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import streamlit as st |
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import os |
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from PyPDF2 import PdfReader |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.embeddings import GooglePalmEmbeddings |
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from langchain.llms import GooglePalm |
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from langchain.vectorstores import FAISS |
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from langchain.chains import ConversationalRetrievalChain |
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from langchain.memory import ConversationBufferMemory |
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os.environ['GOOGLE_API_KEY'] = 'AIzaSyD8uzXToT4I2ABs7qo_XiuKh8-L2nuWCEM' |
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def get_pdf_text(pdf_docs): |
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text = "" |
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for pdf in pdf_docs: |
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pdf_reader = PdfReader(pdf) |
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for page in pdf_reader.pages: |
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text += page.extract_text() |
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return text |
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def get_text_chunks(text): |
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20) |
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chunks = text_splitter.split_text(text) |
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return chunks |
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def get_vector_store(text_chunks): |
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embeddings = GooglePalmEmbeddings() |
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vector_store = FAISS.from_texts(text_chunks, embedding=embeddings) |
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return vector_store |
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def get_conversational_chain(vector_store): |
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llm = GooglePalm() |
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memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) |
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conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vector_store.as_retriever(), memory=memory) |
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return conversation_chain |
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def user_input(user_question): |
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with st.container(): |
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response = st.session_state.conversation({'question': user_question}) |
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st.session_state.chatHistory = response['chat_history'] |
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file_contents = "" |
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left , right = st.columns((2,1)) |
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with left: |
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for i, message in enumerate(st.session_state.chatHistory): |
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if i % 2 == 0: |
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st.write("Human:", message.content) |
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else: |
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st.write("Bot:", message.content) |
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st.success("Done !") |
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with right: |
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for message in st.session_state.chatHistory: |
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file_contents += f"{message.content}\n" |
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file_name = "Chat_History.txt" |
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st.download_button("Download chat history👈", file_contents, file_name=file_name, mime="text/plain") |
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def summary(summarization): |
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with st.container(): |
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file_contents = '' |
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left , right = st.columns((2,1)) |
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with left: |
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if summarization: |
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response1 = st.session_state.conversation({'question': 'Retrieve one-line topics and their descriptors; create detailed, bulleted summaries for each topic.'}) |
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st.write("summary:\n", response1['answer']) |
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st.success("Done !") |
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else: |
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response1 = {} |
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with right: |
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file_contents = response1.get('answer', '') |
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file_name = "summarization_result.txt" |
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st.download_button("Download summery👈", file_contents, file_name=file_name, mime="text/plain") |
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def main(): |
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st.set_page_config("Chat with Multiple PDFs") |
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st.header("Chat with Multiple PDF 💬") |
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st.write("---") |
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with st.container(): |
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with st.sidebar: |
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st.title("Settings") |
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st.subheader("Upload your Documents") |
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pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Process Button", accept_multiple_files=True) |
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if st.button("Process"): |
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with st.spinner("Processing"): |
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raw_text = get_pdf_text(pdf_docs) |
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text_chunks = get_text_chunks(raw_text) |
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vector_store = get_vector_store(text_chunks) |
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st.session_state.conversation = get_conversational_chain(vector_store) |
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st.success("Done") |
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with st.container(): |
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st.subheader("PDF Summarization") |
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st.write('Click on summary button to get summary on given uploaded file.') |
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summarization = st.button("Summarize 👍") |
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summary(summarization) |
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st.write("---") |
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with st.container(): |
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st.subheader("PDF question-answer section") |
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user_question = st.text_input("Ask a Question from the PDF Files") |
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if "conversation" not in st.session_state: |
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st.session_state.conversation = None |
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if "chatHistory" not in st.session_state: |
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st.session_state.chatHistory = None |
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if user_question: |
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user_input(user_question) |
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st.write('##') |
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if __name__ == "__main__": |
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main() |
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