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| """ | |
| # My first app | |
| Here's our first attempt at using data to create a table: | |
| """ | |
| import streamlit as st | |
| from retriever import do_search, dutch_datset_name, german_datset_name | |
| def local_css(file_name): | |
| with open(file_name) as f: | |
| st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True) | |
| def render_retrieved_content(content, score): | |
| if score is not None and score == 0.0: | |
| return f'<blockquote> No result </blockquote>' | |
| if score is not None: | |
| score = round(score, 3) | |
| print_score = f'<b> Similarity Score: {score}</b>' | |
| return f'<blockquote> {content} </blockquote> {print_score}' | |
| local_css('style.css') | |
| st.header('🧐 Where my docs at?') | |
| st.markdown('✨ Imagine you have a bunch of text documents and looking for one specific passage, ' | |
| 'but you can not remember on the exact words. Just about rough content. <br><br>' | |
| '💡 This demo compares different search approaches that can help you to find the right ' | |
| 'information.', unsafe_allow_html=True) | |
| with st.form('search-input'): | |
| option = st.selectbox( | |
| 'Choose a dataset', | |
| (german_datset_name, dutch_datset_name)) | |
| search = st.text_input('Enter your search query') | |
| button = st.form_submit_button('Search') | |
| if search: | |
| result = do_search(search, option) | |
| st.markdown('### 🔎 Term Frequency–Inverse Document Frequency (TF-IDF)') | |
| st.markdown('Is a statistical approach that calculates how relevant a word is to a document ' | |
| 'in your collection. Only documents will be found that contain one of the words of ' | |
| 'the given search query. You still have to remember exact terms that are in the ' | |
| 'searched phrase.') | |
| st.markdown(render_retrieved_content(result[0].content, result[0].score), | |
| unsafe_allow_html=True) | |
| st.markdown('### 🧠 Semantic Search') | |
| st.markdown('An alternative approach is semantic search. Instead of using words of the' | |
| 'documents to calculate the score, we use a neural network which calculates ' | |
| 'sentence embeddings. Sentences and documents that are similar will be close to ' | |
| 'each other in the embedding space. We use this behavior to find topic related ' | |
| 'documents without knowing the exact terms. If you want learn more about this ' | |
| 'topic check out one of our recent <a ' | |
| 'href="https://blog.ml6.eu/decoding-sentence-encoders-37e63244ae00?source=collection_detail----1e091bbd5262-----2-----------------------">blogposts</a>.', | |
| unsafe_allow_html=True) | |
| st.markdown(render_retrieved_content(result[1].content, result[1].score), | |
| unsafe_allow_html=True) | |
| st.markdown('### 🚀 Domain Adapted Semantic Search') | |
| st.markdown('If our document collection contains a lot of domain-specific documents, ' | |
| 'we can not use standard models. These models were trained on a large amount of ' | |
| 'publicly available data, which probably not covers your domain-specific words. To ' | |
| 'improve the search results, we could fine-tune the network to calculate more ' | |
| 'accurate similarities between queries and document regarding to your domain.') | |
| st.markdown(render_retrieved_content(result[2].content, result[2].score), | |
| unsafe_allow_html=True) | |