Spaces:
Runtime error
Runtime error
File size: 3,757 Bytes
a4b8171 cf4955a a4b8171 1d3a9da a4b8171 4fc81f2 a4b8171 8d3589e a4b8171 993e7c9 a4b8171 993e7c9 a4b8171 |
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 |
import streamlit as st
import pandas as pd
from haystack.schema import Answer
from haystack.document_stores import InMemoryDocumentStore
from haystack.pipeline import FAQPipeline
from haystack.retriever.dense import EmbeddingRetriever
import logging
#Haystack function calls - streamlit structure from Tuana GoT QA Haystack demo
@st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True) # use streamlit cache
def start_haystack():
document_store = InMemoryDocumentStore(index="document", embedding_field='embedding', embedding_dim=384, similarity='cosine')
retriever = EmbeddingRetriever(document_store=document_store, embedding_model='sentence-transformers/all-MiniLM-L6-v2', use_gpu=True, top_k=1)
load_data_to_store(document_store,retriever)
pipeline = FAQPipeline(retriever=retriever)
return pipeline
def load_data_to_store(document_store, retriever):
df = pd.read_csv('monopoly_qa-v1.csv')
questions = list(df.Question)
df['embedding'] = retriever.embed_queries(texts=questions)
df = df.rename(columns={"Question":"content","Answer":"answer"})
df.drop('link to source (to prevent duplicate sources)',axis=1, inplace=True)
dicts = df.to_dict(orient="records")
document_store.write_documents(dicts)
pipeline = start_haystack()
# Streamlit App section
def set_state_if_absent(key, value):
if key not in st.session_state:
st.session_state[key] = value
def reset_results(*args):
st.session_state.results = None
set_state_if_absent("question", "how much money should each player have at the beginning?")
set_state_if_absent("results", None)
st.markdown( """
Haystack FAQ Semantic Search Pipeline
""", unsafe_allow_html=True)
question = st.text_input("", value=st.session_state.question, max_chars=100, on_change=reset_results)
def ask_question(question):
prediction = pipeline.run(query=question)
results = []
for answer in prediction["answers"]:
answer = answer.to_dict()
if answer["answer"]:
results.append(
{
"context": "..." + answer["context"] + "...",
"answer": answer["answer"],
"relevance": round(answer["score"] * 100, 2),
"offset_start_in_doc": answer["offsets_in_document"][0]["start"],
}
)
else:
results.append(
{
"context": None,
"answer": None,
"relevance": round(answer["score"] * 100, 2),
}
)
return results
if question:
with st.spinner("👑 Performing semantic search on FAQ Database..."):
try:
msg = 'Asked ' + question
logging.info(msg)
st.session_state.results = ask_question(question)
except Exception as e:
logging.exception(e)
if st.session_state.results:
st.write('## Top Results')
for count, result in enumerate(st.session_state.results):
if result["answer"]:
answer, context = result["answer"], result["context"]
start_idx = context.find(answer)
end_idx = start_idx + len(answer)
st.write(
markdown(context[:start_idx] + str(annotation(body=answer, label="ANSWER", background="#964448", color='#ffffff')) + context[end_idx:]),
unsafe_allow_html=True,
)
st.markdown(f"**Relevance:** {result['relevance']}")
else:
st.info(
"🤔 Haystack is unsure whether any of the documents contain an answer to your question. Try to reformulate it!"
) |