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| import streamlit as st | |
| from streamlit_chat import message | |
| from openai.error import OpenAIError | |
| from .utils import ( | |
| parse_docx, | |
| parse_pdf, | |
| parse_txt, | |
| search_docs, | |
| embed_docs, | |
| text_to_docs, | |
| get_answer, | |
| ) | |
| from uuid import uuid4 | |
| def clear_submit(): | |
| st.session_state["submit"] = False | |
| def set_openai_api_key(api_key: str): | |
| st.session_state["OPENAI_API_KEY"] = api_key | |
| def qa_main(): | |
| st.markdown("<h1>This app allows to chat with files!</h1>", unsafe_allow_html=True) | |
| st.markdown(\ | |
| """ | |
| Developed using LangChain and OpenAI Embeddings.</p> | |
| Before hitting on "Submit", please make sure you have uploaded a file and entered a question. | |
| You can upload files using the sidebar on the left. | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| index = None | |
| doc = None | |
| with st.sidebar: | |
| user_secret = st.text_input( | |
| "OpenAI API Key", | |
| type="password", | |
| placeholder="Paste your OpenAI API key here (sk-...)", | |
| help="You can get your API key from https://platform.openai.com/account/api-keys.", | |
| value=st.session_state.get("OPENAI_API_KEY", ""), | |
| ) | |
| if user_secret: | |
| set_openai_api_key(user_secret) | |
| uploaded_file = st.file_uploader( | |
| "Upload a pdf, docx, or txt file", | |
| type=["pdf", "docx", "txt", "py", "json", "html", "css", "md"], | |
| help="Scanned documents are not supported yet!", | |
| on_change=clear_submit, | |
| accept_multiple_files=False, | |
| ) | |
| # reading the files | |
| if uploaded_file is not None: | |
| if uploaded_file.name.endswith(".pdf"): | |
| doc = parse_pdf(uploaded_file) | |
| elif uploaded_file.name.endswith(".docx"): | |
| doc = parse_docx(uploaded_file) | |
| elif uploaded_file.name.endswith(".txt"): | |
| doc = parse_txt(uploaded_file) | |
| else: | |
| st.error("File type not yet supported! Supported files: [.pdf, .docx, .txt]") | |
| doc = None | |
| text = text_to_docs(text=tuple(doc)) | |
| st.write(text[:1]) | |
| try: | |
| with st.spinner("Indexing document(s)... This may take some time."): | |
| index = embed_docs(tuple(text)) | |
| st.session_state["api_key_configured"] = True | |
| except OpenAIError as e: | |
| st.error(e._message) | |
| tab1, tab2 = st.tabs(["Chat With File", "About the Application"]) | |
| with tab1: | |
| if 'generated' not in st.session_state: | |
| st.session_state['generated'] = [] | |
| if 'past' not in st.session_state: | |
| st.session_state['past'] = [] | |
| def get_text(): | |
| if user_secret: | |
| st.header("Ask me something about the document:") | |
| input_text = st.text_area("You:", on_change=clear_submit) | |
| return input_text | |
| user_input = get_text() | |
| button = st.button("Submit") | |
| if button or st.session_state.get("submit"): | |
| if not user_input: | |
| st.error("Please enter a question!") | |
| else: | |
| st.session_state["submit"] = True | |
| sources = search_docs(index, user_input) | |
| try: | |
| answer = get_answer(sources, user_input) | |
| st.session_state.past.append(user_input) | |
| st.session_state.generated.append(answer["output_text"]) | |
| except OpenAIError as e: | |
| st.error(e._message) | |
| if st.session_state['past']: | |
| for i in range(len(st.session_state['past'])-1, -1, -1): | |
| message(st.session_state['generated'][i], key=str(uuid4())) | |
| message(st.session_state['past'][i], is_user=True, key=str(uuid4())) | |
| with tab2: | |
| st.write('See sources') | |
| # st.write('Chat with Files enables user to extract all the information from a file. User can obtain the transcription, the embedding of each segment and also ask questions to the file through a chat.') | |
| # st.write('Features include- ') | |
| # st.write('1. Reading any pdf, docx or plain txt (such as python programs) file') | |
| # st.write('2. Embedding texts segments with Langchain and OpenAI') | |
| # st.write('3. Chatting with the file using streamlit-chat and LangChain QA with source and the GPT4 model') |