Update app.py

app.py

@@ -1,17 +1,12 @@
 import streamlit as st
 from transformers import pipeline
+from transformers import AutoTokenizer, T5ForConditionalGeneration
 
-classifier = pipeline("token-classification", model="Jean-Baptiste/camembert-ner")
-def main():
-    st.title("Yelp review")
+model_name = "allenai/unifiedqa-t5-small" # you can specify the model size here
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
 
-    with st.form("text_field"):
-        text = st.text_area('enter some text:')
-        # clicked==True only when the button is clicked
-        clicked = st.form_submit_button("Submit")
-        if clicked:
-          results = classifier([text])
-          st.json(results)
-
-if __name__ == "__main__":
-    main()
+def run_model(input_string, **generator_args):
+    input_ids = tokenizer.encode(input_string, return_tensors="pt")
+    res = model.generate(input_ids, **generator_args)
+    return tokenizer.batch_decode(res, skip_special_tokens=True)