid4().hex[:6]
-    folder_name = f"upload_{timestamp}_{unique_id}"
-    hf_folder_prefix = f"demo/{folder_name}"
-
-    responses = []
-
-    # Step 2: Save and upload each image
-    for file in files:
-        filename = file.filename
-        contents = await file.read()
-        temp_path = f"/tmp/{filename}"
-        with open(temp_path, "wb") as f:
-            f.write(contents)
-
-        try:
-            upload_file(
-                path_or_fileobj=temp_path,
-                path_in_repo=f"{hf_folder_prefix}/{filename}",
-                repo_id=T_REPO_ID,
-                repo_type="model",
-                commit_message=f"Upload {filename} to {hf_folder_prefix}",
-                token=True,
-            )
-            responses.append({
-                "filename": filename,
-                "status": "✅ uploaded",
-                "path": f"{hf_folder_prefix}/{filename}"
-            })
-        except Exception as e:
-            responses.append({
-                "filename": filename,
-                "status": f"❌ failed: {str(e)}"
-            })
-
-        os.remove(temp_path)
-
-    # Step 3: Add filter job to background
-    def run_filter():
-        try:
-            result = filter_and_rename_images(folder=hf_folder_prefix)
-            print(f"🧼 Filter result: {result}")
-        except Exception as e:
-            print(f"❌ Filter failed: {str(e)}")
-
-    background_tasks.add_task(run_filter)
-
-    return {
-        "message": f"{len(files)} file(s) uploaded",
-        "upload_folder": hf_folder_prefix,
-        "results": responses,
-        "note": "Filtering started in background"
-    }
-
-
-
-
-
-
-#Tranining Data set start fitering data for traninig
-
-
-T_REPO_ID = "rahul7star/ohamlab"
-DESCRIPTION_TEXT = (
-    "Ra3hul is wearing a black jacket over a striped white t-shirt with blue jeans. "
-    "He is standing near a lake with his arms spread wide open, with mountains and cloudy skies in the background."
-)
-
-def is_image_file(filename: str) -> bool:
-    return filename.lower().endswith((".png", ".jpg", ".jpeg", ".webp"))
-
-@app.post("/filter-images")
-def filter_and_rename_images(folder: str = Query("demo", description="Folder path in repo to scan")):
-    try:
-        all_files = list_repo_files(T_REPO_ID)
-        folder_prefix = folder.rstrip("/") + "/"
-        filter_folder = f"filter-{folder.rstrip('/')}"
-        filter_prefix = filter_folder + "/"
-
-        # Filter images only directly in the folder (no subfolders)
-        image_files = [
-            f for f in all_files
-            if f.startswith(folder_prefix)
-            and "/" not in f[len(folder_prefix):]  # no deeper path
-            and is_image_file(f)
-        ]
-
-        if not image_files:
-            return {"error": f"No images found in folder '{folder}'"}
-
-        uploaded_files = []
-
-        for idx, orig_path in enumerate(image_files, start=1):
-            # Download image content bytes (uses local cache)
-            local_path = hf_hub_download(repo_id=T_REPO_ID, filename=orig_path)
-            with open(local_path, "rb") as f:
-                file_bytes = f.read()
-
-            # Rename images as image1.jpeg, image2.jpeg, ...
-            new_image_name = f"image{idx}.jpeg"
-
-            # Upload renamed image from memory
-            upload_file(
-                path_or_fileobj=io.BytesIO(file_bytes),
-                path_in_repo=filter_prefix + new_image_name,
-                repo_id=T_REPO_ID,
-                repo_type="model",
-                commit_message=f"Upload renamed image {new_image_name} to {filter_folder}",
-                token=True,
-            )
-            uploaded_files.append(filter_prefix + new_image_name)
-
-            # Create and upload text file for each image
-            txt_filename = f"image{idx}.txt"
-            upload_file(
-                path_or_fileobj=io.BytesIO(DESCRIPTION_TEXT.encode("utf-8")),
-                path_in_repo=filter_prefix + txt_filename,
-                repo_id=T_REPO_ID,
-                repo_type="model",
-                commit_message=f"Upload text file {txt_filename} to {filter_folder}",
-                token=True,
-            )
-            uploaded_files.append(filter_prefix + txt_filename)
-
-        return {
-            "message": f"Processed and uploaded {len(image_files)} images and text files.",
-            "files": uploaded_files,
-        }
-
-    except Exception as e:
-        return {"error": str(e)}
+# # upload a single file from UI
+# from typing import List
+# from fastapi import UploadFile, File, APIRouter
+# import os
+# from fastapi import UploadFile, File, APIRouter
+# from typing import List
+# from datetime import datetime
+# import uuid, os
+
+
+# @app.post("/upload")
+# async def upload_images(
+#     background_tasks: BackgroundTasks,
+#     files: List[UploadFile] = File(...)
