Update pages/task3.py

pages/task3.py

@@ -1,89 +1,89 @@
-import streamlit as st
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-import time
-
-def generate_text(model, tokenizer, prompt, max_length, num_generations, temperature):
-    generated_texts = []
-
-    for _ in range(num_generations):
-        input_ids = tokenizer.encode(prompt, return_tensors="pt")
-        output = model.generate(
-            input_ids,
-            max_length=max_length,
-            temperature=temperature,
-            num_return_sequences=1
-        )
-        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
-        generated_texts.append(generated_text)
-
-    return generated_texts
-
-button_style = """
-    <style>
-    .center-align {
-        display: flex;
-        justify-content: center;
-    
-    </style>
-"""
-
-DEVICE = 'cpu' 
-
-# Загрузка пользовательской модели и токенизатора (замените на свои пути и модель)
-# model_path = "sberbank-ai/rugpt3small_based_on_gpt2"
-# tokenizer_path = "sberbank-ai/rugpt3small_based_on_gpt2"
-
-# model = GPT2LMHeadModel.from_pretrained(model_path).to(DEVICE)
-# tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path)
-
-st.markdown("""
-    <style>
-        section[data-testid="stSidebar"][aria-expanded="true"]{
-            display: none;
-        }
-    </style>
-    """, unsafe_allow_html=True)
-
-st.write("## Text generator")
-st.page_link("main.py", label="Home", icon='🏠')
-st.markdown(
-        """
-        This streamlit-app can generate text using your prompt 
-    """
-)
-# Ввод пользовательского prompt
-prompt = st.text_area("Enter your prompt:")
-
-# Параметры генерации
-max_length = st.slider("Max length of generated text:", min_value=10, max_value=500, value=100, step=10)
-num_generations = st.slider("Number of generations:", min_value=1, max_value=10, value=3, step=1)
-temperature = st.slider("Temperature:", min_value=0.1, max_value=2.0, value=1.0, step=0.1)
-try:
-    if st.button("Generate text"):
-        start_time = time.time()
-        generated_texts = generate_text(model, tokenizer, prompt, max_length, num_generations, temperature)
-        end_time = time.time()
-
-        st.subheader("Сгенерированный текст:")
-        for i, text in enumerate(generated_texts, start=1):
-            st.write(f"Генерация {i}:\n{text}")
-
-        generation_time = end_time - start_time
-        st.write(f"\nВремя генерации: {generation_time:.2f} секунд")
-
-    st.markdown(button_style, unsafe_allow_html=True)  # Применяем стиль к кнопке
-    st.markdown(
-        """
-        <style>
-            div[data-baseweb="textarea"] {
-                border: 2px solid #3498db;  /* Цвет границы */
-                border-radius: 5px;  /* Закругленные углы */
-                background-color: #ecf0f1;  /* Цвет фона */
-                padding: 10px;  /* Поля вокруг текстового поля */
-            }
-        </style>
-        """,
-        unsafe_allow_html=True,
-    )
-except:
+import streamlit as st
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import time
+
+def generate_text(model, tokenizer, prompt, max_length, num_generations, temperature):
+    generated_texts = []
+
+    for _ in range(num_generations):
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        output = model.generate(
+            input_ids,
+            max_length=max_length,
+            temperature=temperature,
+            num_return_sequences=1
+        )
+        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+        generated_texts.append(generated_text)
+
+    return generated_texts
+
+button_style = """
+    <style>
+    .center-align {
+        display: flex;
+        justify-content: center;
+    
+    </style>
+"""
+
+DEVICE = 'cpu' 
+
+# Загрузка пользовательской модели и токенизатора (замените на свои пути и модель)
+# model_path = "sberbank-ai/rugpt3small_based_on_gpt2"
+# tokenizer_path = "sberbank-ai/rugpt3small_based_on_gpt2"
+
+# model = GPT2LMHeadModel.from_pretrained(model_path).to(DEVICE)
+# tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path)
+
+st.markdown("""
+    <style>
+        section[data-testid="stSidebar"][aria-expanded="true"]{
+            display: none;
+        }
+    </style>
+    """, unsafe_allow_html=True)
+
+st.write("## Text generator")
+st.page_link("app.py", label="Home", icon='🏠')
+st.markdown(
+        """
+        This streamlit-app can generate text using your prompt 
+    """
+)
+# Ввод пользовательского prompt
+prompt = st.text_area("Enter your prompt:")
+
+# Параметры генерации
+max_length = st.slider("Max length of generated text:", min_value=10, max_value=500, value=100, step=10)
+num_generations = st.slider("Number of generations:", min_value=1, max_value=10, value=3, step=1)
+temperature = st.slider("Temperature:", min_value=0.1, max_value=2.0, value=1.0, step=0.1)
+try:
+    if st.button("Generate text"):
+        start_time = time.time()
+        generated_texts = generate_text(model, tokenizer, prompt, max_length, num_generations, temperature)
+        end_time = time.time()
+
+        st.subheader("Сгенерированный текст:")
+        for i, text in enumerate(generated_texts, start=1):
+            st.write(f"Генерация {i}:\n{text}")
+
+        generation_time = end_time - start_time
+        st.write(f"\nВремя генерации: {generation_time:.2f} секунд")
+
+    st.markdown(button_style, unsafe_allow_html=True)  # Применяем стиль к кнопке
+    st.markdown(
+        """
+        <style>
+            div[data-baseweb="textarea"] {
+                border: 2px solid #3498db;  /* Цвет границы */
+                border-radius: 5px;  /* Закругленные углы */
+                background-color: #ecf0f1;  /* Цвет фона */
+                padding: 10px;  /* Поля вокруг текстового поля */
+            }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+except:
     st.write('Модель в разработке ( ﾉ ﾟｰﾟ)ﾉ( ﾉ ﾟｰﾟ)ﾉ( ﾉ ﾟｰﾟ)ﾉ( ﾉ ﾟｰﾟ)ﾉ')