# Use a pipeline as a high-level helper
from transformers import pipeline
import streamlit as st

# dslim/bert-base-NER
# SIRIS-Lab/affilgood-NER-multilingual
# FacebookAI/xlm-roberta-large-finetuned-conll03-english
#
fintuned_ner_models = ["dslim/bert-base-NER", "SIRIS-Lab/affilgood-NER-multilingual", "FacebookAI/xlm-roberta-large-finetuned-conll03-english"]
def ner_models_result(address, models = fintuned_ner_models):
    ner_result_entities = []
    for model in models:
        pipe = pipeline("ner", model=f"{model}", aggregation_strategy="simple")
        ner_result_entities.append((model, pipe(address)))
    return ner_result_entities
st.title("Basic NER model testing")
affiliation_address = st.text_input("Enter address")
if st.button("Print"):
    ner_results = ner_models_result(address = affiliation_address)
    for result in ner_results:
        st.write("-"*50)
        st.write(f"Model: {result[0]}")
        st.write(f"Result: {result[1]}!")