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#import streamlit as st
#x = st.slider('Select a value')
#st.write(x, 'squared is', x * x)
import streamlit as st
from transformers import pipeline, AutoModelForMaskedLM, AutoTokenizer
st.title("Completamento del testo in Latino con Latin BERT")
st.write("Inserisci un testo con il token [MASK] per vedere le previsioni del modello.")
#dvces et reges carthaginiensivm hanno et mago qui [MASK] punico bello cornelium consulem aput liparas ceperunt
input_text = st.text_input("Testo:", value="Lorem ipsum dolor sit amet, [MASK] adipiscing elit.")
#modelname = "./models/latin_bert/"
modelname = "LuisAVasquez/simple-latin-bert-uncased"
tokenizer = AutoTokenizer.from_pretrained(modelname)
model = AutoModelForMaskedLM.from_pretrained(modelname)
# Creare un pipeline di riempimento maschere
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
#fill_mask = pipeline("fill-mask", model=modelname)
if input_text:
predictions = fill_mask(input_text)
st.subheader("Risultati delle previsioni con Simple Latin Bert:")
for pred in predictions:
st.write(f"**Parola**: {pred['token_str']}, **Probabilità**: {pred['score']:.4f}, **Sequence**: {pred['sequence']}")
st.subheader("Risultati delle previsioni con Latin Bert:")
path_to_latin_bert = "./models/latin_bert/"
unmasker = pipeline('fill-mask', model=path_to_latin_bert)
result = unmasker(input_text)
for pred_due in result:
st.write(f"**Parola**: {pred_due['token_str']}, **Probabilità**: {pred_due['score']:.4f}, **Sequence**: {pred_due['sequence']}")
st.subheader("Risultati delle previsioni con bert-base-latin-uncased:")
path_to_bert_base = "./models/bert-base-latin-uncased/"
unmasker_bert_base = pipeline('fill-mask', model=path_to_bert_base)
result_bert_base = unmasker_bert_base(input_text)
for pred_tre in result_bert_base:
st.write(f"**Parola**: {pred_tre['token_str']}, **Probabilità**: {pred_tre['score']:.4f}, **Sequence**: {pred_tre['sequence']}")
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