Update app.py

app.py

@@ -69,51 +69,63 @@ def display_temp_message(message, message_type="info", duration=5):
     placeholder.empty()
     
 # Load models with caching to avoid reloading on every run
-@st.cache_resource
-def load_models():
-    llama_pipe = None
-    score_pipe = None
-
+def load_model_with_messages(loading_message, success_message, loading_function, error_message_prefix):
+    """Load a model with temporary loading and success messages."""
+    # Create placeholder for the loading message
+    loading_placeholder = st.empty()
+    loading_placeholder.info(loading_message)
+    
     try:
-        st.info("Loading Llama 3.2 summarization model...")
-        llama_pipe = pipeline("text-generation", 
-                                     model="meta-llama/Llama-3.2-1B-Instruct",
-                                     device=0,  # Use GPU if available
-                                     torch_dtype=torch.bfloat16,)  # Use FP16 for efficiency
-        #st.success("Llama 3.2 summarization model loaded successfully!")
-
-        # Display success message that will disappear after 5 seconds
+        # Load the model
+        result = loading_function()
+        
+        # Clear the loading message
+        loading_placeholder.empty()
+        
+        # Show temporary success message
         Thread(
             target=display_temp_message, 
-            args=("Llama 3.2 summarization model loaded successfully!", "success"), 
+            args=(success_message, "success"), 
             daemon=True
         ).start()
-    
+        
+        return result
+        
     except Exception as e:
-        st.error(f"Error loading Llama 3.2 summarization model: {e}")
+        # Clear the loading message
+        loading_placeholder.empty()
+        
+        # Show error message
+        st.error(f"{error_message_prefix}: {e}")
         st.error(f"Detailed error: {type(e).__name__}: {str(e)}")
-    
-    try:
-        st.info("Loading sentiment analysis model...")
-        score_pipe = pipeline("text-classification", 
-                              model="cardiffnlp/twitter-roberta-base-sentiment-latest", 
-                              device=0 if torch.cuda.is_available() else -1)
-        #st.success("Sentiment analysis model loaded successfully!")
+        return None
 
-        # Display success message that will disappear after 5 seconds
-        Thread(
-            target=display_temp_message, 
-            args=("Sentiment analysis model loaded successfully!", "success"), 
-            daemon=True
-        ).start()
-    
-    except Exception as e:
-        st.error(f"Error loading sentiment analysis model: {e}")
-    
-    
-    return llama_pipe, score_pipe
+# Define loading functions
+def load_llama_model():
+    return pipeline("text-generation", 
+                   model="meta-llama/Llama-3.2-1B-Instruct",
+                   device=0,  # Use GPU if available
+                   torch_dtype=torch.bfloat16)  # Use FP16 for efficiency
+
+def load_sentiment_model():
+    return pipeline("text-classification", 
+                   model="cardiffnlp/twitter-roberta-base-sentiment-latest", 
+                   device=0 if torch.cuda.is_available() else -1)
+
+# Load models with temporary messages
+llama_pipe = load_model_with_messages(
+    "Loading Llama 3.2 summarization model...",
+    "Llama 3.2 summarization model loaded successfully!",
+    load_llama_model,
+    "Error loading Llama 3.2 summarization model"
+)
 
-llama_pipe, score_pipe = load_models()
+score_pipe = load_model_with_messages(
+    "Loading sentiment analysis model...",
+    "Sentiment analysis model loaded successfully!",
+    load_sentiment_model,
+    "Error loading sentiment analysis model"
+)
 
 def extract_assistant_content(raw_response):
     """Extract only the assistant's content from the Gemma-3 response."""
@@ -144,7 +156,7 @@ def extract_assistant_content(raw_response):
 query_input = st.text_area("Enter your query text for analysis (this does not need to be part of the CSV):")
 uploaded_file = st.file_uploader("Upload Reviews CSV File (must contain a 'reviewText' column)", type=["csv"])
 
-if score_pipe is None or gemma_pipe is None:
+if score_pipe is None or llama_pipe is None:
     st.error("Model loading failed. Please check your model names, token permissions, and GPU configuration.")
 else:
     candidate_docs = []