Update app.py

app.py

@@ -2,18 +2,6 @@ import os
 os.environ["HF_HOME"] = "/tmp/hf_cache"
 os.makedirs("/tmp/hf_cache", exist_ok=True)
 
-import uuid
-import yaml
-import json
-import shutil
-import torch
-from pathlib import Path
-from PIL import Image
-from fastapi import FastAPI
-from fastapi.responses import JSONResponse
-from huggingface_hub import hf_hub_download, whoami
-
-
 from fastapi import FastAPI, Query
 from huggingface_hub import list_repo_files, hf_hub_download, upload_file
 import io
@@ -23,6 +11,8 @@ from fastapi import FastAPI, UploadFile, File
 from fastapi.middleware.cors import CORSMiddleware
 
 
+import os
+import os
 import zipfile
 import tempfile  # ✅ Add this!
 
@@ -30,7 +20,6 @@ import tempfile  # ✅ Add this!
 
 
 app = FastAPI()
-print("entry point for the app ")
 
 # CORS setup to allow requests from your frontend
 app.add_middleware(
@@ -45,438 +34,4 @@ app.add_middleware(
 def health_check():
     return {"status": "✅ FastAPI running on Hugging Face Spaces!"}
 
-@app.get("/healthz")
-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)}"}
-
-    
-# # 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)})