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| import streamlit as st | |
| from annotated_text import annotated_text | |
| from refined.inference.processor import Refined | |
| import requests | |
| import json | |
| import spacy | |
| # Page config | |
| st.set_page_config( | |
| page_title="Entity Linking by WordLift", | |
| page_icon="fav-ico.png", | |
| layout="wide", | |
| initial_sidebar_state="collapsed", | |
| menu_items={ | |
| 'Get Help': 'https://wordlift.io/book-a-demo/', | |
| 'About': "# This is a demo app for NEL/NED/NER and SEO" | |
| } | |
| ) | |
| # Sidebar | |
| st.sidebar.image("logo-wordlift.png") | |
| language_options = {"English", "English - spaCy", "German"} | |
| selected_language = st.sidebar.selectbox("Select the Language", list(language_options), index=0) | |
| # Based on selected language, configure model, entity set, and citation options | |
| if selected_language == "German" or selected_language == "English - spaCy": | |
| selected_model_name = None | |
| selected_entity_set = None | |
| entity_fishing_citation = """ | |
| @misc{entity-fishing, | |
| title = {entity-fishing}, | |
| publisher = {GitHub}, | |
| year = {2016--2023}, | |
| archivePrefix = {swh}, | |
| eprint = {1:dir:cb0ba3379413db12b0018b7c3af8d0d2d864139c} | |
| } | |
| """ | |
| with st.sidebar.expander('Citations'): | |
| st.markdown(entity_fishing_citation) | |
| else: | |
| model_options = ["aida_model", "wikipedia_model_with_numbers"] | |
| entity_set_options = ["wikidata", "wikipedia"] | |
| selected_model_name = st.sidebar.selectbox("Select the Model", model_options) | |
| selected_entity_set = st.sidebar.selectbox("Select the Entity Set", entity_set_options) | |
| refined_citation = """ | |
| @inproceedings{ayoola-etal-2022-refined, | |
| title = "{R}e{F}in{ED}: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking", | |
| author = "Tom Ayoola, Shubhi Tyagi, Joseph Fisher, Christos Christodoulopoulos, Andrea Pierleoni", | |
| booktitle = "NAACL", | |
| year = "2022" | |
| } | |
| """ | |
| with st.sidebar.expander('Citations'): | |
| st.markdown(refined_citation) | |
| # 👈 Add the caching decorator | |
| def load_model(selected_language, model_name=None, entity_set=None): | |
| if selected_language == "German": | |
| # Load the German-specific model | |
| nlp_model_de = spacy.load("de_core_news_lg") | |
| nlp_model_de.add_pipe("entityfishing") | |
| return nlp_model_de | |
| elif selected_language == "English - spaCy": | |
| # Load English-specific model | |
| nlp_model_en = spacy.load("en_core_web_sm") | |
| nlp_model_en.add_pipe("entityfishing") | |
| return nlp_model_en | |
| else: | |
| # Load the pretrained model for other languages | |
| refined_model = Refined.from_pretrained(model_name=model_name, entity_set=entity_set) | |
| return refined_model | |
| # Use the cached model | |
| model = load_model(selected_language, selected_model_name, selected_entity_set) | |
| # Helper functions | |
| def get_wikidata_id(entity_string): | |
| entity_list = entity_string.split("=") | |
| entity_id = str(entity_list[1]) | |
| entity_link = "http://www.wikidata.org/entity/" + entity_id | |
| return {"id": entity_id, "link": entity_link} | |
| def get_entity_data(entity_link): | |
| try: | |
| # Format the entity_link | |
| formatted_link = entity_link.replace("http://", "http/") | |
| response = requests.get(f'https://api.wordlift.io/id/{formatted_link}') | |
| return response.json() | |
| except Exception as e: | |
| print(f"Exception when fetching data for entity: {entity_link}. Exception: {e}") | |
| return None | |
| # Create the form | |
| with st.form(key='my_form'): | |
| text_input = st.text_area(label='Enter a sentence') | |
