init app

app.py

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+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import streamlit as st
+from PIL import Image
+
+tokenizer= T5Tokenizer.from_pretrained("Voicelab/vlt5-base-keywords")
+model = T5ForConditionalGeneration.from_pretrained("Voicelab/vlt5-base-keywords")
+
+img_full = Image.open("images/vl-logo-nlp-blue.png")
+img_short = Image.open("images/vl-logo-nlp-short.png")
+img_favicon = Image.open("images/favicon_vl.png")
+max_length: int = 1000
+cache_size: int = 100
+
+st.set_page_config(
+    page_title='DEMO - keywords generation',
+    page_icon=img_favicon,
+    initial_sidebar_state="expanded",
+)
+
+def get_predictions(text):
+    input_ids = tokenizer(
+        text, return_tensors="pt", truncation=True
+    ).input_ids
+    output = model.generate(input_ids, no_repeat_ngram_size=3, num_beams=4)
+    predicted_kw = tokenizer.decode(output[0], skip_special_tokens=True)
+    return predicted_kw
+
+def trim_length():
+    if len(st.session_state["input"]) > max_length:
+        st.session_state["input"] = st.session_state["input"][:max_length]
+
+
+if __name__ == "__main__":
+    st.sidebar.image(img_short)
+    st.image(img_full)
+    st.title('VLT5 - keywords generation')
+
+    generated_keywords = ""
+    user_input = st.text_area(
+        label=f"Input text (max {max_length} characters)",
+        value="",
+        height=300,
+        on_change=trim_length,
+        key="input",
+    )
+    
+    result = st.button("Generate keywords")
+    if result:
+        generated_keywords = get_predictions(text=user_input)
+        st.text_area("Generated keywords", generated_keywords)
+

requirements.txt

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+streamlit
+transformers
+torch
+transformers[sentencepiece]
+PIL