Create app.py

app.py

@@ -0,0 +1,35 @@
+import streamlit as st
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("moussaKam/AraBART", padding= True, truncation=True, max_length=128)
+@st.cache
+def load_model(model_name):
+    model = AutoModelForMaskedLM.from_pretrained(model_name)
+    return model
+model = load_model("moussaKam/AraBART")
+
+
+@st.cache
+def next_word(text, pipe):
+    res_dict= {  
+  'token_str':[],
+  'score':[],
+  }
+    res=pipe(text)
+    for e in res:
+        res_dict['token_str'].extend(e['token_str'])
+        res_dict['score'].extend(e['score'])
+    return res_dict
+
+st.title("Predict Next Word")
+st.write("Use our model to expand your query based on the DB content")
+default_value = "التاريخ هو تحليل و"
+# sent is the the variable holding the user's input
+sent = st.text_area("Text", default_value, height = 60)
+sent += ' <mask>'
+
+pipe = pipeline("fill-mask", tokenizer = tokenizer, model = model, device=0)
+dict_next_words = next_word(sent, pipe)
+
+st.write(dict_next_words)