Update app.py

app.py

@@ -25,16 +25,43 @@ if 'text_processor' not in st.session_state:
 
 def load_model(model_type):
     """Load appropriate model based on type"""
-    if model_type == "summarize":
-        base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
-        model = PeftModel.from_pretrained(base_model, "pendar02/results")
-        tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
-    else:  # question_focused
-        base_model = AutoModelForSeq2SeqLM.from_pretrained("GanjinZero/biobart-base")
-        model = PeftModel.from_pretrained(base_model, "pendar02/biobart-finetune")
-        tokenizer = AutoTokenizer.from_pretrained("GanjinZero/biobart-base")
+    try:
+        if model_type == "summarize":
+            base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
+            # Try loading with ignore_mismatched_sizes=True and other safety parameters
+            model = PeftModel.from_pretrained(
+                base_model, 
+                "pendar02/results",
+                is_trainable=False,
+                config_kwargs={"inference_mode": True}
+            )
+            tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
+        else:  # question_focused
+            base_model = AutoModelForSeq2SeqLM.from_pretrained("GanjinZero/biobart-base")
+            model = PeftModel.from_pretrained(
+                base_model, 
+                "pendar02/biobart-finetune",
+                is_trainable=False,
+                config_kwargs={"inference_mode": True}
+            )
+            tokenizer = AutoTokenizer.from_pretrained("GanjinZero/biobart-base")
+        
+        # Ensure model is in evaluation mode
+        model.eval()
+        return model, tokenizer
     
-    return model, tokenizer
+    except Exception as e:
+        # Fallback to base model if PEFT loading fails
+        st.warning(f"Error loading PEFT model: {str(e)}. Falling back to base model.")
+        if model_type == "summarize":
+            model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
+            tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
+        else:
+            model = AutoModelForSeq2SeqLM.from_pretrained("GanjinZero/biobart-base")
+            tokenizer = AutoTokenizer.from_pretrained("GanjinZero/biobart-base")
+        
+        model.eval()
+        return model, tokenizer
 
 @st.cache_data
 def process_excel(uploaded_file):