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| import os | |
| import re | |
| from hashlib import blake2b | |
| from tempfile import NamedTemporaryFile | |
| import dotenv | |
| from grobid_quantities.quantities import QuantitiesAPI | |
| from langchain.llms.huggingface_hub import HuggingFaceHub | |
| from langchain.memory import ConversationBufferWindowMemory | |
| from streamlit_pdf_viewer import pdf_viewer | |
| dotenv.load_dotenv(override=True) | |
| import streamlit as st | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings | |
| from document_qa.document_qa_engine import DocumentQAEngine | |
| from document_qa.grobid_processors import GrobidAggregationProcessor, decorate_text_with_annotations | |
| from grobid_client_generic import GrobidClientGeneric | |
| OPENAI_MODELS = ['chatgpt-3.5-turbo', | |
| "gpt-4", | |
| "gpt-4-1106-preview"] | |
| OPEN_MODELS = { | |
| 'mistral-7b-instruct-v0.1': 'mistralai/Mistral-7B-Instruct-v0.1', | |
| "zephyr-7b-beta": 'HuggingFaceH4/zephyr-7b-beta' | |
| } | |
| DISABLE_MEMORY = ['zephyr-7b-beta'] | |
| if 'rqa' not in st.session_state: | |
| st.session_state['rqa'] = {} | |
| if 'model' not in st.session_state: | |
| st.session_state['model'] = None | |
| if 'api_keys' not in st.session_state: | |
| st.session_state['api_keys'] = {} | |
| if 'doc_id' not in st.session_state: | |
| st.session_state['doc_id'] = None | |
| if 'loaded_embeddings' not in st.session_state: | |
| st.session_state['loaded_embeddings'] = None | |
| if 'hash' not in st.session_state: | |
| st.session_state['hash'] = None | |
| if 'git_rev' not in st.session_state: | |
| st.session_state['git_rev'] = "unknown" | |
| if os.path.exists("revision.txt"): | |
| with open("revision.txt", 'r') as fr: | |
| from_file = fr.read() | |
| st.session_state['git_rev'] = from_file if len(from_file) > 0 else "unknown" | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| if 'ner_processing' not in st.session_state: | |
| st.session_state['ner_processing'] = False | |
| if 'uploaded' not in st.session_state: | |
| st.session_state['uploaded'] = False | |
| if 'memory' not in st.session_state: | |
| st.session_state['memory'] = None | |
| if 'binary' not in st.session_state: | |
| st.session_state['binary'] = None | |
| if 'annotations' not in st.session_state: | |
| st.session_state['annotations'] = None | |
| if 'pdf' not in st.session_state: | |
| st.session_state['pdf'] = None | |
| st.set_page_config( | |
| page_title="Scientific Document Insights Q/A", | |
| page_icon="📝", | |
| initial_sidebar_state="expanded", | |
| layout="wide", | |
| menu_items={ | |
| 'Get Help': 'https://github.com/lfoppiano/document-qa', | |
| 'Report a bug': "https://github.com/lfoppiano/document-qa/issues", | |
| 'About': "Upload a scientific article in PDF, ask questions, get insights." | |
| } | |
| ) | |
| css_modify_left_column = ''' | |
| <style> | |
| [data-testid="stHorizontalBlock"] > div:nth-child(1) { | |
| overflow: hidden; | |
| background-color: red; | |
| height: 70vh; | |
| } | |
| </style> | |
| ''' | |
| css_modify_right_column = ''' | |
| <style> | |
| [data-testid="stHorizontalBlock"]> div:first-child { | |
| background-color: red; | |
| position: fixed | |
| height: 70vh; | |
| } | |
| </style> | |
| ''' | |
| css_disable_scrolling_container = ''' | |
| <style> | |
| [data-testid="ScrollToBottomContainer"] { | |
| overflow: hidden; | |
| } | |
| </style> | |
| ''' | |
| # st.markdown(css_lock_column_fixed, unsafe_allow_html=True) | |
| # st.markdown(css2, unsafe_allow_html=True) | |
| def new_file(): | |
| st.session_state['loaded_embeddings'] = None | |
| st.session_state['doc_id'] = None | |
| st.session_state['uploaded'] = True | |
| if st.session_state['memory']: | |
| st.session_state['memory'].clear() | |
| def clear_memory(): | |
| st.session_state['memory'].clear() | |
| # @st.cache_resource | |
| def init_qa(model, api_key=None): | |
| ## For debug add: callbacks=[PromptLayerCallbackHandler(pl_tags=["langchain", "chatgpt", "document-qa"])]) | |
| if model in OPENAI_MODELS: | |
| st.session_state['memory'] = ConversationBufferWindowMemory(k=4) | |
| if api_key: | |
| chat = ChatOpenAI(model_name=model, | |
| temperature=0, | |
| openai_api_key=api_key, | |
