demo for titles generation

app.py

@@ -0,0 +1,22 @@
+import streamlit as st
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import nltk
+nltk.download('punkt')
+
+tokenizer = AutoTokenizer.from_pretrained("fabiochiu/t5-small-medium-title-generation")
+model = AutoModelForSeq2SeqLM.from_pretrained("fabiochiu/t5-small-medium-title-generation")
+
+text = st.text_area('Enter an abstract to summerize, i.e. generate a title!')
+
+
+if text:
+    inputs = ["summarize: " + text]
+    inputs = tokenizer(inputs, max_length=max_input_length, truncation=True, return_tensors="pt")
+    output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64)
+    decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+    predicted_title = nltk.sent_tokenize(decoded_output.strip())[0]
+    
+    html_string = f"<h3>{predicted_title}</h3>"
+
+    st.markdown(html_string, unsafe_allow_html=True)