Update app.py

app.py

@@ -1,19 +1,17 @@
-from transformers import AutoModelWithLMHead, AutoTokenizer
+import streamlit as st
+from transformers import pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
-model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+classifier = pipeline("table-question-answering", model="google/tapas-base-finetuned-wtq")
+def main():
+    st.title("English to German")
 
-def get_question(answer, context, max_length=64):
-  input_text = "answer: %s  context: %s </s>" % (answer, context)
-  features = tokenizer([input_text], return_tensors='pt')
+    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)
 
-  output = model.generate(input_ids=features['input_ids'], 
-               attention_mask=features['attention_mask'],
-               max_length=max_length)
-
-  return tokenizer.decode(output[0])
-
-context = "Manuel has created RuPERTa-base with the support of HF-Transformers and Google"
-answer = "Manuel"
-
-get_question(answer, context)
+if __name__ == "__main__":
+    main()