Update app.py

app.py

@@ -1,3 +1,4 @@
+
 # app.py
 import streamlit as st
 import os
@@ -78,12 +79,12 @@ with st.sidebar:
                 vector_store = process_input(input_data)
                 st.session_state.vectorstore = vector_store
                 st.success("File processed successfully. You can now ask questions.")
-            except st.StreamlitAPIException as e:
-                st.error(f"File upload failed: Streamlit API error - {str(e)}. Check server configuration.")
-            except (PermissionError, OSError) as e:
-                st.error(f"File upload failed: Permission or OS error - {str(e)}. Check file system access.")
+            except PermissionError as e:
+                st.error(f"File upload failed: Permission error - {str(e)}. Check file system access.")
+            except OSError as e:
+                st.error(f"File upload failed: OS error - {str(e)}. Check server configuration.")
             except Exception as e:
-                st.error(f"File upload failed: Unexpected error - {str(e)}. Please try again or check server logs.")
+                st.error(f"File upload failed: {str(e)} (Exception type: {type(e).__name__}). Please try again or check server logs.")
     
     st.subheader("Chat History")
     for i, (q, a) in enumerate(st.session_state.history):
@@ -159,21 +160,21 @@ def process_input(input_data):
     
     # Step 1: Read PDF file in memory
     status.text("Reading PDF file...")
-    progress_bar.progress(0.20)
+    progress_bar.progress(0.25)
     
     pdf_reader = PdfReader(BytesIO(input_data.read()))
     documents = "".join([page.extract_text() or "" for page in pdf_reader.pages])
     
     # Step 2: Split text
     status.text("Splitting text into chunks...")
-    progress_bar.progress(0.40)
+    progress_bar.progress(0.50)
     
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
     texts = text_splitter.split_text(documents)
     
     # Step 3: Create embeddings
     status.text("Creating embeddings...")
-    progress_bar.progress(0.60)
+    progress_bar.progress(0.75)
     
     hf_embeddings = HuggingFaceEmbeddings(
         model_name="sentence-transformers/all-mpnet-base-v2",
@@ -182,7 +183,7 @@ def process_input(input_data):
     
     # Step 4: Initialize FAISS vector store
     status.text("Building vector store...")
-    progress_bar.progress(0.80)
+    progress_bar.progress(1.0)
     
     dimension = len(hf_embeddings.embed_query("test"))
     index = faiss.IndexFlatL2(dimension)
@@ -197,9 +198,8 @@ def process_input(input_data):
     uuids = [str(uuid.uuid4()) for _ in texts]
     vector_store.add_texts(texts, ids=uuids)
     
-    # Step 5: Complete processing
+    # Complete processing
     status.text("Processing complete!")
-    progress_bar.progress(1.0)
     
     return vector_store