+# ):
+#     # Step 1: Generate dynamic folder name
+#     timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+#     unique_id = uuid.uuid4().hex[:6]
+#     folder_name = f"upload_{timestamp}_{unique_id}"
+#     hf_folder_prefix = f"demo/{folder_name}"
+
+#     responses = []
+
+#     # Step 2: Save and upload each image
+#     for file in files:
+#         filename = file.filename
+#         contents = await file.read()
+#         temp_path = f"/tmp/{filename}"
+#         with open(temp_path, "wb") as f:
+#             f.write(contents)
+
+#         try:
+#             upload_file(
+#                 path_or_fileobj=temp_path,
+#                 path_in_repo=f"{hf_folder_prefix}/{filename}",
+#                 repo_id=T_REPO_ID,
+#                 repo_type="model",
+#                 commit_message=f"Upload {filename} to {hf_folder_prefix}",
+#                 token=True,
+#             )
+#             responses.append({
+#                 "filename": filename,
+#                 "status": "✅ uploaded",
+#                 "path": f"{hf_folder_prefix}/{filename}"
+#             })
+#         except Exception as e:
+#             responses.append({
+#                 "filename": filename,
+#                 "status": f"❌ failed: {str(e)}"
+#             })
+
+#         os.remove(temp_path)
+
+#     # Step 3: Add filter job to background
+#     def run_filter():
+#         try:
+#             result = filter_and_rename_images(folder=hf_folder_prefix)
+#             print(f"🧼 Filter result: {result}")
+#         except Exception as e:
+#             print(f"❌ Filter failed: {str(e)}")
+
+#     background_tasks.add_task(run_filter)
+
+#     return {
+#         "message": f"{len(files)} file(s) uploaded",
+#         "upload_folder": hf_folder_prefix,
+#         "results": responses,
+#         "note": "Filtering started in background"
+#     }
+
+
+
+
+
+
+# #Tranining Data set start fitering data for traninig
+
+
+# T_REPO_ID = "rahul7star/ohamlab"
+# DESCRIPTION_TEXT = (
+#     "Ra3hul is wearing a black jacket over a striped white t-shirt with blue jeans. "
+#     "He is standing near a lake with his arms spread wide open, with mountains and cloudy skies in the background."
+# )
+
+# def is_image_file(filename: str) -> bool:
+#     return filename.lower().endswith((".png", ".jpg", ".jpeg", ".webp"))
+
+# @app.post("/filter-images")
+# def filter_and_rename_images(folder: str = Query("demo", description="Folder path in repo to scan")):
+#     try:
+#         all_files = list_repo_files(T_REPO_ID)
+#         folder_prefix = folder.rstrip("/") + "/"
+#         filter_folder = f"filter-{folder.rstrip('/')}"
+#         filter_prefix = filter_folder + "/"
+
+#         # Filter images only directly in the folder (no subfolders)
+#         image_files = [
+#             f for f in all_files
+#             if f.startswith(folder_prefix)
+#             and "/" not in f[len(folder_prefix):]  # no deeper path
+#             and is_image_file(f)
+#         ]
+
+#         if not image_files:
+#             return {"error": f"No images found in folder '{folder}'"}
+
+#         uploaded_files = []
+
+#         for idx, orig_path in enumerate(image_files, start=1):
+#             # Download image content bytes (uses local cache)
+#             local_path = hf_hub_download(repo_id=T_REPO_ID, filename=orig_path)
+#             with open(local_path, "rb") as f:
+#                 file_bytes = f.read()
+
+#             # Rename images as image1.jpeg, image2.jpeg, ...
