update error handling

app.py

@@ -1,76 +1,79 @@
-import streamlit as st
-import os
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter 
-from langchain_community.embeddings import OpenAIEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from langchain.chat_models import ChatOpenAI
-
-def read_pdf(pdf):
-    text = ""
-    pdf_reader = PdfReader(pdf)
-    for page in pdf_reader.pages:
-        text += page.extract_text()
-    return text
-
-def get_chunk_data(text):
-    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=250, length_function=len)
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-def get_vector_store(text):
-    api_key = os.getenv("OPENAI_API_KEY")
-    embeddings = OpenAIEmbeddings(openai_api_key=api_key)
-    vectorstore = FAISS.from_texts(texts=text, embedding=embeddings)
-    return vectorstore
-
-def get_conversation(vectorstore):
-    api_key = os.getenv("OPENAI_API_KEY")
-    llm = ChatOpenAI(openai_api_key=api_key)
-    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
-    conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)
-    return conversation_chain
-
-def handleInput(user_text, conversation_chain):
-    res = conversation_chain({'question': user_text})
-    chat_history = res['chat_history']
-    ans =  res['answer'] 
-    st.write(ans)
-
-def main():
-    load_dotenv()
-    
-    st.set_page_config(page_title="Chat with PDF")
-    
-    if "conversation" not in st.session_state:
-        st.session_state.conversation = None
-    if "chat_history" not in st.session_state:
-        st.session_state.chat_history = None
-    
-    st.header("Chat With PDF")
-
-    user_text = st.text_input("Ask question:")
-    if user_text and st.session_state.conversation:
-        handleInput(user_text, st.session_state.conversation)
-        
-    with st.sidebar:
-        st.subheader("Your Documents")
-        pdf = st.file_uploader("Upload PDF")
-        if pdf and st.button("Submit"):
-            with st.spinner("Processing..."):
-                # Read data from pdf
-                raw_text = read_pdf(pdf)
-                # Split data into chunks
-                load_chunks = get_chunk_data(raw_text)
-                # Create a vector store 
-                vector_store = get_vector_store(load_chunks)
-                
-                # Create conversation chain
-                conversation_chain = get_conversation(vector_store)
-                st.session_state.conversation = conversation_chain  # Save the conversation chain to session state
-
-if __name__ == '__main__':
-    main()
+import streamlit as st
+import os
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter 
+from langchain_community.embeddings import OpenAIEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+
+def read_pdf(pdf):
+    text = ""
+    try:
+        pdf_reader = PdfReader(pdf, strict = False)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    except Exception as e:
+        st.write("Error Reading PDF : {e}")
+    return text
+
+def get_chunk_data(text):
+    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=250, length_function=len)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def get_vector_store(text):
+    api_key = os.getenv("OPENAI_API_KEY")
+    embeddings = OpenAIEmbeddings(openai_api_key=api_key)
+    vectorstore = FAISS.from_texts(texts=text, embedding=embeddings)
+    return vectorstore
+
+def get_conversation(vectorstore):
+    api_key = os.getenv("OPENAI_API_KEY")
+    llm = ChatOpenAI(openai_api_key=api_key)
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)
+    return conversation_chain
+
+def handleInput(user_text, conversation_chain):
+    res = conversation_chain({'question': user_text})
+    chat_history = res['chat_history']
+    ans =  res['answer'] 
+    st.write(ans)
+
+def main():
+    load_dotenv()
+    
+    st.set_page_config(page_title="Chat with PDF")
+    
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = None
+    
+    st.header("Chat With PDF")
+
+    user_text = st.text_input("Ask question:")
+    if user_text and st.session_state.conversation:
+        handleInput(user_text, st.session_state.conversation)
+        
+    with st.sidebar:
+        st.subheader("Your Documents")
+        pdf = st.file_uploader("Upload PDF")
+        if pdf and st.button("Submit"):
+            with st.spinner("Processing..."):
+                # Read data from pdf
+                raw_text = read_pdf(pdf)
+                # Split data into chunks
+                load_chunks = get_chunk_data(raw_text)
+                # Create a vector store 
+                vector_store = get_vector_store(load_chunks)
+                
+                # Create conversation chain
+                conversation_chain = get_conversation(vector_store)
+                st.session_state.conversation = conversation_chain  # Save the conversation chain to session state
+
+if __name__ == '__main__':
+    main()