Update app.py

app.py

@@ -42,6 +42,26 @@ sys.path.insert(0, "ai-toolkit")
 from toolkit.job import get_job
 
 
+from dataclasses import dataclass, field
+from typing import Dict
+import uuid
+from datetime import datetime
+
+@dataclass
+class Job:
+    job_id: str
+    folder_path: str
+    status: str = "pending"
+    created_at: str = field(default_factory=lambda: datetime.utcnow().isoformat())
+
+# Global job queue (in-memory)
+JOB_QUEUE: Dict[str, Job] = {}
+
+
+
+
+
+
 app = FastAPI()
 
 # CORS setup to allow requests from your frontend
@@ -154,11 +174,15 @@ async def upload_images(
     background_tasks: BackgroundTasks,
     files: List[UploadFile] = File(...)
 ):
-    # Step 1: Generate dynamic folder name
+    # Step 1: Generate dynamic folder name and job ID
     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}"
+    job_id = f"job_{unique_id}"
+
+    # Register job
+    JOB_QUEUE[job_id] = Job(job_id=job_id, folder_path=hf_folder_prefix)
 
     responses = []
 
@@ -192,25 +216,45 @@ async def upload_images(
 
         os.remove(temp_path)
 
-    # Step 3: Add filter job to background
-    def run_filter():
+    # Step 3 & 4: Add background task with job ID
+    def process_job(job_id: str):
+        job = JOB_QUEUE.get(job_id)
+        if not job:
+            print(f"❌ Job {job_id} not found")
+            return
+
         try:
-            result = filter_and_rename_images(folder=hf_folder_prefix)
-            print(f"🧼 Filter result: {result}")
+            print(f"🔧 Starting filter for {job.folder_path}")
+            filter_result = filter_and_rename_images(folder=job.folder_path)
+            print(f"🧼 Filter result: {filter_result}")
+
+            print(f"🚀 Triggering LoRA for {job.folder_path}")
+            auto_run_lora_from_repo(folder_path=job.folder_path)
+
+            job.status = "completed"
         except Exception as e:
-            print(f"❌ Filter failed: {str(e)}")
+            job.status = f"failed: {str(e)}"
+            print(f"❌ Job {job_id} failed: {str(e)}")
 
-    background_tasks.add_task(run_filter)
+    background_tasks.add_task(process_job, job_id)
 
     return {
         "message": f"{len(files)} file(s) uploaded",
+        "job_id": job_id,
         "upload_folder": hf_folder_prefix,
         "results": responses,
-        "note": "Filtering started in background"
+        "note": "Filtering + LoRA training queued"
     }
 
 
 
+def auto_run_lora_from_repo(folder_path: str):
+    try:
+        FOLDER_IN_REPO = folder_path
+        print(f"🚀 Auto-run triggered for: {FOLDER_IN_REPO}")
+        # Do your training etc...
+    except Exception as e:
+        print(f"❌ auto_run_lora_from_repo failed: {str(e)}")