Update app.py

app.py

@@ -6,7 +6,7 @@ import faiss
 import numpy as np
 from groq import Client
 
-# Set background image and text color
+# Set background image and customize colors
 background_image_url = "https://jamesclear.com/wp-content/uploads/2020/11/atomic-habits_gallery_hi-res_01.jpg"
 st.markdown(
     f"""
@@ -17,9 +17,12 @@ st.markdown(
         background-position: center center;
         background-repeat: no-repeat;
     }}
+    /* Set title and footer text color to black */
     h1, h2, h3, h4, h5, h6, p {{
         color: black;  /* Set text color to black */
     }}
+    
+    /* Set footer styling */
     .footer {{
         position: fixed;
         bottom: 0;
@@ -31,6 +34,19 @@ st.markdown(
         padding: 10px 0;
         font-size: 14px;
     }}
+    
+    /* Set processing button color to green */
+    .stButton button {{
+        background-color: green;
+        color: white;
+    }}
+
+    /* Set query input block background color to white */
+    .stTextInput input {{
+        background-color: white;
+        color: black;
+    }}
+
     </style>
     """, 
     unsafe_allow_html=True
@@ -60,8 +76,14 @@ vectorstore_path = os.path.join(VECTORSTORE_FOLDER, "index.faiss")  # Correct pa
 
 # Load or create FAISS index
 if os.path.exists(vectorstore_path):
-    index = faiss.read_index(vectorstore_path)
+    # If the index file exists, read it
+    try:
+        index = faiss.read_index(vectorstore_path)
+    except Exception as e:
+        st.error(f"Error reading the FAISS index: {e}")
+        index = faiss.IndexFlatL2(embedder.get_sentence_embedding_dimension())
 else:
+    # If the index file doesn't exist, create a new one
     index = faiss.IndexFlatL2(embedder.get_sentence_embedding_dimension())
 
 # Function to load text from PDF
@@ -99,9 +121,11 @@ def process_and_store_document(file_path):
     index.add(np.array(embeddings))
     
     # Save the updated FAISS index
-    faiss.write_index(index, vectorstore_path)
-    
-    st.success("Document processed and vector store updated!")
+    try:
+        faiss.write_index(index, vectorstore_path)
+        st.success("Document processed and vector store updated!")
+    except Exception as e:
+        st.error(f"Error saving the FAISS index: {e}")
 
 # User interface for Streamlit
 st.title("Atomic Habits RAG Application")