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from operator import index
import streamlit as st
import logging
import os
from annotated_text import annotation
from json import JSONDecodeError
from markdown import markdown
from utils.config import parser
from utils.haystack import start_document_store, query, initialize_pipeline
from utils.ui import reset_results, set_initial_state
import pandas as pd
try:
args = parser.parse_args()
document_store = start_document_store(type=args.store)
st.set_page_config(
page_title="test",
layout="centered",
page_icon = (":shark:"),
menu_items={
'Get Help': 'https://www.extremelycoolapp.com/help',
'Report a bug': "https://www.extremelycoolapp.com/bug",
'About': "# This is a header. This is an *extremely* cool app!"
})
st.sidebar.image("ml_logo.png", use_column_width=True)
# Sidebar for Task Selection
st.sidebar.header('Options:')
task_selection = st.sidebar.radio('Select the task:', ['Extractive', 'Generative'])
pipeline_rag = initialize_pipeline("rag", document_store)
pipeline_extractive = initialize_pipeline("extractive", document_store)
set_initial_state()
st.write('# ' + args.name)
if "question" not in st.session_state:
st.session_state.question = ""
# Search bar
question = st.text_input("", value=st.session_state.question, max_chars=100, on_change=reset_results)
run_pressed = st.button("Run")
run_query = (
run_pressed or question != st.session_state.question #or task_selection != st.session_state.task
)
# Get results for query
if run_query and question:
if task_selection == 'Extractive':
reset_results()
st.session_state.question = question
with st.spinner("🔎 Running your pipeline"):
try:
st.session_state.results_extractive = query(pipeline_extractive, question)
st.session_state.task = task_selection
except JSONDecodeError as je:
st.error(
"👓 An error occurred reading the results. Is the document store working?"
)
except Exception as e:
logging.exception(e)
st.error("🐞 An error occurred during the request.")
elif task_selection == 'Generative':
reset_results()
st.session_state.question = question
with st.spinner("🔎 Running your pipeline"):
try:
st.session_state.results_generative = query(pipeline_rag, question)
st.session_state.task = task_selection
except JSONDecodeError as je:
st.error(
"👓 An error occurred reading the results. Is the document store working?"
)
except Exception as e:
logging.exception(e)
st.error("🐞 An error occurred during the request.")
# Display results
if (st.session_state.results_extractive or st.session_state.results_generative) and run_query:
# Handle Extractive Answers
if task_selection == 'Extractive':
results = st.session_state.results_extractive
st.subheader("Extracted Answers:")
if 'answers' in results:
answers = results['answers']
treshold = 0.2
higher_then_treshold = any(ans.score > treshold for ans in answers)
if not higher_then_treshold:
st.markdown(f"<span style='color:red'>Please note none of the answers achieved a score higher then {int(treshold) * 100}%. Which probably means that the desired answer is not in the searched documents.</span>", unsafe_allow_html=True)
for count, answer in enumerate(answers):
if answer.answer:
text, context = answer.answer, answer.context
start_idx = context.find(text)
end_idx = start_idx + len(text)
score = round(answer.score, 3)
st.markdown(f"**Answer {count + 1}:**")
st.markdown(
context[:start_idx] + str(annotation(body=text, label=f'SCORE {score}', background='#964448', color='#ffffff')) + context[end_idx:],
unsafe_allow_html=True,
)
else:
st.info(
"🤔 Haystack is unsure whether any of the documents contain an answer to your question. Try to reformulate it!"
)
# Handle Generative Answers
elif task_selection == 'Generative':
results = st.session_state.results_generative
st.subheader("Generated Answer:")
if 'results' in results:
st.markdown("**Answer:**")
st.write(results['results'][0])
# Handle Retrieved Documents
if 'documents' in results:
retrieved_documents = results['documents']
st.subheader("Retriever Results:")
data = []
for i, document in enumerate(retrieved_documents):
# Truncate the content
truncated_content = (document.content[:150] + '...') if len(document.content) > 150 else document.content
data.append([i + 1, document.meta['name'], truncated_content])
# Convert data to DataFrame and display using Streamlit
df = pd.DataFrame(data, columns=['Ranked Context', 'Document Name', 'Content'])
st.table(df)
except SystemExit as e:
os._exit(e.code)
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