Update app_flash1.py

app_flash1.py

@@ -140,9 +140,11 @@ def train_flashpack_model(dataset_name="rahul7star/prompt-enhancer-dataset",
 def get_flashpack_model(hf_repo="rahul7star/FlashPack"):
     local_model_path = "model.flashpack"
 
+    # 1️⃣ Try local
     if os.path.exists(local_model_path):
         print("✅ Loading local model")
     else:
+        # 2️⃣ Try HF
         try:
             files = list_repo_files(hf_repo)
             if "model.flashpack" in files:
@@ -155,16 +157,24 @@ def get_flashpack_model(hf_repo="rahul7star/FlashPack"):
             print(f"⚠️ Error accessing HF: {e}")
             return None, None, None, None
 
+    # Load the model
     model = GemmaTrainer().from_flashpack(local_model_path)
     model.eval()
+
+    # Load encoder
     tokenizer, embed_model, encode_fn = build_encoder("gpt2")
 
+    # Enhancement function (without dataset)
     @torch.no_grad()
     def enhance_fn(prompt, chat):
         chat = chat or []
-        short_emb = encode_fn(prompt)
-        mapped = model(short_emb.to(device)).cpu()
-        long_prompt = f"✅ Enhanced long prompt for: {prompt}"
+        short_emb = encode_fn(prompt).to(device)
+        mapped = model(short_emb).cpu()
+
+        # Convert the model output tensor to a string representation for demonstration
+        # In practice, you could use a small language head on top of mapped embeddings
+        long_prompt = f"✅ Enhanced long prompt generated for: {prompt}"
+
         chat.append({"role": "user", "content": prompt})
         chat.append({"role": "assistant", "content": long_prompt})
         return chat