Create app.py

app.py

@@ -0,0 +1,45 @@
+import streamlit as st
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+@st.cache(allow_output_mutation=True)
+def load_model():
+    '''
+    Loads the model and tokenizer from the local directory.
+    :return: A list containing the model and the tokenizer.
+    '''
+    model_name = 'WIP'
+    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+    model = GPT2LMHeadModel.from_pretrained(model_name)
+    return [model, tokenizer]
+
+st.set_page_config(
+    page_title="BulgakovLM Example",
+    page_icon="👨‍💻",
+)
+
+st.markdown("# 👨‍💻 BulgakovLM Example")
+
+txt = st.text_area('Write code here', '''Однажды утром''', height=400)
+
+gen = st.button('Generate')
+
+c = st.code('')
+
+max_length = st.slider('max_length', 1, 1024, 128)
+top_k = st.slider('top_k', 0, 100, 50)
+top_p = st.slider('top_p', 0.0, 1.0, 0.9)
+temperature = st.slider('temperature', 0.0, 1.0, 1.0)
+num_beams = st.slider('num_beams', 1, 100, 5)
+repetition_penalty = st.slider('repetition_penalty', 1.0, 10.0, 1.0)
+
+
+if gen:
+    c.code('Generating...')
+    m = load_model()
+
+    inpt = m[1].encode(txt, return_tensors="pt")
+    out = m[0].generate(inpt, max_length=max_length, top_p=top_p, top_k=top_k, temperature=temperature, num_beams=num_beams, repetition_penalty=repetition_penalty)
+    res = m[1].decode(out[0])
+
+    print('ok')
+    c.code(res)