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| import tempfile | |
| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.embeddings import OpenAIEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| import os | |
| import pickle | |
| from datetime import datetime | |
| from backend.generate_metadata import generate_metadata, ingest | |
| MODEL_NAME = "mixtral" | |
| css = ''' | |
| <style> | |
| .chat-message { | |
| padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex | |
| } | |
| .chat-message.user { | |
| background-color: #2b313e | |
| } | |
| .chat-message.bot { | |
| background-color: #475063 | |
| } | |
| .chat-message .avatar { | |
| width: 20%; | |
| } | |
| .chat-message .avatar img { | |
| max-width: 78px; | |
| max-height: 78px; | |
| border-radius: 50%; | |
| object-fit: cover; | |
| } | |
| .chat-message .message { | |
| width: 80%; | |
| padding: 0 1.5rem; | |
| color: #fff; | |
| } | |
| ''' | |
| bot_template = ''' | |
| <div class="chat-message bot"> | |
| <div class="avatar"> | |
| <img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png" | |
| style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;"> | |
| </div> | |
| <div class="message">{{MSG}}</div> | |
| </div> | |
| ''' | |
| user_template = ''' | |
| <div class="chat-message user"> | |
| <div class="avatar"> | |
| <img src="https://i.ibb.co/rdZC7LZ/Photo-logo-1.png"> | |
| </div> | |
| <div class="message">{{MSG}}</div> | |
| </div> | |
| ''' | |
| def get_pdf_text(pdf_docs): | |
| text = "" | |
| for pdf in pdf_docs: | |
| pdf_reader = PdfReader(pdf) | |
| for page in pdf_reader.pages: | |
| text += page.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): | |
| embeddings = OpenAIEmbeddings() | |
| # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") | |
| vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
| return vectorstore | |
| def get_conversation_chain(vectorstore): | |
| llm = ChatOpenAI() | |
| # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512}) | |
| 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): | |
| # Display user message | |
| if i % 2 == 0: | |
| st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True) | |
| else: | |
| print(message) | |
| # Display AI response | |
| st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True) | |
| def safe_vec_store(): | |
| # USE VECTARA INSTEAD | |
| os.makedirs('vectorstore', exist_ok=True) | |
| filename = 'vectors' + datetime.now().strftime('%Y%m%d%H%M') + '.pkl' | |
| file_path = os.path.join('vectorstore', filename) | |
| vector_store = st.session_state.vectorstore | |
| # Serialize and save the entire FAISS object using pickle | |
| with open(file_path, 'wb') as f: | |
| pickle.dump(vector_store, f) | |
| """ | |
| def main(): | |
| st.subheader("Your documents") | |
| if st.session_state.classify: | |
| pdf_doc = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=False) | |
| else: | |
| pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True) | |
| filenames = [file.name for file in pdf_docs if file is not None] | |
| if st.button("Process"): | |
| with st.spinner("Processing"): | |
| if st.session_state.classify: | |
| # THE CLASSIFICATION APP | |
| st.write("Classifying") | |
| plain_text_doc = ingest(pdf_doc.name) | |
| classification_result = generate_metadata(plain_text_doc) | |
| st.write(classification_result) | |
| else: | |
| # NORMAL RAG | |
| loaded_vec_store = None | |
| for filename in filenames: | |
| if ".pkl" in filename: | |
| file_path = os.path.join('vectorstore', filename) | |
| with open(file_path, 'rb') as f: | |
| loaded_vec_store = pickle.load(f) | |
| raw_text = get_pdf_text(pdf_docs) | |
| text_chunks = get_text_chunks(raw_text) | |
| vec = get_vectorstore(text_chunks) | |
| if loaded_vec_store: | |
| vec.merge_from(loaded_vec_store) | |
| st.warning("loaded vectorstore") | |
| if "vectorstore" in st.session_state: | |
| vec.merge_from(st.session_state.vectorstore) | |
| st.warning("merged to existing") | |
| st.session_state.vectorstore = vec | |
| st.session_state.conversation = get_conversation_chain(vec) | |
| st.success("data loaded") | |
| 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 | |
| user_question = st.text_input("Ask a question about your documents:") | |
| if user_question: | |
| handle_userinput(user_question) | |
| with st.sidebar: | |
| st.subheader("Classification instructions") | |
| classifier_docs = st.file_uploader("Upload your instructions here and click on 'Process'", | |
| accept_multiple_files=True) | |
| filenames = [file.name for file in classifier_docs if file is not None] | |
| if st.button("Process Classification"): | |
| st.session_state.classify = True | |
| with st.spinner("Processing"): | |
| st.warning("set classify") | |
| time.sleep(3) | |
| if st.button("Save Embeddings"): | |
| if "vectorstore" in st.session_state: | |
| safe_vec_store() | |
| # st.session_state.vectorstore.save_local("faiss_index") | |
| st.sidebar.success("saved") | |
| else: | |
| st.sidebar.warning("No embeddings to save. Please process documents first.") | |
| if st.button("Load Embeddings"): | |
| st.warning("this function is not in use, just upload the vectorstore") | |
| """ | |
| def main(): | |
| st.set_page_config(page_title="Doc Verify RAG", page_icon=":mag:") | |
| st.write('Anomaly detection for document metadata', unsafe_allow_html=True) | |
| st.header("Doc Verify RAG :mag:") | |
| def set_pw(): | |
| st.session_state.openai_api_key = True | |
| if "openai_api_key" not in st.session_state: | |
| st.session_state.openai_api_key = False | |
| if "openai_org" not in st.session_state: | |
| st.session_state.openai_org = False | |
| if "classify" not in st.session_state: | |
| st.session_state.classify = False | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf", "txt"]) | |
| if uploaded_file is not None: | |
| try: | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp: | |
| tmp.write(uploaded_file.read()) | |
| file_path = tmp.name | |
| st.write(f'Created temporary file {file_path}') | |
| docs = ingest(file_path) | |
| st.write('## Querying Together.ai API') | |
| metadata = generate_metadata(docs) | |
| st.write(f'## Metadata Generated by {MODEL_NAME}') | |
| st.write(metadata) | |
| # Clean up the temporary file | |
| os.remove(file_path) | |
| except Exception as e: | |
| st.error(f'Error: {e}') | |
| with col2: | |
| OPENAI_API_KEY = st.text_input("OPENAI API KEY:", type="password", | |
| disabled=st.session_state.openai_api_key, on_change=set_pw) | |
| classification = st.file_uploader("upload the metadata", type=["csv", "txt"]) | |
| if __name__ == '__main__': | |
| main() | |