| frequency_penalty=0.1) | |
| embeddings = OpenAIEmbeddings(openai_api_key=api_key) | |
| else: | |
| chat = ChatOpenAI(model_name=model, | |
| temperature=0, | |
| frequency_penalty=0.1) | |
| embeddings = OpenAIEmbeddings() | |
| elif model in OPEN_MODELS: | |
| chat = HuggingFaceHub( | |
| repo_id=OPEN_MODELS[model], | |
| model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048} | |
| ) | |
| embeddings = HuggingFaceEmbeddings( | |
| model_name="all-MiniLM-L6-v2") | |
| st.session_state['memory'] = ConversationBufferWindowMemory(k=4) if model not in DISABLE_MEMORY else None | |
| else: | |
| st.error("The model was not loaded properly. Try reloading. ") | |
| st.stop() | |
| return | |
| return DocumentQAEngine(chat, embeddings, grobid_url=os.environ['GROBID_URL'], memory=st.session_state['memory']) | |
| def init_ner(): | |
| quantities_client = QuantitiesAPI(os.environ['GROBID_QUANTITIES_URL'], check_server=True) | |
| materials_client = GrobidClientGeneric(ping=True) | |
| config_materials = { | |
| 'grobid': { | |
| "server": os.environ['GROBID_MATERIALS_URL'], | |
| 'sleep_time': 5, | |
| 'timeout': 60, | |
| 'url_mapping': { | |
| 'processText_disable_linking': "/service/process/text?disableLinking=True", | |
| # 'processText_disable_linking': "/service/process/text" | |
| } | |
| } | |
| } | |
| materials_client.set_config(config_materials) | |
| gqa = GrobidAggregationProcessor(None, | |
| grobid_quantities_client=quantities_client, | |
| grobid_superconductors_client=materials_client | |
| ) | |
| return gqa | |
| gqa = init_ner() | |
| def get_file_hash(fname): | |
| hash_md5 = blake2b() | |
| with open(fname, "rb") as f: | |
| for chunk in iter(lambda: f.read(4096), b""): | |
| hash_md5.update(chunk) | |
| return hash_md5.hexdigest() | |
| def play_old_messages(): | |
| if st.session_state['messages']: | |
| for message in st.session_state['messages']: | |
| if message['role'] == 'user': | |
| with st.chat_message("user"): | |
| st.markdown(message['content']) | |
| elif message['role'] == 'assistant': | |
| with st.chat_message("assistant"): | |
| if mode == "LLM": | |
| st.markdown(message['content'], unsafe_allow_html=True) | |
| else: | |
| st.write(message['content']) | |
| # is_api_key_provided = st.session_state['api_key'] | |
| with st.sidebar: | |
| st.session_state['model'] = model = st.selectbox( | |
| "Model:", | |
| options=OPENAI_MODELS + list(OPEN_MODELS.keys()), | |
| index=4, | |
| placeholder="Select model", | |
| help="Select the LLM model:", | |
| disabled=st.session_state['doc_id'] is not None or st.session_state['uploaded'] | |
| ) | |
| st.markdown( | |
| ":warning: [Usage disclaimer](https://github.com/lfoppiano/document-qa/tree/review-interface#disclaimer-on-data-security-and-privacy-%EF%B8%8F) :warning: ") | |
| if (model in OPEN_MODELS) and model not in st.session_state['api_keys']: | |
| if 'HUGGINGFACEHUB_API_TOKEN' not in os.environ: | |
| api_key = st.text_input('Huggingface API Key', type="password") | |
| st.markdown("Get it [here](https://huggingface.co/docs/hub/security-tokens)") | |
| else: | |
| api_key = os.environ['HUGGINGFACEHUB_API_TOKEN'] | |
| if api_key: | |
| # st.session_state['api_key'] = is_api_key_provided = True | |
| if model not in st.session_state['rqa'] or model not in st.session_state['api_keys']: | |
| with st.spinner("Preparing environment"): | |
| st.session_state['api_keys'][model] = api_key | |
| # if 'HUGGINGFACEHUB_API_TOKEN' not in os.environ: | |
| # os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key | |
| st.session_state['rqa'][model] = init_qa(model) | |
| elif model in OPENAI_MODELS and model not in st.session_state['api_keys']: | |
| if 'OPENAI_API_KEY' not in os.environ: | |
| api_key = st.text_input('OpenAI API Key', type="password") | |
| st.markdown("Get it [here](https://platform.openai.com/account/api-keys)") | |
| else: | |
| api_key = os.environ['OPENAI_API_KEY'] | |
| if api_key: | |
| if model not in st.session_state['rqa'] or model not in st.session_state['api_keys']: | |
| with st.spinner("Preparing environment"): | |
| st.session_state['api_keys'][model] = api_key | |
| if 'OPENAI_API_KEY' not in os.environ: | |
| st.session_state['rqa'][model] = init_qa(model, api_key) | |
| else: | |