+#             new_image_name = f"image{idx}.jpeg"
+
+#             # Upload renamed image from memory
+#             upload_file(
+#                 path_or_fileobj=io.BytesIO(file_bytes),
+#                 path_in_repo=filter_prefix + new_image_name,
+#                 repo_id=T_REPO_ID,
+#                 repo_type="model",
+#                 commit_message=f"Upload renamed image {new_image_name} to {filter_folder}",
+#                 token=True,
+#             )
+#             uploaded_files.append(filter_prefix + new_image_name)
+
+#             # Create and upload text file for each image
+#             txt_filename = f"image{idx}.txt"
+#             upload_file(
+#                 path_or_fileobj=io.BytesIO(DESCRIPTION_TEXT.encode("utf-8")),
+#                 path_in_repo=filter_prefix + txt_filename,
+#                 repo_id=T_REPO_ID,
+#                 repo_type="model",
+#                 commit_message=f"Upload text file {txt_filename} to {filter_folder}",
+#                 token=True,
+#             )
+#             uploaded_files.append(filter_prefix + txt_filename)
+
+#         return {
+#             "message": f"Processed and uploaded {len(image_files)} images and text files.",
+#             "files": uploaded_files,
+#         }
+
+#     except Exception as e:
+#         return {"error": str(e)}
 
         
 
-# ========== CONFIGURATION ==========
-REPO_ID = "rahul7star/ohamlab"
-FOLDER_IN_REPO = "filter-demo/upload_20250708_041329_9c5c81"
-CONCEPT_SENTENCE = "ohamlab style"
-LORA_NAME = "ohami_filter_autorun"
-
-# ========== FASTAPI APP ==========
-
-# ========== HELPERS ==========
-def create_dataset(images, *captions):
-    destination_folder = f"datasets_{uuid.uuid4()}"
-    os.makedirs(destination_folder, exist_ok=True)
-
-    jsonl_file_path = os.path.join(destination_folder, "metadata.jsonl")
-    with open(jsonl_file_path, "a") as jsonl_file:
-        for index, image in enumerate(images):
-            new_image_path = shutil.copy(str(image), destination_folder)
-            caption = captions[index]
-            file_name = os.path.basename(new_image_path)
-            data = {"file_name": file_name, "prompt": caption}
-            jsonl_file.write(json.dumps(data) + "\n")
-
-    return destination_folder
-
-def recursive_update(d, u):
-    for k, v in u.items():
-        if isinstance(v, dict) and v:
-            d[k] = recursive_update(d.get(k, {}), v)
-        else:
-            d[k] = v
-    return d
-
-def start_training(
-    lora_name,
-    concept_sentence,
-    steps,
-    lr,
-    rank,
-    model_to_train,
-    low_vram,
-    dataset_folder,
-    sample_1,
-    sample_2,
-    sample_3,
-    use_more_advanced_options,
-    more_advanced_options,
-):
-    try:
-        user = whoami()
-        username = user.get("name", "anonymous")
-        push_to_hub = True
-    except:
-        username = "anonymous"
-        push_to_hub = False
-
-    slugged_lora_name = lora_name.replace(" ", "_").lower()
-
-    # Load base config
-    config = {
-        "config": {
-            "name": slugged_lora_name,
-            "process": [
-                {
-                    "model": {
-                        "low_vram": low_vram,
-                        "is_flux": True,
-                        "quantize": True,
-                        "name_or_path": "black-forest-labs/FLUX.1-dev"
-                    },
-                    "network": {
-                        "linear": rank,
-                        "linear_alpha": rank,
-                        "type": "lora"
-                    },
-                    "train": {
-                        "steps": steps,
-                        "lr": lr,
-                        "skip_first_sample": True,
-                        "batch_size": 1,
-                        "dtype": "bf16",
-                        "gradient_accumulation_steps": 1,
-                        "gradient_checkpointing": True,
-                        "noise_scheduler": "flowmatch",
-                        "optimizer": "adamw8bit",
-                        "ema_config": {
-                            "use_ema": True,
-                            "ema_decay": 0.99
-                        }
-                    },
-                    "datasets": [
-                        {"folder_path": dataset_folder}
-                    ],
-                    "save": {
-                        "dtype": "float16",
-                        "save_every": 10000,
-                        "push_to_hub": push_to_hub,
