Create app.py

app.py

@@ -0,0 +1,48 @@
+import streamlit as st
+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+
+
+
+model_name_or_path = "sberbank-ai/rugpt3small_based_on_gpt2"
+tokenizer = GPT2Tokenizer.from_pretrained(model_name_or_path)
+model =  GPT2LMHeadModel.from_pretrained(
+    model_name_or_path,
+    output_attentions = False,
+    output_hidden_states = False,
+)
+
+
+# Загрузка сохраненных весов
+model_weights_path = "hunter_pelevin.pt"
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.load_state_dict(torch.load(model_weights_path, map_location=device))
+model.eval()
+def generate_text(user_input, model=model, tokenizer=tokenizer):
+    input_ids = tokenizer.encode(user_input, return_tensors="pt")
+    with torch.no_grad():
+        out = model.generate(
+            input_ids,
+            max_length=slider1,
+            num_beams=10,
+            do_sample=True,
+            temperature=slider3,
+            top_k=500,
+            top_p=0.8,
+            no_repeat_ngram_size=3,
+            num_return_sequences=slider2,
+        )
+    generated_text = list(map(tokenizer.decode, out))[0]
+    return generated_text
+
+
+st.title("Простое веб-приложение на Streamlit")
+# Получаем ввод от пользователя
+user_input = st.text_area("Введите текст:")
+slider1 = st.slider("Выберите длинну текста:", min_value=10, max_value=100, value=50)
+slider2 = st.slider("Выберите количество генераций", min_value=1, max_value=5, value=2)
+slider3 = st.slider("Выберите степень безумия:", min_value=0.1, max_value=3.0, value=1.2, step=0.1)
+if user_input:
+        gen_text = generate_text(user_input)
+        st.write(gen_text)