Create app.py

app.py

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+from io import BytesIO
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_openai import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+
+def extract_text_from_pdf(pdf_file_obj):
+    pdf_reader = PdfReader(BytesIO(pdf_file_obj.getbuffer()))
+    text = ""
+    for page_num in range(len(pdf_reader.pages)):
+        page_obj = pdf_reader.pages[page_num]
+        text += page_obj.extract_text()
+    return text
+
+def get_text_chunks(text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def get_vectorstore(text_chunks):
+    metadatas = [{"source": f"{i}-pl"} for i in range(len(text_chunks))]
+    embeddings = OpenAIEmbeddings()
+    vectorstore = Chroma.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+def get_conversation_chain(vectorstore):
+    llm = ChatOpenAI()
+
+    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 handle_userinput(user_question):
+    response = st.session_state.conversation({'question': user_question})
+    st.session_state.chat_history = response['chat_history']
+
+    for i, message in enumerate(st.session_state.chat_history):
+        if i % 2 == 0:
+            st.markdown(("User: "+message.content))
+        else:
+            st.markdown(("AI: "+message.content))
+
+def main():
+    st.title("PDF Question Answering")
+    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
+
+    if st.session_state.conversation is not None:
+        st.header("Ask questions from your PDF")
+        user_question = st.text_input("Ask a question about your document:")
+        if user_question:
+            handle_userinput(user_question)
+
+    if st.session_state.conversation is None:
+        st.header("Upload your PDF here")
+        pdf_doc = st.file_uploader("Browse your file here", type="pdf")
+        if pdf_doc is not None:
+            with st.spinner("Processing"):
+                # get pdf text
+                raw_text = extract_text_from_pdf(pdf_doc)
+
+                # get the text chunks
+                text_chunks = get_text_chunks(raw_text)
+
+                # create vector store
+                vectorstore = get_vectorstore(text_chunks)
+
+                # create conversation chain
+                st.session_state.conversation = get_conversation_chain(
+                    vectorstore)
+
+if __name__ == '__main__':
+    main()