Update app.py

app.py

@@ -1,17 +1,21 @@
-import streamlit as st
-from transformers import pipeline
+import torch
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering,
 
-classifier = pipeline("translation", model="t5-small")
-def main():
-    st.title("English to German")
+tokenizer = AutoTokenizer.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1")
+model = AutoModelForQuestionAnswering.from_pretrained("valhalla/longformer-base-4096-finetuned-squadv1")
 
-    with st.form("text_field"):
-        text = st.text_area('enter some english word:')
-        # clicked==True only when the button is clicked
-        clicked = st.form_submit_button("Submit")
-        if clicked:
-          results = classifier([text])
-          st.json(results)
+text = "Huggingface has democratized NLP. Huge thanks to Huggingface for this."
+question = "What has Huggingface done ?"
+encoding = tokenizer(question, text, return_tensors="pt")
+input_ids = encoding["input_ids"]
 
-if __name__ == "__main__":
-    main()
+# default is local attention everywhere
+# the forward method will automatically set global attention on question tokens
+attention_mask = encoding["attention_mask"]
+
+start_scores, end_scores = model(input_ids, attention_mask=attention_mask)
+all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
+
+answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1]
+answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
+# output => democratized NLP