-                        "hf_repo_id": f"{username}/{slugged_lora_name}",
-                        "hf_private": True,
-                        "max_step_saves_to_keep": 4
-                    },
-                    "sample": {
-                        "guidance_scale": 3.5,
-                        "sample_every": steps,
-                        "sample_steps": 28,
-                        "width": 1024,
-                        "height": 1024,
-                        "walk_seed": True,
-                        "seed": 42,
-                        "sampler": "flowmatch",
-                        "prompts": [p for p in [sample_1, sample_2, sample_3] if p]
-                    },
-                    "trigger_word": concept_sentence
-                }
-            ]
-        }
-    }
-
-    # Apply advanced YAML overrides if any
-    if use_more_advanced_options and more_advanced_options:
-        advanced_config = yaml.safe_load(more_advanced_options)
-        config["config"]["process"][0] = recursive_update(config["config"]["process"][0], advanced_config)
-
-    # Save YAML config
-    os.makedirs("tmp_configs", exist_ok=True)
-    config_path = f"tmp_configs/{uuid.uuid4()}_{slugged_lora_name}.yaml"
-    with open(config_path, "w") as f:
-        yaml.dump(config, f)
-
-    # Simulate training
-    print(f"[INFO] Starting training with config: {config_path}")
-    print(json.dumps(config, indent=2))
-    return f"Training started successfully with config: {config_path}"
-
-# ========== MAIN ENDPOINT ==========
-@app.post("/train-from-hf")
-def auto_run_lora_from_repo():
-    try:
-        local_dir = Path(f"/tmp/{LORA_NAME}-{uuid.uuid4()}")
-        os.makedirs(local_dir, exist_ok=True)
-
-        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:
-            return JSONResponse(status_code=400, content={"error": "No images found in the HF repo folder."})
-
-        captions = [
-            f"Autogenerated caption for {img.stem} in the {CONCEPT_SENTENCE} [trigger]" for img in image_paths
-        ]
-
-        dataset_path = create_dataset(image_paths, *captions)
-
-        result = start_training(
-            lora_name=LORA_NAME,
-            concept_sentence=CONCEPT_SENTENCE,
-            steps=1000,
-            lr=4e-4,
-            rank=16,
-            model_to_train="dev",
-            low_vram=True,
-            dataset_folder=dataset_path,
-            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}",
-            use_more_advanced_options=True,
-            more_advanced_options="""
-training:
-  seed: 42
-  precision: bf16
-  batch_size: 2
-augmentation:
-  flip: true
-  color_jitter: true
-"""
-        )
-
-        return {"message": result}
-
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
+# # ========== CONFIGURATION ==========
+# REPO_ID = "rahul7star/ohamlab"
+# FOLDER_IN_REPO = "filter-demo/upload_20250708_041329_9c5c81"
+# CONCEPT_SENTENCE = "ohamlab style"
+# LORA_NAME = "ohami_filter_autorun"
+
+# # ========== FASTAPI APP ==========
+
+# # ========== HELPERS ==========
+# def create_dataset(images, *captions):
+#     destination_folder = f"datasets_{uuid.uuid4()}"
+#     os.makedirs(destination_folder, exist_ok=True)
+
+#     jsonl_file_path = os.path.join(destination_folder, "metadata.jsonl")
+#     with open(jsonl_file_path, "a") as jsonl_file:
+#         for index, image in enumerate(images):
+#             new_image_path = shutil.copy(str(image), destination_folder)
+#             caption = captions[index]
+#             file_name = os.path.basename(new_image_path)
+#             data = {"file_name": file_name, "prompt": caption}
+#             jsonl_file.write(json.dumps(data) + "\n")
+
+#     return destination_folder
+
+# def recursive_update(d, u):
+#     for k, v in u.items():
+#         if isinstance(v, dict) and v:
+#             d[k] = recursive_update(d.get(k, {}), v)
+#         else:
+#             d[k] = v
+#     return d
+
+# def start_training(
+#     lora_name,
+#     concept_sentence,
+#     steps,
+#     lr,
+#     rank,
+#     model_to_train,
+#     low_vram,
+#     dataset_folder,
+#     sample_1,
+#     sample_2,
+#     sample_3,
+#     use_more_advanced_options,
+#     more_advanced_options,
+# ):
+#     try:
+#         user = whoami()
+#         username = user.get("name", "anonymous")
+#         push_to_hub = True
+#     except:
+#         username = "anonymous"
+#         push_to_hub = False
+
+#     slugged_lora_name = lora_name.replace(" ", "_").lower()
+
+#     # Load base config
+#     config = {
