Update app.py

app.py

@@ -1,7 +1,12 @@
 import streamlit as st
 import os
+import logging
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
 st.title("Meta LLaMA Text Generation")
 
 @st.cache_resource
@@ -9,16 +14,28 @@ def load_model():
     model_name = "meta-llama/Meta-Llama-3-8B"
     access_token = os.getenv('hf')
     
+    if not access_token:
+        st.error("Hugging Face access token is not set. Please set the environment variable 'hf'.")
+        return None, None
+    
+    logger.info("Loading tokenizer and model...")
     tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=access_token)
     model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=access_token)
     return tokenizer, model
 
 tokenizer, model = load_model()
 
-prompt = st.text_input("Enter a prompt:", "Once upon a time")
+if tokenizer is not None and model is not None:
+    prompt = st.text_input("Enter a prompt:", "Once upon a time")
 
-if st.button("Generate Text"):
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(**inputs, max_length=50)
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    st.write(generated_text)
+    if st.button("Generate Text"):
+        try:
+            inputs = tokenizer(prompt, return_tensors="pt")
+            outputs = model.generate(**inputs, max_length=50)
+            generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+            st.write(generated_text)
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+            logger.error(f"An error occurred during text generation: {e}")
+else:
+    st.error("Failed to load the model. Check the logs for more details.")