Update

app.py

@@ -3,16 +3,27 @@ import streamlit as st
 
 from transformers import AutoTokenizer, AutoModelWithLMHead
 
-tokenizer = AutoTokenizer.from_pretrained("gpt2-base")
+###################
+# global variables
+
+
+###################
+# page configs and functions
+
+st.set_page_config(
+    page_title="Romanian Text Generator",
+    page_icon="🇷🇴",
+)
+
+tokenizer = AutoTokenizer.from_pretrained("dumitrescustefan/gpt-neo-romanian-780m")
 
 
 @st.cache
 def load_model(model_name):
-    model = AutoModelWithLMHead.from_pretrained("gpt2-base")
+    model = AutoModelWithLMHead.from_pretrained("dumitrescustefan/gpt-neo-romanian-780m")
     return model
 
-
-model = load_model("gpt2-base")
+model = load_model("dumitrescustefan/gpt-neo-romanian-780m")
 
 
 def infer(input_ids, max_length, temperature, top_k, top_p):
@@ -32,7 +43,7 @@ def infer(input_ids, max_length, temperature, top_k, top_p):
 default_value = "See how a modern neural network auto-completes your text 🤗 This site, built by the Hugging Face team, lets you write a whole document directly from your browser, and you can trigger the Transformer anywhere using the Tab key. Its like having a smart machine that completes your thoughts 😀 Get started by typing a custom snippet, check out the repository, or try one of the examples. Have fun!"
 
 # prompts
-st.title("Write with Transformers 🦄")
+st.title("Write")
 st.write(
     "The almighty king of text generation, GPT-2 comes in four available sizes, only three of which have been publicly made available. Feared for its fake news generation capabilities, it currently stands as the most syntactically coherent model. A direct successor to the original GPT, it reinforces the already established pre-training/fine-tuning killer duo. From the paper: Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.")