Update app.py

app.py

@@ -39,8 +39,8 @@ def get_vector_store(chunks):
             model="models/embedding-001")  # type: ignore
         vector_store = FAISS.from_texts(chunks, embedding=embeddings)
         vector_store.save_local("faiss_index")
-    except (GoogleAPIError, InvalidArgument) as e:
-        st.error("Error processing embeddings. Please try again in a minute.")
+    except (GoogleAPIError, InvalidArgument):
+        raise RuntimeError("Error processing embeddings. Please try again in a minute.")
 
 # Function to get conversational chain
 def get_conversational_chain():
@@ -60,8 +60,8 @@ def get_conversational_chain():
                                 input_variables=["context", "question"])
         chain = load_qa_chain(llm=model, chain_type="stuff", prompt=prompt)
         return chain
-    except (GoogleAPIError, InvalidArgument) as e:
-        st.error("Error creating conversational chain. Please try again in a minute.")
+    except (GoogleAPIError, InvalidArgument):
+        raise RuntimeError("Error creating conversational chain. Please try again in a minute.")
 
 # Function to clear chat history
 def clear_chat_history():
@@ -79,8 +79,8 @@ def user_input(user_question):
         response = chain(
             {"input_documents": docs, "question": user_question}, return_only_outputs=True)
         return response
-    except (GoogleAPIError, InvalidArgument) as e:
-        st.error("Error processing request. Please try again in a minute.")
+    except (GoogleAPIError, InvalidArgument):
+        raise RuntimeError("Error processing request. Please try again in a minute.")
 
 # Main function to run the Streamlit app
 def main():
@@ -136,11 +136,14 @@ def main():
             "Upload your PDF files", accept_multiple_files=True)
         if st.button("Submit & Process", key="submit_button"):
             if pdf_docs:
-                with st.spinner("Processing..."):
-                    raw_text = get_pdf_text(pdf_docs)
-                    text_chunks = get_text_chunks(raw_text)
-                    get_vector_store(text_chunks)
-                    st.success("Processing completed!")
+                try:
+                    with st.spinner("Processing..."):
+                        raw_text = get_pdf_text(pdf_docs)
+                        text_chunks = get_text_chunks(raw_text)
+                        get_vector_store(text_chunks)
+                        st.success("Processing completed!")
+                except RuntimeError as e:
+                    st.error(str(e))
             else:
                 st.error("Please upload at least one PDF file.")
 
@@ -167,16 +170,17 @@ def main():
 
         # Generate bot response
         if st.session_state.messages[-1]["role"] != "assistant":
-            with st.chat_message("assistant"):
-                with st.spinner("Thinking..."):
-                    response = user_input(prompt)
-                    if response:
-                        full_response = ''.join(response['output_text'])
-                        st.write(full_response)
-                        message = {"role": "assistant", "content": full_response}
-                        st.session_state.messages.append(message)
-                    else:
-                        st.error("Please try again in a minute.")
+            try:
+                with st.chat_message("assistant"):
+                    with st.spinner("Thinking..."):
+                        response = user_input(prompt)
+                        if response:
+                            full_response = ''.join(response['output_text'])
+                            st.write(full_response)
+                            message = {"role": "assistant", "content": full_response}
+                            st.session_state.messages.append(message)
+            except RuntimeError as e:
+                st.error(str(e))
 
 if __name__ == "__main__":
     main()