Update app.py

app.py

@@ -16,20 +16,27 @@ st.set_page_config(page_title="AnthroBot", page_icon="🤖", layout="centered")
 
 # Load model & tokenizer
 @st.cache_resource
-def load_model():
-    peft_config = PeftConfig.from_pretrained("SallySims/AnthroBot_Model_Lora")
-    base_model = AutoModelForCausalLM.from_pretrained(
-        peft_config.base_model_name_or_path,
-        torch_dtype=torch.float16,
-        device_map="auto"
-    )
-    model = PeftModel.from_pretrained(base_model, "SallySims/AnthroBot_Model_Lora")
-    model.eval()
-
-    tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)
-    tokenizer.pad_token = tokenizer.eos_token
-    return model, tokenizer
 
+def load_model():
+    try:
+        peft_config = PeftConfig.from_pretrained("SallySims/AnthroBot_Model_Lora")
+        base_model = AutoModelForCausalLM.from_pretrained(
+            peft_config.base_model_name_or_path,
+            torch_dtype=torch.float16,
+            device_map="auto"
+        )
+        model = PeftModel.from_pretrained(base_model, "SallySims/AnthroBot_Model_Lora")
+        model.eval()
+
+        tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        st.write("✅ Model and tokenizer loaded successfully.")
+        return model, tokenizer
+
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        raise e 
 model, tokenizer = load_model()
 
 # Prediction function
@@ -101,5 +108,3 @@ with tab2:
 
                 csv_output = df.to_csv(index=False).encode("utf-8")
                 st.download_button("📤 Download Predictions", data=csv_output, file_name="predictions.csv")
-
-