Spaces:
Sleeping
Sleeping
File size: 15,358 Bytes
e8ebf39 5b25803 e8ebf39 5b25803 844c34d 99f35d8 452072e e8ebf39 9997b7b 844c34d e8ebf39 0b28b48 5b25803 e8ebf39 88c1cba e8ebf39 452ec4c e8ebf39 88c1cba e8ebf39 88c1cba 0f074cc ae04b9d 99f35d8 9c4d6ae 9997b7b 9c4d6ae e8ebf39 0f074cc 99f35d8 e8ebf39 9c4d6ae 88c1cba 9997b7b 844c34d 9997b7b 844c34d b2f5314 844c34d b2f5314 63bcda0 7cdc620 41803fb 88c1cba 137e5e2 844c34d e8ebf39 ae04b9d 5b25803 ae04b9d 5b25803 ae04b9d 5b25803 e8ebf39 ae04b9d e8ebf39 5b25803 e8ebf39 0f074cc 452ec4c e8ebf39 88c1cba 452ec4c 0f074cc 63bcda0 c5c0bc4 88c1cba 63bcda0 88c1cba 0f074cc 88c1cba 9c4d6ae 99f35d8 9c4d6ae 63bcda0 0f074cc 9997b7b 0f074cc 88c1cba 844c34d 452ec4c 9c4d6ae 9997b7b 9c4d6ae 88c1cba 9997b7b 0f074cc 9997b7b 0f074cc 844c34d 9c4d6ae 9997b7b 88c1cba e8ebf39 99f35d8 9997b7b 182ca2f e8ebf39 9997b7b 9c4d6ae 88c1cba 0f074cc 88c1cba e8ebf39 88c1cba 6915a03 88c1cba 0f074cc 88c1cba e8ebf39 844c34d 7cdc620 9c4d6ae e8ebf39 f36264a ae04b9d e8ebf39 844c34d 182ca2f e8ebf39 844c34d 182ca2f e8ebf39 452ec4c e8ebf39 88c1cba 60c4caf e8ebf39 5b25803 e8ebf39 5b25803 e8ebf39 452ec4c e8ebf39 6551eca e8ebf39 6551eca 88c1cba e8ebf39 6551eca 88c1cba 99f35d8 e8ebf39 88c1cba e8ebf39 99f35d8 b19b313 99f35d8 e8ebf39 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 |
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
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
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'] = ConversationBufferWindowMemory(k=4)
st.set_page_config(
page_title="Scientific Document Insights Q/A",
page_icon="📝",
initial_sidebar_state="expanded",
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."
}
)
def new_file():
st.session_state['loaded_embeddings'] = None
st.session_state['doc_id'] = None
st.session_state['uploaded'] = True
st.session_state['memory'].clear()
def clear_memory():
st.session_state['memory'].clear()
# @st.cache_resource
def init_qa(model, api_key=None):
if model == 'chatgpt-3.5-turbo':
if api_key:
chat = ChatOpenAI(model_name="gpt-3.5-turbo",
temperature=0,
openai_api_key=api_key,
frequency_penalty=0.1)
embeddings = OpenAIEmbeddings(openai_api_key=api_key)
else:
chat = ChatOpenAI(model_name="gpt-3.5-turbo",
temperature=0,
frequency_penalty=0.1)
embeddings = OpenAIEmbeddings()
elif model == 'mistral-7b-instruct-v0.1':
chat = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.1",
model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048})
embeddings = HuggingFaceEmbeddings(
model_name="all-MiniLM-L6-v2")
elif model == 'zephyr-7b-beta':
chat = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta",
model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048})
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
else:
st.error("The model was not loaded properly. Try reloading. ")
st.stop()
return
return DocumentQAEngine(chat, embeddings, grobid_url=os.environ['GROBID_URL'])
@st.cache_resource
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.radio(
"Model",
("chatgpt-3.5-turbo", "mistral-7b-instruct-v0.1", "zephyr-7b-beta"),
index=2,
captions=[
"ChatGPT 3.5 Turbo + Ada-002-text (embeddings)",
"Mistral-7B-Instruct-V0.1 + Sentence BERT (embeddings) :free:",
"Zephyr-7B-beta + Sentence BERT (embeddings) :free:"
],
help="Select the LLM model and embeddings you want to use.",
disabled=st.session_state['doc_id'] is not None or st.session_state['uploaded'])
st.markdown(
":warning: Mistral and Zephyr are **FREE** to use. Requests might fail anytime. Use at your own risk. :warning: ")
if (model == 'mistral-7b-instruct-v0.1' or model == 'zephyr-7b-beta') 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 == 'chatgpt-3.5-turbo' 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.',
on_click=clear_memory(),
help="Clear the conversational memory. Currently implemented to retrain the 4 most recent messages.")
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", 100, 2000, value=250,
help="Size of chunks in which the document is partitioned",
disabled=uploaded_file is not None)
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("Named Entities Recognition (NER) processing on LLM response")
st.markdown(
'**NER on LLM responses**: The responses from the LLMs are post-processed 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 st.spinner('Reading file, calling Grobid, and creating memory embeddings...'):
binary = uploaded_file.getvalue()
tmp_file = NamedTemporaryFile()
tmp_file.write(bytearray(binary))
# hash = get_file_hash(tmp_file.name)[:10]
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()
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 = st.session_state['rqa'][model].query_document(question, st.session_state.doc_id,
context_size=context_size,
memory=st.session_state.memory)
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})
for id in range(0, len(st.session_state.messages), 2):
question = st.session_state.messages[id]['content']
if len(st.session_state.messages) > id + 1:
answer = st.session_state.messages[id + 1]['content']
st.session_state.memory.save_context({"input": question}, {"output": answer})
elif st.session_state.loaded_embeddings and st.session_state.doc_id:
play_old_messages()
|