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app.py

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+import transformers
+import torch
+import tokenizers
+import streamlit as st
+
+
+@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None}, suppress_st_warning=True)
+def get_model(model_name, model_path):
+    tokenizer = transformers.GPT2Tokenizer.from_pretrained(model_name)
+    model = transformers.GPT2LMHeadModel.from_pretrained(model_name)
+    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+    model.eval()
+    return model, tokenizer
+
+
+@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None}, suppress_st_warning=True)
+def predict(text, model, tokenizer, n_beams=5, temperature=2.5, top_p=0.8, max_length=300):
+    input_ids = tokenizer.encode(text, return_tensors="pt")
+    with torch.no_grad():
+        out = model.generate(input_ids,
+                             do_sample=True,
+                             num_beams=n_beams,
+                             temperature=temperature,
+                             top_p=top_p,
+                             max_length=max_length,
+                             )
+
+    return list(map(tokenizer.decode, out))[0]
+
+
+model, tokenizer = get_model('sberbank-ai/rugpt3medium_based_on_gpt2', 'SOME_CHECKPOINT.bin')
+
+st.title("NeuroKorzh")
+st.markdown("<img width=200px src='https://avatars.yandex.net/get-music-content/2399641/5d26d7e5.p.975699/m1000x1000'>",
+            unsafe_allow_html=True)
+
+st.markdown("\n")
+
+text = st.text_area(label='Starting point for text generation', height=200)
+button = st.button('Go')
+
+if button:
+    try:
+        result = predict(text, model, tokenizer)
+        st.subheader('Max Korzh:')
+        st.write(result)
+    except Exception:
+        st.error("Ooooops, something went wrong. Try again please and report to me, tg: @vladyur")