Update app.py

app.py

@@ -8,7 +8,9 @@ from tensorflow.keras.preprocessing.text import Tokenizer
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 import time
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
 
+tok = GPT2Tokenizer()
 model_checkpoint = 'cointegrated/rubert-tiny-toxicity'
 toxicity_tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
 toxicity_model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
@@ -21,6 +23,8 @@ bert_model = BertForSequenceClassification.from_pretrained('my_bert_model')
 tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 bert_model = bert_model.to(device)
+model_finetuned = load_full_model_from_pt(model_directory)
+model_finetuned.eval()
 
 labels = ["не токсичный", "оскорбляющий", "непристойный", "угрожающий", "опасный"]
 def text2toxicity(text, aggregate=True):
@@ -49,7 +53,17 @@ def predict_text(text):
     return predicted_class
 
 
-
+def generate_text(model, prompt, max_length=150, temperature=1.0):
+    input_ids = tok.encode(prompt, return_tensors='pt')
+    output = sber.generate(
+        input_ids=input_ids,
+        max_length=max_length + len(input_ids[0]),
+        temperature=temperature,
+        num_return_sequences=1,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    generated_text = tok.decode(output[0], skip_special_tokens=True)
+    return generated_text
 
 
 def page_reviews_classification():
@@ -111,6 +125,19 @@ def page_toxicity_analysis():
             st.write(f"Вероятность того что комментарий {label}: {prob:.4f}")
 
 
+def page_gpt_generation():
+    st.title("Генерация текста с помощью GPT-модели")
+    
+    user_prompt = st.text_area("Введите ваш текст:")
+    sequence_length = st.slider("Длина последовательности:", min_value=10, max_value=1000, value=150, step=10)
+    num_generations = st.slider("Число генераций:", min_value=1, max_value=10, value=1)
+    temperature = st.slider("Температура:", min_value=0.1, max_value=3.0, value=1.0, step=0.1)
+
+    if st.button("Генерировать"):
+        for _ in range(num_generations):
+            generated_text = generate_text(model_finetuned, user_prompt, sequence_length, temperature)
+            st.text(generated_text)
+
 def main():
     page_selection = st.sidebar.selectbox("Выберите страницу:", ["Классификация отзывов", "Анализ токсичности"])