feat(main): split into files

shad_mlops_transformers/main.py

@@ -1,8 +1,15 @@
 import streamlit as st
+from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl")
+model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
 
 st.markdown("### Hello there")
 st.markdown("<img width=200px src='https://rozetked.me/images/uploads/dwoilp3BVjlE.jpg'>", unsafe_allow_html=True)
 
 text = st.text_area("TEXT HERE")
 
-st.markdown("hey you")
+raw_predictions = nlp("is this real?")
+
+st.markdown(f"{raw_predictions}")

shad_mlops_transformers/model.py

@@ -0,0 +1,8 @@
+from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl")
+model = AutoModelForTokenClassification.from_pretrained("Davlan/distilbert-base-multilingual-cased-ner-hrl")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
+ner_results = nlp(example)
+print(ner_results)