document-qa / streamlit_app.py
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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
from document_qa.ner_client_generic import NERClientGeneric
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, DataStorage
from document_qa.grobid_processors import GrobidAggregationProcessor, decorate_text_with_annotations
OPENAI_MODELS = ['gpt-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 'should_show_annotations' not in st.session_state:
st.session_state['should_show_annotations'] = True
if 'pdf' not in st.session_state:
st.session_state['pdf'] = None
if 'pdf_rendering' not in st.session_state:
st.session_state['pdf_rendering'] = None
st.set_page_config(
page_title="Scientific Document Insights Q/A",
page_icon="πŸ“",
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
storage = DataStorage(embeddings)
return DocumentQAEngine(chat, storage, grobid_url=os.environ['GROBID_URL'], memory=st.session_state['memory'])
@st.cache_resource
def init_ner():
quantities_client = QuantitiesAPI(os.environ['GROBID_QUANTITIES_URL'], check_server=True)
materials_client = NERClientGeneric(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(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=(OPENAI_MODELS + list(OPEN_MODELS.keys())).index(
"zephyr-7b-beta") if "DEFAULT_MODEL" not in os.environ or not os.environ["DEFAULT_MODEL"] else (
OPENAI_MODELS + list(OPEN_MODELS.keys())).index(os.environ["DEFAULT_MODEL"]),
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("πŸ“ 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."
)
# Add a checkbox for showing annotations
# st.session_state['show_annotations'] = st.checkbox("Show annotations", value=True)
# st.session_state['should_show_annotations'] = st.checkbox("Show annotations", value=True)
chunk_size = st.slider("Text chunks size", -1, 2000, value=-1,
help="Size of chunks in which split the document. -1: use paragraphs, > 0 paragraphs are aggregated.",
disabled=uploaded_file is not None)
if chunk_size == -1:
context_size = st.slider("Context size (paragraphs)", 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 (chunks)", 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.session_state['pdf_rendering'] = st.radio(
"PDF rendering mode",
("unwrap", "legacy_embed"),
index=0,
disabled=not uploaded_file,
help="PDF rendering engine."
"Note: The Legacy PDF viewer does not support annotations and might not work on Chrome.",
format_func=lambda q: "Legacy PDF Viewer" if q == "legacy_embed" else "Streamlit PDF Viewer (Pdf.js)"
)
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)
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, coordinates = 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([cs for cs in c.split(",")]) for c in coord_doc]
for coord_doc in coordinates]
gradients = generate_color_gradient(len(annotations))
for i, color in enumerate(gradients):
for annotation in annotations[i]:
annotation['color'] = color
st.session_state['annotations'] = [annotation for annotation_doc in annotations for annotation in
annotation_doc]
if not text_response:
st.error("Something went wrong. Contact Luca Foppiano (Foppiano.Luca@nims.co.jp) 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})
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,
annotation_outline_size=1,
annotations=st.session_state['annotations'],
rendering=st.session_state['pdf_rendering']
)