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