import streamlit as st import streamlit.components.v1 as components import rebel import wikipedia from utils import clip_text from datetime import datetime as dt import os MAX_TOPICS = 3 wiki_state_variables = { 'has_run_wiki': False, 'wiki_suggestions': [], 'wiki_text': [], 'nodes': [], "topics": [], "html_wiki": "" } free_text_state_variables = { 'has_run_free': False, "html_free": "" } BUTTON_COLUMS = 4 def wiki_init_state_variables(): for k in free_text_state_variables.keys(): if k in st.session_state: del st.session_state[k] for k, v in wiki_state_variables.items(): if k not in st.session_state: st.session_state[k] = v def wiki_generate_graph(): st.session_state["GRAPH_FILENAME"] = str( dt.now().timestamp()*1000) + ".html" if 'wiki_text' not in st.session_state: return if len(st.session_state['wiki_text']) == 0: st.error("please enter a topic and select a wiki page first") return with st.spinner(text="Generating graph..."): texts = st.session_state['wiki_text'] st.session_state['nodes'] = [] nodes = rebel.generate_knowledge_graph( texts, st.session_state["GRAPH_FILENAME"]) HtmlFile = open( st.session_state["GRAPH_FILENAME"], 'r', encoding='utf-8') source_code = HtmlFile.read() st.session_state["html_wiki"] = source_code os.remove(st.session_state["GRAPH_FILENAME"]) for n in nodes: n = n.lower() if n not in st.session_state['topics']: possible_topics = wikipedia.search(n, results=2) st.session_state['nodes'].extend(possible_topics) st.session_state['nodes'] = list(set(st.session_state['nodes'])) st.session_state['has_run_wiki'] = True st.success('Done!') def wiki_show_suggestion(): st.session_state['wiki_suggestions'] = [] with st.spinner(text="fetching wiki topics..."): if st.session_state['input_method'] == "wikipedia": text = st.session_state.text if (text is not None) and (text != ""): subjects = text.split(",")[:MAX_TOPICS] for subj in subjects: st.session_state['wiki_suggestions'] += wikipedia.search( subj, results=3) def wiki_show_text(page_title): with st.spinner(text="fetching wiki page..."): try: page = wikipedia.page(title=page_title, auto_suggest=False) st.session_state['wiki_text'].append(clip_text(page.summary)) st.session_state['topics'].append(page_title.lower()) st.session_state['wiki_suggestions'].remove(page_title) except wikipedia.DisambiguationError as e: with st.spinner(text="Woops, ambigious term, recalculating options..."): st.session_state['wiki_suggestions'].remove(page_title) temp = st.session_state['wiki_suggestions'] + e.options[:3] st.session_state['wiki_suggestions'] = list(set(temp)) except wikipedia.WikipediaException: st.session_state['wiki_suggestions'].remove(page_title) def wiki_add_text(term): if len(st.session_state['wiki_text']) > MAX_TOPICS: return try: page = wikipedia.page(title=term, auto_suggest=False) extra_text = clip_text(page.summary) st.session_state['wiki_text'].append(extra_text) st.session_state['topics'].append(term.lower()) st.session_state['nodes'].remove(term) except wikipedia.DisambiguationError as e: with st.spinner(text="Woops, ambigious term, recalculating options..."): st.session_state['nodes'].remove(term) temp = st.session_state['nodes'] + e.options[:3] st.session_state['nodes'] = list(set(temp)) except wikipedia.WikipediaException as e: st.session_state['nodes'].remove(term) def wiki_reset_session(): for k in wiki_state_variables: del st.session_state[k] def free_reset_session(): for k in free_text_state_variables: del st.session_state[k] def free_text_generate(): st.session_state["GRAPH_FILENAME"] = str( dt.now().timestamp()*1000) + ".html" text = st.session_state['free_text'][0:100] rebel.generate_knowledge_graph([text], st.session_state["GRAPH_FILENAME"]) HtmlFile = open(st.session_state["GRAPH_FILENAME"], 'r', encoding='utf-8') source_code = HtmlFile.read() st.session_state["html_free"] = source_code os.remove(st.session_state["GRAPH_FILENAME"]) st.session_state['has_run_free'] = True def free_text_layout(): st.text_area("Free text", key="free_text", height=5, value="Tardigrades, known colloquially as water bears or moss piglets, are a phylum of eight-legged segmented micro-animals.") st.button("Generate", on_click=free_text_generate, key="free_text_generate") def free_test_init_state_variables(): for k in wiki_state_variables.keys(): if k in st.session_state: del st.session_state[k] for k, v in