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| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from langchain.vectorstores import FAISS | |
| from langchain.chains import LLMChain, ConversationalRetrievalChain | |
| from utils import (get_hf_embeddings, | |
| get_openAI_chat_model, | |
| get_hf_model, | |
| get_local_gpt4_model, | |
| set_LangChain_tracking, | |
| check_password) | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.docstore.document import Document | |
| embeddings = get_hf_embeddings() | |
| openai_chat_model = get_openAI_chat_model() | |
| #local_model = get_local_gpt4_model(model = "GPT4All-13B-snoozy.ggmlv3.q4_0.bin") | |
| hf_chat_model = get_hf_model(repo_id = "tiiuae/falcon-40b") | |
| ## Preparing Prompt | |
| from langchain.prompts import PromptTemplate | |
| entity_extraction_template = """ | |
| Extract all top 10 important entites from the following context \ | |
| return as python list \ | |
| {input_text} \ | |
| List of entities:""" | |
| ENTITY_EXTRACTION_PROMPT = PromptTemplate.from_template(entity_extraction_template) | |
| def get_qa_prompt(List_of_entities): | |
| qa_template = """ | |
| Use the following pieces of context to answer the question at the end. \ | |
| Use the following list of entities as your working scope. \ | |
| If the question is out of given list of entities, just say that your question \ | |
| is out of scope and give them the list of entities as your working scope \ | |
| If you dont know the answer, just say that you don't know and tell \ | |
| the user to seach web for more information, don't try to make up \ | |
| an answer. Use three sentences maximum and keep the answer as \ | |
| concise as possible.\ | |
| list of entities: \ | |
| """ + str(List_of_entities) + """ \ | |
| context: {context} \ | |
| Question: {question} \ | |
| Helpful Answer:""" | |
| print(qa_template) | |
| QA_CHAIN_PROMPT = PromptTemplate.from_template(qa_template) | |
| return QA_CHAIN_PROMPT | |
| if check_password(): | |
| st.title("Chat with your PDF ") | |
| st.session_state.file_tracking = "new_run" | |
| with st.expander("Upload your PDF : ", expanded=True): | |
| st.session_state.lc_tracking = st.text_input("Please give a name to your session?") | |
| input_file = st.file_uploader(label = "Upload a file", | |
| accept_multiple_files=False, | |
| type=["pdf"], | |
| ) | |
| if st.button("Process the file"): | |
| st.session_state.file_tracking = "req_to_process" | |
| try: | |
| set_LangChain_tracking(project=str(st.session_state.lc_tracking)) | |
| except: | |
| set_LangChain_tracking(project="default") | |
| if st.session_state.file_tracking == "req_to_process" and input_file is not None: | |
| # Load Text Data | |
| input_text = '' | |
| bytes_data = PdfReader(input_file) | |
| for page in bytes_data.pages: | |
| input_text += page.extract_text() | |
| st.session_state.ner_chain = LLMChain(llm=hf_chat_model, prompt=ENTITY_EXTRACTION_PROMPT) | |
| st.session_state.ners = st.session_state.ner_chain.run(input_text=input_text, verbose=True) | |
| input_text = input_text.replace('\n', '') | |
| text_doc_chunks = [Document(page_content=x, metadata={}) for x in input_text.split('.')] | |
| # Embed and VectorStore | |
| vector_store = FAISS.from_documents(text_doc_chunks, embeddings) | |
| st.session_state.chat_history = [] | |
| st.session_state.formatted_prompt = get_qa_prompt(st.session_state.ners) | |
| st.session_state.chat_chain = ConversationalRetrievalChain.from_llm( | |
| hf_chat_model, | |
| chain_type="stuff", # "stuff", "map_reduce", "refine", "map_rerank" | |
| verbose=True, | |
| retriever=vector_store.as_retriever(), | |
| # search_type="mmr" | |
| # search_kwargs={"k": 1} | |
| # search_type="similarity_score_threshold", search_kwargs={"score_threshold": .5} | |
| combine_docs_chain_kwargs={"prompt": st.session_state.formatted_prompt}, | |
| ) | |
| if "chat_chain" in st.session_state: | |
| st.header("We are ready to start chat with your pdf") | |
| st.subheader("The scope of your PDF is: ") | |
| st.markdown(st.session_state.ners) | |
| else: | |
| st.header("Upload and Process your file first") | |
| if "chat_chain" in st.session_state and st.session_state.chat_history is not None: | |
| if question := st.chat_input("Please type some thing here?"): | |
| response = st.session_state.chat_chain({"question": question, "chat_history": st.session_state.chat_history}) | |
| st.session_state.chat_history.append((question, response["answer"])) | |
| # Display chat messages from history on app rerun | |
| for message in st.session_state.chat_history: | |
| with st.chat_message("user"): | |
| st.markdown(message[0]) | |
| with st.chat_message("assistant"): | |
| st.markdown(message[1]) |