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import streamlit as st
from PIL import Image
from .constants import (QUERIES, PLAIN_GPT_ANS, GPT_WEB_RET_AUG_ANS, GPT_LOCAL_RET_AUG_ANS,
BUTTON_LOCAL_RET_AUG, BUTTON_WEB_RET_AUG)
def set_question():
st.session_state['query'] = st.session_state['q_drop_down']
def set_q1():
st.session_state['query'] = QUERIES[0]
def set_q2():
st.session_state['query'] = QUERIES[1]
def set_q3():
st.session_state['query'] = QUERIES[2]
def set_q4():
st.session_state['query'] = QUERIES[3]
def set_q5():
st.session_state['query'] = QUERIES[4]
def main_column():
placeholder = st.empty()
with placeholder:
search_bar, button = st.columns([3, 1])
with search_bar:
_ = st.text_area(f" ", max_chars=200, key='query')
with button:
st.write(" ")
st.write(" ")
run_pressed = st.button("Run", key="run")
st.write(" ")
st.radio("Answer Type:", (BUTTON_LOCAL_RET_AUG, BUTTON_WEB_RET_AUG), key="query_type")
st.markdown(f"<h5>{PLAIN_GPT_ANS}</h5>", unsafe_allow_html=True)
placeholder_plain_gpt = st.empty()
placeholder_plain_gpt.text_area(f" ", placeholder="The answer will appear here.", disabled=True,
key=PLAIN_GPT_ANS, height=1, label_visibility='collapsed')
if st.session_state.get("query_type", BUTTON_LOCAL_RET_AUG) == BUTTON_LOCAL_RET_AUG:
st.markdown(f"<h5>{GPT_LOCAL_RET_AUG_ANS}</h5>", unsafe_allow_html=True)
else:
st.markdown(f"<h5>{GPT_WEB_RET_AUG_ANS}</h5>", unsafe_allow_html=True)
placeholder_retrieval_augmented = st.empty()
placeholder_retrieval_augmented.text_area(f" ", placeholder="The answer will appear here.", disabled=True,
key=GPT_LOCAL_RET_AUG_ANS, height=1, label_visibility='collapsed')
return run_pressed, placeholder_plain_gpt, placeholder_retrieval_augmented
def right_sidebar():
st.write("")
st.write("")
st.markdown("<h5> Example questions </h5>", unsafe_allow_html=True)
st.button(QUERIES[0], on_click=set_q1, use_container_width=True)
st.button(QUERIES[1], on_click=set_q2, use_container_width=True)
st.button(QUERIES[2], on_click=set_q3, use_container_width=True)
st.button(QUERIES[3], on_click=set_q4, use_container_width=True)
st.button(QUERIES[4], on_click=set_q5, use_container_width=True)
def left_sidebar():
with st.sidebar:
image = Image.open('logo/haystack-logo-colored.png')
st.markdown("Thanks for coming to this :hugging_face: space. \n\n"
"This is an effort towards showcasing how you can use Haystack for Retrieval Augmented QA, "
"with local [FAISSDocumentStore](https://docs.haystack.deepset.ai/reference/document-store-api#faissdocumentstore)"
" or a [WebRetriever](https://docs.haystack.deepset.ai/docs/retriever#retrieval-from-the-web). \n\n"
"More information on how this was built and instructions along "
"with a repository will be published soon and updated here.")
# st.markdown(
# "## How to use\n"
# "1. Enter your [OpenAI API key](https://platform.openai.com/account/api-keys) below\n"
# "2. Enter a Serper Dev API key\n"
# "3. Enjoy 🤗\n"
# )
# api_key_input = st.text_input(
# "OpenAI API Key",
# type="password",
# placeholder="Paste your OpenAI API key here (sk-...)",
# help="You can get your API key from https://platform.openai.com/account/api-keys.",
# value=st.session_state.get("OPENAI_API_KEY", ""),
# )
# if api_key_input:
# set_openai_api_key(api_key_input)
st.markdown("---")
st.markdown(
"## How this works\n"
"This app was built with [Haystack](https://haystack.deepset.ai) using the"
" [PromptNode](https://docs.haystack.deepset.ai/docs/prompt_node), "
"[Retriever](https://docs.haystack.deepset.ai/docs/retriever#embedding-retrieval-recommended),"
"and [FAISSDocumentStore](https://docs.haystack.deepset.ai/reference/document-store-api#faissdocumentstore).\n\n"
" You can find the source code in **Files and versions** tab."
)
st.markdown("---")
st.image(image, width=250)
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