app.py

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

# Sidebar
st.sidebar.image("logo-wordlift.png")

# 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)

# 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')

# Process the text and extract the entities
if 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 = "#007aff"
            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