| submit_button = st.form_submit_button(label='Analyze') | |
| # Initialization | |
| entities_map = {} | |
| entities_data = {} | |
| if text_input: | |
| if selected_language in ["German", "English - spaCy"]: | |
| doc = model(text_input) | |
| entities = [(ent.text, ent.label_, ent._.kb_qid, ent._.url_wikidata) for ent in doc.ents] | |
| for entity in entities: | |
| entity_string, entity_type, wikidata_id, wikidata_url = entity | |
| if wikidata_url: | |
| # Ensure correct format for the German and English model | |
| formatted_wikidata_url = wikidata_url.replace("https://www.wikidata.org/wiki/", "http://www.wikidata.org/entity/") | |
| entities_map[entity_string] = {"id": wikidata_id, "link": formatted_wikidata_url} | |
| entity_data = get_entity_data(formatted_wikidata_url) | |
| if entity_data is not None: | |
| entities_data[entity_string] = entity_data | |
| else: | |
| entities = model.process_text(text_input) | |
| for entity in entities: | |
| single_entity_list = str(entity).strip('][').replace("\'", "").split(', ') | |
| if len(single_entity_list) >= 2 and "wikidata" in single_entity_list[1]: | |
| entities_map[single_entity_list[0].strip()] = get_wikidata_id(single_entity_list[1]) | |
| entity_data = get_entity_data(entities_map[single_entity_list[0].strip()]["link"]) | |
| if entity_data is not None: | |
| entities_data[single_entity_list[0].strip()] = entity_data | |
| combined_entity_info_dictionary = dict([(k, [entities_map[k], entities_data[k] if k in entities_data else None]) for k in entities_map]) | |
| if submit_button: | |
| # Prepare a list to hold the final output | |
| final_text = [] | |
| # JSON-LD data | |
| json_ld_data = { | |
| "@context": "https://schema.org", | |
| "@type": "WebPage", | |
| "mentions": [] | |
| } | |
| # Replace each entity in the text with its annotated version | |
| for entity_string, entity_info in entities_map.items(): | |
| # Check if the entity has a valid Wikidata link | |
| if entity_info["link"] is None or entity_info["link"] == "None": | |
| continue # skip this entity | |
| entity_data = entities_data.get(entity_string, None) | |
| entity_type = None | |
| if entity_data is not None: | |
| entity_type = entity_data.get("@type", None) | |
| # Use different colors based on the entity's type | |
| color = "#8ef" # Default color | |
| if entity_type == "Place": | |
| color = "#8AC7DB" | |
| elif entity_type == "Organization": | |
| color = "#ADD8E6" | |
| elif entity_type == "Person": | |
| color = "#67B7D1" | |
| elif entity_type == "Product": | |
| color = "#2ea3f2" | |
| elif entity_type == "CreativeWork": | |
| color = "#00BFFF" | |
| elif entity_type == "Event": | |
| color = "#1E90FF" | |
| entity_annotation = (entity_string, entity_info["id"], color) | |
| text_input = text_input.replace(entity_string, f'{{{str(entity_annotation)}}}', 1) | |
| # Add the entity to JSON-LD data | |
| entity_json_ld = combined_entity_info_dictionary[entity_string][1] | |
| if entity_json_ld and entity_json_ld.get("link") != "None": | |
| json_ld_data["mentions"].append(entity_json_ld) | |
| # Split the modified text_input into a list | |
| text_list = text_input.split("{") | |
| for item in text_list: | |
| if "}" in item: | |
| item_list = item.split("}") | |
| final_text.append(eval(item_list[0])) | |
| if len(item_list[1]) > 0: | |
| final_text.append(item_list[1]) | |
| else: | |
| final_text.append(item) | |
| # Pass the final_text to the annotated_text function | |
| annotated_text(*final_text) | |
| with st.expander("See annotations"): | |
| st.write(combined_entity_info_dictionary) | |
| with st.expander("Here is the final JSON-LD"): | |
| st.json(json_ld_data) # Output JSON-LD |