| st.session_state['rqa'][model] = init_qa(model) | |
| # else: | |
| # is_api_key_provided = st.session_state['api_key'] | |
| st.button( | |
| 'Reset chat memory.', | |
| key="reset-memory-button", | |
| on_click=clear_memory, | |
| help="Clear the conversational memory. Currently implemented to retrain the 4 most recent messages.", | |
| disabled=model in st.session_state['rqa'] and st.session_state['rqa'][model].memory is None) | |
| left_column, right_column = st.columns([1, 1]) | |
| with right_column: | |
| st.title("📝 Scientific Document Insights Q/A") | |
| st.subheader("Upload a scientific article in PDF, ask questions, get insights.") | |
| st.markdown( | |
| ":warning: Do not upload sensitive data. We **temporarily** store text from the uploaded PDF documents solely for the purpose of processing your request, and we **do not assume responsibility** for any subsequent use or handling of the data submitted to third parties LLMs.") | |
| uploaded_file = st.file_uploader("Upload an article", | |
| type=("pdf", "txt"), | |
| on_change=new_file, | |
| disabled=st.session_state['model'] is not None and st.session_state['model'] not in | |
| st.session_state['api_keys'], | |
| help="The full-text is extracted using Grobid. ") | |
| question = st.chat_input( | |
| "Ask something about the article", | |
| # placeholder="Can you give me a short summary?", | |
| disabled=not uploaded_file | |
| ) | |
| with st.sidebar: | |
| st.header("Settings") | |
| mode = st.radio("Query mode", ("LLM", "Embeddings"), disabled=not uploaded_file, index=0, horizontal=True, | |
| help="LLM will respond the question, Embedding will show the " | |
| "paragraphs relevant to the question in the paper.") | |
| chunk_size = st.slider("Chunks size", -1, 2000, value=-1, | |
| help="Size of chunks in which the document is partitioned", | |
| disabled=uploaded_file is not None) | |
| if chunk_size == -1: | |
| context_size = st.slider("Context size", 3, 20, value=10, | |
| help="Number of paragraphs to consider when answering a question", | |
| disabled=not uploaded_file) | |
| else: | |
| context_size = st.slider("Context size", 3, 10, value=4, | |
| help="Number of chunks to consider when answering a question", | |
| disabled=not uploaded_file) | |
| st.session_state['ner_processing'] = st.checkbox("Identify materials and properties.") | |
| st.markdown( | |
| 'The LLM responses undergo post-processing to extract <span style="color:orange">physical quantities, measurements</span>, and <span style="color:green">materials</span> mentions.', | |
| unsafe_allow_html=True) | |
| st.divider() | |
| st.header("Documentation") | |
| st.markdown("https://github.com/lfoppiano/document-qa") | |
| st.markdown( | |
| """Upload a scientific article as PDF document. Once the spinner stops, you can proceed to ask your questions.""") | |
| if st.session_state['git_rev'] != "unknown": | |
| st.markdown("**Revision number**: [" + st.session_state[ | |
| 'git_rev'] + "](https://github.com/lfoppiano/document-qa/commit/" + st.session_state['git_rev'] + ")") | |
| st.header("Query mode (Advanced use)") | |
| st.markdown( | |
| """By default, the mode is set to LLM (Language Model) which enables question/answering. You can directly ask questions related to the document content, and the system will answer the question using content from the document.""") | |
| st.markdown( | |
| """If you switch the mode to "Embedding," the system will return specific chunks from the document that are semantically related to your query. This mode helps to test why sometimes the answers are not satisfying or incomplete. """) | |
| if uploaded_file and not st.session_state.loaded_embeddings: | |
| if model not in st.session_state['api_keys']: | |
| st.error("Before uploading a document, you must enter the API key. ") | |
| st.stop() | |
| with right_column: | |
| with st.spinner('Reading file, calling Grobid, and creating memory embeddings...'): | |
| binary = uploaded_file.getvalue() | |
| tmp_file = NamedTemporaryFile() | |
| tmp_file.write(bytearray(binary)) | |
| st.session_state['binary'] = binary | |
| st.session_state['doc_id'] = hash = st.session_state['rqa'][model].create_memory_embeddings(tmp_file.name, | |
| chunk_size=chunk_size, | |
| perc_overlap=0.1, | |