free_text_state_variables.items(): if k not in st.session_state: st.session_state[k] = v st.title('RE:Belle') st.markdown( """ ### Building Beautiful Knowledge Graphs With REBEL """) st.selectbox( 'input method', ('wikipedia', 'free text'), key="input_method") def show_wiki_hub_page(): st.sidebar.button("Reset", on_click=wiki_reset_session, key="reset_key") st.sidebar.markdown( """ ## How To Create a Graph: - Enter wikipedia search terms, separated by comma's - Choose one or more of the suggested topics (max 3) - Click generate! """ ) cols = st.columns([8, 1]) with cols[0]: st.text_input("wikipedia search term", on_change=wiki_show_suggestion, key="text", value="graphs, are, awesome") with cols[1]: st.text('') st.text('') st.button("Search", on_click=wiki_show_suggestion, key="show_suggestion_key") if len(st.session_state['wiki_suggestions']) != 0: num_buttons = len(st.session_state['wiki_suggestions']) num_cols = num_buttons if 0 < num_buttons < BUTTON_COLUMS else BUTTON_COLUMS columns = st.columns([1] * num_cols) for q in range(1 + num_buttons//num_cols): for i, (c, s) in enumerate(zip(columns, st.session_state['wiki_suggestions'][q*num_cols: (q+1)*num_cols])): with c: st.button(s, on_click=wiki_show_text, args=( s,), key=str(i)+s+"wiki_suggestion") if len(st.session_state['wiki_text']) != 0: for i, t in enumerate(st.session_state['wiki_text']): new_expander = st.expander(label=t[:30] + "...", expanded=(i == 0)) with new_expander: st.markdown(t) if len(st.session_state['wiki_text']) > 0: st.button("Generate", on_click=wiki_generate_graph, key="gen_graph") st.sidebar.markdown( """ ## How to expand the graph - Click a button below the graph to expand that node (Only nodes that have wiki pages will be expanded) - Hit the Generate button again to expand your graph! """ ) if st.session_state['has_run_wiki']: components.html(st.session_state["html_wiki"], width=720, height=600) num_buttons = len(st.session_state["nodes"]) num_cols = num_buttons if 0 < num_buttons < BUTTON_COLUMS else BUTTON_COLUMS columns = st.columns([1] * num_cols + [1]) for q in range(1 + num_buttons//num_cols): for i, (c, s) in enumerate(zip(columns, st.session_state["nodes"][q*num_cols: (q+1)*num_cols])): with c: st.button(s, on_click=wiki_add_text, args=(s,), key=str(i)+s) def show_free_text_hub_page(): st.sidebar.button("Reset", on_click=free_reset_session, key="free_reset_key") st.sidebar.markdown( """ ## How To Create a Graph: - Enter a text you'd like to see as a graph. - Click generate! """ ) free_text_layout() if st.session_state['has_run_free']: components.html(st.session_state["html_free"], width=720, height=600) if st.session_state['input_method'] == "wikipedia": wiki_init_state_variables() show_wiki_hub_page() else: free_test_init_state_variables() show_free_text_hub_page() st.sidebar.markdown( """ ## What This Is And Why We Built it This space shows how a transformer network can be used to convert *human* text into a computer-queryable format: a **knowledge graph**. Knowledge graphs are graphs where each node (or *vertex* if you're fancy) represent a concept/person/thing and each edge the link between those concepts. If you'd like to know more, you can read [this blogpost](https://www.ml6.eu/knowhow/knowledge-graphs-an-introduction-and-business-applications). Knowledge graphs aren't just cool to look at, they are an extremely versatile way of storing data, and are used in machine learning to perform tasks like fraud detection. You can read more about the applications of knowledge graphs in ML in [this blogpost](https://blog.ml6.eu/how-are-knowledge-graphs-and-machine-learning-related-ff6f5c1760b5). There is one problem though: building knowledge graphs from scratch is a time-consuming and tedious task, so it would be a lot easier if we could leverage machine learning to **create** them from existing texts. This demo shows how a model named **REBEL** has been trained to do just that: it reads summaries from Wikipedia (or any other text you input), and generates a graph containing the information it distills from the text. """ ) st.sidebar.markdown( """ *Credits for the REBEL model go out to Pere-Lluís Huguet Cabot and Roberto Navigli. The code can be found [here](https://github.com/Babelscape/rebel), and the original paper [here](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf)* """ )