Update app.py

app.py

@@ -1,41 +1,14 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from gliclass import GLiClassModel, ZeroShotClassificationPipeline
+from transformers import AutoTokenizer
 
-# Загрузка модели и токенизатора
-model_name = "openvla/openvla-7b"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
+model = GLiClassModel.from_pretrained("knowledgator/gliclass-large-v1.0")
+tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-large-v1.0")
 
-# Перемещение модели на GPU, если доступно
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = model.to(device)
+pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')
 
-def generate_response(prompt):
-    # Токенизация входного текста
-    inputs = tokenizer(prompt, return_tensors="pt").to(device)
-    
-    # Генерация ответа
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=100,
-            do_sample=True,
-            temperature=0.7,
-            top_p=0.9
-        )
-    
-    # Декодирование и возврат ответа
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response.strip()
+text = "One day I will see the world!"
+labels = ["travel", "dreams", "sport", "science", "politics"]
+results = pipeline(text, labels, threshold=0.5)[0] #because we have one text
 
-# Основной цикл чат-бота
-print("Чат-бот готов! Введите 'выход' для завершения.")
-while True:
-    user_input = input("Вы: ")
-    if user_input.lower() == 'выход':
-        break
-    
-    response = generate_response(user_input)
-    print("Бот:", response)
-
-print("До свидания!")
+for result in results:
+ print(result["label"], "=>", result["score"])