| include_biblio=True) | |
| st.session_state['loaded_embeddings'] = True | |
| st.session_state.messages = [] | |
| # timestamp = datetime.utcnow() | |
| def rgb_to_hex(rgb): | |
| return "#{:02x}{:02x}{:02x}".format(*rgb) | |
| def generate_color_gradient(num_elements): | |
| # Define warm and cold colors in RGB format | |
| warm_color = (255, 165, 0) # Orange | |
| cold_color = (0, 0, 255) # Blue | |
| # Generate a linear gradient of colors | |
| color_gradient = [ | |
| rgb_to_hex(tuple(int(warm * (1 - i/num_elements) + cold * (i/num_elements)) for warm, cold in zip(warm_color, cold_color))) | |
| for i in range(num_elements) | |
| ] | |
| return color_gradient | |
| with right_column: | |
| # css = ''' | |
| # <style> | |
| # [data-testid="column"] { | |
| # overflow: auto; | |
| # height: 70vh; | |
| # } | |
| # </style> | |
| # ''' | |
| # st.markdown(css, unsafe_allow_html=True) | |
| # st.markdown( | |
| # """ | |
| # <script> | |
| # document.querySelectorAll('[data-testid="column"]').scrollIntoView({behavior: "smooth"}); | |
| # </script> | |
| # """, | |
| # unsafe_allow_html=True, | |
| # ) | |
| if st.session_state.loaded_embeddings and question and len(question) > 0 and st.session_state.doc_id: | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| if message['mode'] == "LLM": | |
| st.markdown(message["content"], unsafe_allow_html=True) | |
| elif message['mode'] == "Embeddings": | |
| st.write(message["content"]) | |
| if model not in st.session_state['rqa']: | |
| st.error("The API Key for the " + model + " is missing. Please add it before sending any query. `") | |
| st.stop() | |
| with st.chat_message("user"): | |
| st.markdown(question) | |
| st.session_state.messages.append({"role": "user", "mode": mode, "content": question}) | |
| text_response = None | |
| if mode == "Embeddings": | |
| with st.spinner("Generating LLM response..."): | |
| text_response = st.session_state['rqa'][model].query_storage(question, st.session_state.doc_id, | |
| context_size=context_size) | |
| elif mode == "LLM": | |
| with st.spinner("Generating response..."): | |
| _, text_response, coordinates = st.session_state['rqa'][model].query_document(question, | |
| st.session_state.doc_id, | |
| context_size=context_size) | |
| annotations = [ | |
| GrobidAggregationProcessor.box_to_dict(coo) for coo in [c.split(",") for coord in | |
| coordinates for c in coord] | |
| ] | |
| gradients = generate_color_gradient(len(annotations)) | |
| for i, color in enumerate(gradients): | |
| annotations[i]['color'] = color | |
| st.session_state['annotations'] = annotations | |
| # with left_column: | |
| # pdf_viewer(input=st.session_state['binary'], annotations=st.session_state['annotations'], key=1) | |
| if not text_response: | |
| st.error("Something went wrong. Contact Luca Foppiano ([email protected]) to report the issue.") | |
| with st.chat_message("assistant"): | |
| if mode == "LLM": | |
| if st.session_state['ner_processing']: | |
| with st.spinner("Processing NER on LLM response..."): | |
| entities = gqa.process_single_text(text_response) | |
| decorated_text = decorate_text_with_annotations(text_response.strip(), entities) | |
| decorated_text = decorated_text.replace('class="label material"', 'style="color:green"') | |
| decorated_text = re.sub(r'class="label[^"]+"', 'style="color:orange"', decorated_text) | |
| text_response = decorated_text | |
| st.markdown(text_response, unsafe_allow_html=True) | |
| else: | |
| st.write(text_response) | |
| st.session_state.messages.append({"role": "assistant", "mode": mode, "content": text_response}) | |
| # if len(st.session_state.messages) > 1: | |
| # last_answer = st.session_state.messages[len(st.session_state.messages)-1] | |
| # if last_answer['role'] == "assistant": | |
| # last_question = st.session_state.messages[len(st.session_state.messages)-2] | |
| # st.session_state.memory.save_context({"input": last_question['content']}, {"output": last_answer['content']}) | |
| elif st.session_state.loaded_embeddings and st.session_state.doc_id: | |
| play_old_messages() | |
| with left_column: | |
| if st.session_state['binary']: | |
| pdf_viewer(input=st.session_state['binary'], width=600, height=800, annotations=st.session_state['annotations']) | |