Update app.py

app.py

@@ -166,30 +166,44 @@ def train_model_cloud(file_obj, sep, epochs, batch_size, lr, progress=gr.Progres
 # =========================================================
 # 5. LOAD & PREDIKSI
 # =========================================================
+
+cached_model = None
+cached_tokenizer = None
+current_loaded_path = None
+
 def load_model_inference():
-    global active_model_path
+    global active_model_path, cached_model, cached_tokenizer, current_loaded_path
     
-    # Prioritas 1: Model aktif (hasil upload/training barusan)
     if active_model_path and os.path.exists(active_model_path):
         target_path = active_model_path
-    
-    # Prioritas 2: Folder default (upload manual via Files HF)
     elif os.path.exists("model_default") and os.path.exists("model_default/config.json"):
         target_path = "model_default"
-        
-    # Prioritas 3: Download Base Model
     else:
-        return AutoModelForSequenceClassification.from_pretrained("indobenchmark/indobert-base-p1", num_labels=8), \
-               AutoTokenizer.from_pretrained("indobenchmark/indobert-base-p1")
+        target_path = "indobenchmark/indobert-base-p1"
+
+    if cached_model is not None and current_loaded_path == target_path:
+        return cached_model, cached_tokenizer
 
     try:
+        print(f"🔄 Memuat model ke memori dari: {target_path} ...")
         tokenizer = AutoTokenizer.from_pretrained(target_path)
         model = AutoModelForSequenceClassification.from_pretrained(target_path)
         model.eval()
+
+        cached_model = model
+        cached_tokenizer = tokenizer
+        current_loaded_path = target_path
+        return model, tokenizer
+    except Exception as e:
+        print(f"⚠️ Gagal memuat, fallback ke base model. Error: {e}")
+        tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indobert-base-p1")
+        model = AutoModelForSequenceClassification.from_pretrained("indobenchmark/indobert-base-p1", num_labels=8)
+        model.eval()
+        
+        cached_model = model
+        cached_tokenizer = tokenizer
+        current_loaded_path = "indobenchmark/indobert-base-p1"
         return model, tokenizer
-    except:
-        return AutoModelForSequenceClassification.from_pretrained("indobenchmark/indobert-base-p1", num_labels=8), \
-               AutoTokenizer.from_pretrained("indobenchmark/indobert-base-p1")
 
 def predict_text(text):
     if not text: return None
@@ -285,4 +299,4 @@ with gr.Blocks(title="IndoBERT Emotion Cloud") as app:
                     btn_batch.click(predict_csv, inputs=[in_csv_test, in_sep], outputs=out_json)
 
 if __name__ == "__main__":
-    app.launch()
+    app.launch(ssr_mode=False)