+#         "config": {
+#             "name": slugged_lora_name,
+#             "process": [
+#                 {
+#                     "model": {
+#                         "low_vram": low_vram,
+#                         "is_flux": True,
+#                         "quantize": True,
+#                         "name_or_path": "black-forest-labs/FLUX.1-dev"
+#                     },
+#                     "network": {
+#                         "linear": rank,
+#                         "linear_alpha": rank,
+#                         "type": "lora"
+#                     },
+#                     "train": {
+#                         "steps": steps,
+#                         "lr": lr,
+#                         "skip_first_sample": True,
+#                         "batch_size": 1,
+#                         "dtype": "bf16",
+#                         "gradient_accumulation_steps": 1,
+#                         "gradient_checkpointing": True,
+#                         "noise_scheduler": "flowmatch",
+#                         "optimizer": "adamw8bit",
+#                         "ema_config": {
+#                             "use_ema": True,
+#                             "ema_decay": 0.99
+#                         }
+#                     },
+#                     "datasets": [
+#                         {"folder_path": dataset_folder}
+#                     ],
+#                     "save": {
+#                         "dtype": "float16",
+#                         "save_every": 10000,
+#                         "push_to_hub": push_to_hub,
+#                         "hf_repo_id": f"{username}/{slugged_lora_name}",
+#                         "hf_private": True,
+#                         "max_step_saves_to_keep": 4
+#                     },
+#                     "sample": {
+#                         "guidance_scale": 3.5,
+#                         "sample_every": steps,
+#                         "sample_steps": 28,
+#                         "width": 1024,
+#                         "height": 1024,
+#                         "walk_seed": True,
+#                         "seed": 42,
+#                         "sampler": "flowmatch",
+#                         "prompts": [p for p in [sample_1, sample_2, sample_3] if p]
+#                     },
+#                     "trigger_word": concept_sentence
+#                 }
+#             ]
+#         }
+#     }
+
+#     # Apply advanced YAML overrides if any
+#     if use_more_advanced_options and more_advanced_options:
+#         advanced_config = yaml.safe_load(more_advanced_options)
+#         config["config"]["process"][0] = recursive_update(config["config"]["process"][0], advanced_config)
+
+#     # Save YAML config
+#     os.makedirs("tmp_configs", exist_ok=True)
+#     config_path = f"tmp_configs/{uuid.uuid4()}_{slugged_lora_name}.yaml"
+#     with open(config_path, "w") as f:
+#         yaml.dump(config, f)
+
+#     # Simulate training
+#     print(f"[INFO] Starting training with config: {config_path}")
+#     print(json.dumps(config, indent=2))
+#     return f"Training started successfully with config: {config_path}"
+
+# # ========== MAIN ENDPOINT ==========
+# @app.post("/train-from-hf")
+# def auto_run_lora_from_repo():
+#     try:
+#         local_dir = Path(f"/tmp/{LORA_NAME}-{uuid.uuid4()}")
+#         os.makedirs(local_dir, exist_ok=True)
+
+#         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:
+#             return JSONResponse(status_code=400, content={"error": "No images found in the HF repo folder."})
+
+#         captions = [
+#             f"Autogenerated caption for {img.stem} in the {CONCEPT_SENTENCE} [trigger]" for img in image_paths
+#         ]
+
+#         dataset_path = create_dataset(image_paths, *captions)
+
+#         result = start_training(
+#             lora_name=LORA_NAME,
+#             concept_sentence=CONCEPT_SENTENCE,
+#             steps=1000,
+#             lr=4e-4,
+#             rank=16,
+#             model_to_train="dev",
+#             low_vram=True,
+#             dataset_folder=dataset_path,
+#             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}",
+#             use_more_advanced_options=True,
+#             more_advanced_options="""
+# training:
+#   seed: 42
+#   precision: bf16
+#   batch_size: 2
+# augmentation:
+#   flip: true
+#   color_jitter: true
+# """
+#         )
+
+#         return {"message": result}
+
+#     except Exception as e:
+#         return JSONResponse(status_code=500, content={"error": str(e)})