Upload app.py

app.py

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+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import streamlit as st
+
+st.set_page_config(
+    page_title="GPT-2 Demo", 
+    page_icon=":robot_face:", 
+    layout="wide")
+
+st.title("GPT-2 Text Generation Demo")
+st.info("This is an GPT2 Text Generation Example using HuggingFace GPT2 Model")
+
+pretrained = "gpt2-large"
+tokenizer = GPT2Tokenizer.from_pretrained(pretrained)
+model = GPT2LMHeadModel.from_pretrained(pretrained, pad_token_id=tokenizer.eos_token_id)
+
+sentence = st.text_input('Input your sentence here:', value='My favorite ice cream flavor is ')
+
+st.info("Max generated sentence: 100 words")
+if (st.button("Generate")):
+    input_ids = tokenizer.encode(sentence, return_tensors='pt')
+    paragraph_generated = model.generate(input_ids, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)
+    text = tokenizer.decode(paragraph_generated[0], skip_special_tokens=True)
+
+    st.write(text)