removing useless st print

src/streamlit_app.py

@@ -33,7 +33,7 @@ def create_and_save_embeddings(model, data_df):
     Generates and saves document embeddings and the dataframe.
     This function is called only once if the files don't exist.
     """
-    st.info("First time setup: Generating and saving embeddings. This may take a moment...")
+    print("First time setup: Generating and saving embeddings. This may take a moment...")
     # Combine title and abstract for richer embeddings
     data_df['text_to_embed'] = data_df['title'] + ". " + data_df['abstract'].fillna('')
     
@@ -44,9 +44,9 @@ def create_and_save_embeddings(model, data_df):
     try:
         torch.save(corpus_embeddings.cpu(), EMBEDDINGS_FILE)
         data_df.to_pickle(DATA_FILE)
-        st.success("Embeddings and data saved successfully!")
+        print("Embeddings and data saved successfully!")
     except Exception as e:
-        st.warning(f"Could not save embeddings to disk: {e}. Will regenerate on each session.")
+        print(f"Could not save embeddings to disk: {e}. Will regenerate on each session.")
     
     return corpus_embeddings, data_df
 
@@ -65,19 +65,19 @@ def load_data_and_embeddings():
             data_df = pd.read_pickle(DATA_FILE)
             return model, corpus_embeddings, data_df
         except Exception as e:
-            st.warning(f"Could not load saved embeddings: {e}. Regenerating...")
+            print(f"Could not load saved embeddings: {e}. Regenerating...")
 
-    st.info("embeding model path exists: " + str(Path(EMBEDDING_MODEL).exists()))
+    print("embeding model path exists: " + str(Path(EMBEDDING_MODEL).exists()))
     
     # Load the raw data from CSV
     try:
         data_df = pd.read_csv(CSV_FILE)
         corpus_embeddings, data_df = create_and_save_embeddings(model, data_df)
     except FileNotFoundError:
-        st.error(f"CSV file '{CSV_FILE}' not found. Please ensure it's in your repository.")
+        print(f"CSV file '{CSV_FILE}' not found. Please ensure it's in your repository.")
         st.stop()
     except Exception as e:
-        st.error(f"Error loading data: {e}")
+        print(f"Error loading data: {e}")
         st.stop()
         
     return model, corpus_embeddings, data_df