Update app.py

app.py

@@ -6,7 +6,7 @@ import streamlit as st
 from transformers import pipeline, AutoModelForMaskedLM, AutoTokenizer
 
 
-st.title("Completamento del testo in Latino con Latin BERT")
+st.title("Completamento di parole in testi Latino Antico")
 st.write("Inserisci un testo con il token [MASK] per vedere le previsioni del modello.")
 
 
@@ -15,9 +15,6 @@ st.write("Asdrubal, frater Annibalis, qui secundo Punico bello [MASK] ingentibus
 st.write("hanno et mago qui [MASK]  punico bello cornelium consulem aput liparas ceperunt > primo");
 st.write("Lorem ipsum dolor sit amet, [MASK] adipiscing elit. > consectetur");
 st.write("Populus Romanus cum Macedonibus [MASK] ter gessit => bellum");
-#Asdrubal, frater Annibalis, qui secundo Punico bello [MASK] ingentibus copiis ab Hispania veniens => cum
-#hanno et mago qui [MASK]  punico bello cornelium consulem aput liparas ceperunt => primo
-#Lorem ipsum dolor sit amet, [MASK] adipiscing elit. => consectetur
 input_text = st.text_input("Testo:", value="Lorem ipsum dolor sit amet, [MASK] adipiscing elit.")
 
 # Model based on BERT
@@ -25,39 +22,20 @@ input_text = st.text_input("Testo:", value="Lorem ipsum dolor sit amet, [MASK] a
 #Hugging face LuisAVasquez/simple-latin-bert-uncased
 #modelname_lv = "LuisAVasquez/simple-latin-bert-uncased"
 #https://github.com/dbamman/latin-bert
-modelname = "./models/bert-base-latin-uncased"
 
 #ClassCat/roberta-base-latin-v2
-tokenizer_robertaclasscat = AutoTokenizer.from_pretrained("Cicciokr/xlm-roberta-latin")
-model_robertaclasscat = AutoModelForMaskedLM.from_pretrained("Cicciokr/xlm-roberta-latin")
-fill_mask_robertaclasscat = pipeline("fill-mask", model=model_robertaclasscat, tokenizer=tokenizer_robertaclasscat)
-
-
-
-
-tokenizer_robertapstroe = AutoTokenizer.from_pretrained("pstroe/roberta-base-latin-cased")
-model_robertapstroe = AutoModelForMaskedLM.from_pretrained("pstroe/roberta-base-latin-cased")
-fill_mask_robertapstroe = pipeline("fill-mask", model=model_robertapstroe, tokenizer=tokenizer_robertapstroe)
-
-tokenizer = AutoTokenizer.from_pretrained(modelname)
-model = AutoModelForMaskedLM.from_pretrained(modelname)
-fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
-
+tokenizer_roberta = AutoTokenizer.from_pretrained("Cicciokr/Roberta-Base-Latin-Uncased")
+model_roberta = AutoModelForMaskedLM.from_pretrained("Cicciokr/Roberta-Base-Latin-Uncased")
+fill_mask_roberta = pipeline("fill-mask", model=model_robertaclasscat, tokenizer=tokenizer_robertaclasscat)
 
 
 if input_text:
     predictions = fill_mask(input_text)
-    st.subheader("Risultati delle previsioni con Bert:")
-    for pred in predictions:
-        st.write(f"**Parola**: {pred['token_str']}, **Probabilità**: {pred['score']:.4f}, **Sequence**: {pred['sequence']}")
+    
     input_text_roberta = input_text.replace("[MASK]", "<mask>")
-    predictions_robertaclasscat = fill_mask_robertaclasscat(input_text_roberta)    
-    st.subheader("Risultati delle previsioni con Roberta ClassCat:")
-    for pred_robertaclasscat in predictions_robertaclasscat:
-        st.write(f"**Parola**: {pred_robertaclasscat['token_str']}, **Probabilità**: {pred_robertaclasscat['score']:.4f}, **Sequence**: {pred_robertaclasscat['sequence']}")
-    predictions_robertapstroe = fill_mask_robertapstroe(input_text_roberta)    
-    st.subheader("Risultati delle previsioni con Roberta Pstroe:")
-    for pred_robertapstroe in predictions_robertapstroe:
-        st.write(f"**Parola**: {pred_robertapstroe['token_str']}, **Probabilità**: {pred_robertapstroe['score']:.4f}, **Sequence**: {pred_robertapstroe['sequence']}")
+    predictions_roberta = fill_mask_roberta(input_text_roberta)    
+    st.subheader("Risultati delle previsioni:")
+    for pred_roberta in predictions_roberta:
+        st.write(f"**Parola**: {pred_roberta['token_str']}, **Probabilità**: {pred_roberta['score']:.4f}, **Sequence**: {pred_roberta['sequence']}")