Update app.py

app.py

@@ -18,6 +18,29 @@ from peft import LoraConfig, get_peft_model, TaskType
 from huggingface_hub import HfApi, HfFolder, Repository
 import os, tempfile, shutil
 
+import asyncio
+import tempfile
+import shutil
+from huggingface_hub import HfApi, HfFolder, Repository
+
+async def async_upload_model(local_dir, hf_repo, output_log):
+    try:
+        token = HfFolder.get_token()
+        api = HfApi()
+        api.create_repo(repo_id=hf_repo, exist_ok=True)
+
+        output_log.append(f"\n☁️ Starting async upload to: {hf_repo}")
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            repo = Repository(local_dir=tmpdir, clone_from=hf_repo, use_auth_token=token)
+            # Copy model files
+            shutil.copytree(local_dir, tmpdir, dirs_exist_ok=True)
+            repo.push_to_hub(commit_message="Upload fine-tuned model")
+
+        output_log.append("\n✅ Async upload complete!")
+    except Exception as e:
+        output_log.append(f"\n❌ Async upload error: {e}")
+
 
 # === GPU check (Zero GPU compatible) ===
 def check_gpu_status():
@@ -139,34 +162,25 @@ You are a wise teacher interpreting Bhagavad Gita with deep insights.
             tokenizer=tokenizer,
         )
 
-        # ==== Train ====
-        progress(0.7, desc="Training...")
-        output_log.append("\n🚀 Starting training...\n" + "=" * 50)
-        train_result = trainer.train()
-
-        # ==== Save model ====
-        progress(0.85, desc="Saving model...")
-        output_log.append("\n💾 Saving model locally...")
-        trainer.save_model(output_dir)
-        tokenizer.save_pretrained(output_dir)
-
-        # ==== Upload to HF Hub ====
-        progress(0.9, desc="Uploading to Hugging Face Hub...")
-        hf_repo = "rahul7star/Qwen0.5-3B-Gita"
-        output_log.append(f"\n☁️ Uploading fine-tuned model to: {hf_repo}")
+       # ==== Train ====
+       progress(0.7, desc="Training...")
+       output_log.append("\n🚀 Starting training...\n" + "=" * 50)
+       train_result = trainer.train()
 
-        api = HfApi()
-        token = HfFolder.get_token()
+# ==== Save model locally ====
+       progress(0.85, desc="Saving model...")
+       output_log.append("\n💾 Saving model locally...")
+       trainer.save_model(output_dir)
+       tokenizer.save_pretrained(output_dir)
 
-        api.create_repo(repo_id=hf_repo, exist_ok=True)
+# ==== Async upload ====
+       hf_repo = "rahul7star/Qwen0.5-3B-Gita"
+       asyncio.create_task(async_upload_model(output_dir, hf_repo, output_log))
 
-        with tempfile.TemporaryDirectory() as tmpdir:
-            repo = Repository(local_dir=tmpdir, clone_from=hf_repo, use_auth_token=token)
-            shutil.copytree(output_dir, tmpdir, dirs_exist_ok=True)
-            repo.push_to_hub(commit_message="Upload fine-tuned Qwen-Gita LoRA model")
+       progress(1.0, desc="Complete!")
+       
 
-        progress(1.0, desc="Complete!")
-        output_log.append("\n✅ Training complete & model uploaded successfully!")
+       output_log.append("\n✅ Training complete & model uploaded successfully!")
 
     except Exception as e:
         output_log.append(f"\n❌ Error: {e}")