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| import streamlit as st | |
| from streamlit_lottie import st_lottie | |
| import fitz # PyMuPDF | |
| import requests | |
| import os, shutil | |
| import sidebar | |
| import llm_model | |
| def index_document(uploaded_file): | |
| if uploaded_file is not None: | |
| # Specify the folder path where you want to store the uploaded file in the 'assets' folder | |
| assets_folder = "assets/uploaded_files" | |
| if not os.path.exists(assets_folder): | |
| os.makedirs(assets_folder) | |
| # Save the uploaded file to the specified folder | |
| file_path = os.path.join(assets_folder, uploaded_file.name) | |
| with open(file_path, "wb") as f: | |
| f.write(uploaded_file.getvalue()) | |
| file_name = os.path.join(assets_folder, uploaded_file.name) | |
| st.success(f"File '{file_name}' uploaded !") | |
| with st.spinner("Indexing document... This is a free CPU version and may take a while ⏳"): | |
| llm_model.create_vector_db(file_name, instructor_embeddings) | |
| return file_name | |
| else: | |
| return None | |
| def load_lottieurl(url: str): | |
| r = requests.get(url) | |
| if r.status_code != 200: | |
| return None | |
| return r.json() | |
| def is_query_valid(query: str) -> bool: | |
| if not query: | |
| st.error("Please enter a question!") | |
| return False | |
| return True | |
| # Function to load model parameters | |
| def load_model(): | |
| return llm_model.load_model_params() | |
| st.set_page_config(page_title="Document QA Bot") | |
| lottie_book = load_lottieurl("https://assets4.lottiefiles.com/temp/lf20_aKAfIn.json") | |
| st_lottie(lottie_book, speed=1, height=200, key="initial") | |
| # Place the title below the Lottie animation | |
| st.title("Document Q&A Bot 🤖") | |
| # Left Sidebar | |
| sidebar.sidebar() | |
| # st.sidebar.header("Upload PDF") | |
| # load model parameters | |
| llm, instructor_embeddings = load_model() | |
| # Upload file through Streamlit | |
| uploaded_file = st.file_uploader("Upload a file", type=["pdf", "doc", "docx", "txt"]) | |
| filename = index_document(uploaded_file) | |
| print(filename) | |
| if not filename: | |
| st.stop() | |
| with st.form(key="qa_form"): | |
| query = st.text_area("Ask a question about the document") | |
| submit = st.form_submit_button("Submit") | |
| if submit: | |
| if not is_query_valid(query): | |
| st.stop() | |
| # Output Columns | |
| answer_col, sources_col = st.columns(2) | |
| qa_chain = llm_model.document_parser(instructor_embeddings, llm) | |
| result = qa_chain(query) | |
| with answer_col: | |
| st.markdown("#### Answer") | |
| st.markdown(result["result"]) | |
| with sources_col: | |
| st.markdown("#### Sources") | |
| if not ("i don't know" in result["result"].lower()): | |
| for source in result["source_documents"]: | |
| st.markdown(source.page_content) | |
| st.markdown(source.metadata["source"]) | |
| st.markdown("--------------------------") | |