app.py

import streamlit as st
from annotated_text import annotated_text
from refined.inference.processor import Refined
import requests
import json
import spacy

# Load German model
nlp_model_de = spacy.load("de_core_news_sm")
nlp_model_de.add_pipe("entityfishing", config={"language": "de"})

# 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", "German"}
selected_language = st.sidebar.selectbox("Select the Language", list(language_options))

# Initiate the model
model_options = {"aida_model", "wikipedia_model_with_numbers"}
selected_model_name = st.sidebar.selectbox("Select the Model", list(model_options))

# Select entity_set
entity_set_options = {"wikidata", "wikipedia"}
selected_entity_set = st.sidebar.selectbox("Select the Entity Set", list(entity_set_options))

@st.cache_resource  # 👈 Add the caching decorator
def load_model(model_name, entity_set):
    # Load the pretrained model
    refined_model = Refined.from_pretrained(model_name=model_name, entity_set=entity_set)
    return refined_model

# Use the cached model
refined_model = load_model(selected_model_name, selected_entity_set)

# Addi citation
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(citation)

# 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:
        response = requests.get(f'https://api.wordlift.io/id/{entity_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')

if text_input:
    if selected_language == "German":
        doc_de = nlp_model_de(text_input)
        entities = [(ent.text, ent.label_, ent._.kb_qid, ent._.url_wikidata) for ent in doc_de.ents]
        # You will have to adjust the rest of the code since the format is different
        # For the demo, we'll simply print them for now
        for entity in entities:
            st.write(entity)

    else:
        entities = refined_model.process_text(text_input)
    
        entities = refined_model.process_text(text_input)
    
        entities_map = {}
        entities_data = {}
        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():
                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]
                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