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pages/gpt_v1.py

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+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+import streamlit as st
+
+model = GPT2LMHeadModel.from_pretrained(
+    'sberbank-ai/rugpt3small_based_on_gpt2',
+    output_attentions = False,
+    output_hidden_states = False,
+)
+
+tokenizer = GPT2Tokenizer.from_pretrained('sberbank-ai/rugpt3small_based_on_gpt2')
+
+
+# Вешаем сохраненные веса на нашу модель
+model.load_state_dict(torch.load('models/model.pt', map_location=torch.device('cpu')))
+
+prompt = st.text_input('Введите текст prompt:')
+length = st.slider('Длина генерируемой последовательности:', 10, 1000, 50)
+num_samples = st.slider('Число генераций:', 1, 10, 1)
+temperature = st.slider('Температура:', 0.1, 1.0, 0.5)
+
+
+def generate_text(model, tokenizer, prompt, length, num_samples, temperature):
+    input_ids = tokenizer.encode(prompt, return_tensors='pt')
+    output_sequences = model.generate(
+        input_ids=input_ids,
+        max_length=length,
+        num_return_sequences=num_samples,
+        temperature=temperature
+    )
+
+    generated_texts = []
+    for output_sequence in output_sequences:
+        generated_text = tokenizer.decode(output_sequence, clean_up_tokenization_spaces=True)
+        generated_texts.append(generated_text)
+
+    return generated_texts
+
+
+if st.button('Сгенерировать текст'):
+    generated_texts = generate_text(model, tokenizer, prompt, length, num_samples, temperature)
+    for i, text in enumerate(generated_texts):
+        st.write(f'Текст {i+1}:')
+        st.write(text)
+
+
+

pages/gpt_v2.py

@@ -0,0 +1,41 @@
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+import streamlit as st
+
+model = GPT2LMHeadModel.from_pretrained(
+    'sberbank-ai/rugpt3small_based_on_gpt2',
+    output_attentions = False,
+    output_hidden_states = False,
+)
+
+tokenizer = GPT2Tokenizer.from_pretrained('sberbank-ai/rugpt3small_based_on_gpt2')
+
+# Вешаем сохраненные веса на нашу модель
+model.load_state_dict(torch.load('model.pt', map_location=torch.device('cpu')))
+
+prompt = st.text_input('Введите текст prompt:')
+length = st.slider('Длина генерируемой последовательности:', 10, 256, 16)
+num_samples = st.slider('Число генераций:', 1, 6, 1)
+temperature = st.slider('Температура:', 1.0, 6.0, 1.0)
+selected_text = st.empty()
+
+def generate_text(model, tokenizer, prompt, length, num_samples, temperature, selected_text):
+    input_ids = tokenizer.encode(prompt, return_tensors='pt')
+    output_sequences = model.generate(
+        input_ids=input_ids,
+        max_length=length,
+        num_return_sequences=num_samples,
+        temperature=temperature
+    )
+
+    generated_texts = []
+    for output_sequence in output_sequences:
+        generated_text = tokenizer.decode(output_sequence, clean_up_tokenization_spaces=True)
+        generated_texts.append(generated_text)
+
+    selected_text.slider('Выберите текст:', 1, num_samples, 1)
+    return generated_texts[selected_text.value-1]
+
+if st.button('Сгенерировать текст'):
+    text = generate_text(model, tokenizer, prompt, length, num_samples, temperature, selected_text)
+    st.write(text)