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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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import openpyxl |
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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'] = 'your_google_api_key_here' |
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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_excel_text(excel_docs): |
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text = "" |
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for excel_doc in excel_docs: |
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workbook = openpyxl.load_workbook(filename=excel_doc) |
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for sheet in workbook: |
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for row in sheet: |
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for cell in row: |
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text += str(cell.value) + " " |
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return text.strip() |
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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 get_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.markdown(f'<div style="background-color: rgb(30 24 17 / 77%); border-radius: 10px; padding: 10px; margin-bottom: 5px; text-align: end;"><span style="text-align: end;">User:</span> {message.content}</div>', unsafe_allow_html=True) |
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else: |
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st.markdown(f'<div style="background-color: rgb(145 74 1 / 25%); border-radius: 10px; padding: 10px; margin-bottom: 5px; ">Bot: {message.content}</div>', unsafe_allow_html=True) |
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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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def main(): |
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st.set_page_config("DocChat") |
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st.markdown("<style>body { background-color: black; color: white; }</style>", unsafe_allow_html=True) |
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st.markdown("<h3 style='color: orange;'>🧾 DocChat - Chat with multiple documents</h3>", unsafe_allow_html=True) |
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st.caption("🚀 Chat bot developed By :- [Dinesh Abeysinghe](https://www.linkedin.com/in/dinesh-abeysinghe-bb773293) | [GitHub Source Code](https://github.com/dineshabey/AI-Chat_with_document)") |
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st.markdown("<div style= 'text-align: center;'>First need to upload PDF file or Excel file. Then click PROCESS PDF file / PROCESS EXCEL file and next you can start chat with document related things <span style='color: orange;'>Please click like button</span>❤️ and support me and enjoy it.</div>", unsafe_allow_html=True) |
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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 Documents") |
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st.markdown("**PDF files:**") |
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pdf_docs = st.file_uploader("Upload PDF Files", accept_multiple_files=True) |
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if st.button("Process PDF file"): |
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with st.spinner("Processing PDFs..."): |
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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("PDF processed successfully!") |
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st.markdown("**Excel files:**") |
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excel_docs = st.file_uploader("Upload Excel Files", accept_multiple_files=True) |
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if st.button("Process Excel file"): |
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with st.spinner("Processing Excel files..."): |
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raw_text = get_excel_text(excel_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("Excel file processed successfully!") |
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with st.container(): |
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st.subheader("Document Q&A") |
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user_question = st.text_input("Ask a Question from the document") |
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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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get_user_input(user_question) |
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if __name__ == "__main__": |
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main() |
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