init

app.py

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+import streamlit as st
+
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+
+@st.cache_resource
+def init_model():
+    model = T5ForConditionalGeneration.from_pretrained("t5-small")
+    tokenizer = T5Tokenizer.from_pretrained("t5-small")
+    return model, tokenizer
+
+
+max_source_length = 512
+max_target_length = 128
+
+model, tokenizer = init_model()
+
+st.title('T5-Small')
+with st.form('my_form'):
+    text = st.text_area('Enter text:', '')
+    cols = st.columns(3)
+    submitted = cols[0].form_submit_button('translate')
+    task_prefix = cols[1].text_input("input language", "translate Chinese to English: ")
+
+placeholder = st.markdown("", unsafe_allow_html=True)
+if submitted:
+    with st.spinner("Translating..."):
+        input_ids = tokenizer(f"{task_prefix}{text}", return_tensors="pt").input_ids
+        outputs = model.generate(input_ids)
+        placeholder.markdown(tokenizer.decode(outputs[0], skip_special_tokens=True))

requirements.txt

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+torch
+torchvision
+torchaudio
+transformers
+sentencepiece