Add application file

app.py

@@ -138,6 +138,24 @@ def create_sample_data():
     
     return pd.DataFrame(data)
 
+@st.cache_resource
+def load_or_train_model():
+    """Load trained model or train new one"""
+    model_path = "./trained_travel_planner"
+    
+    if os.path.exists(model_path):
+        try:
+            model = T5ForConditionalGeneration.from_pretrained(model_path)
+            tokenizer = T5Tokenizer.from_pretrained(model_path)
+            if torch.cuda.is_available():
+                model = model.cuda()
+            return model, tokenizer
+        except Exception as e:
+            st.error(f"Error loading trained model: {str(e)}")
+    
+    # If no trained model exists or loading fails, train new model
+    return train_model()
+
 def train_model():
     """Train the T5 model on travel planning data"""
     try:
@@ -207,24 +225,6 @@ def train_model():
         st.error(f"Error during model training: {str(e)}")
         return None, None
 
-@st.cache_resource
-def load_or_train_model():
-    """Load trained model or train new one"""
-    model_path = "./trained_travel_planner"
-    
-    if os.path.exists(model_path):
-        try:
-            model = T5ForConditionalGeneration.from_pretrained(model_path)
-            tokenizer = T5Tokenizer.from_pretrained(model_path)
-            if torch.cuda.is_available():
-                model = model.cuda()
-            return model, tokenizer
-        except Exception as e:
-            st.error(f"Error loading trained model: {str(e)}")
-    
-    # If no trained model exists or loading fails, train new model
-    return train_model()
-
 def generate_travel_plan(destination, days, interests, budget, model, tokenizer):
     """Generate a travel plan using the trained model with enhanced features"""
     try: