Update new_sum.py

new_sum.py

@@ -1,9 +1,6 @@
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoConfig
 import torch
 
-# ======================
-# MODEL SETUP
-# ======================
 MODEL_NAME = "cointegrated/rut5-base-multitask"
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -11,37 +8,41 @@ config = AutoConfig.from_pretrained(MODEL_NAME)
 config.tie_word_embeddings = False  
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-
 model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME, config=config).to(device)
 model.eval()
 
-# ======================
-# CORE FUNCTION
-# ======================
+
 def generate_summary(text: str) -> str:
     if not text:
         return ""
 
-    prompt = "summarize | " + text
+    # чуть лучше для T5
+    prompt = "summarize: " + text
 
     inputs = tokenizer(
         prompt,
         return_tensors="pt",
         truncation=True,
-        padding="max_length",
+        padding="longest",
         max_length=512
     ).to(device)
 
     with torch.no_grad():
         outputs = model.generate(
             **inputs,
-            max_length=200,
+            max_length=150,
             min_length=30,
-            num_beams=3,
+            num_beams=4,
             do_sample=False,
+            repetition_penalty=2.0,
             no_repeat_ngram_size=3,
-            repetition_penalty=1.2,
             early_stopping=True
         )
 
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # 🔥 ВАЖНО: защита от мусорных токенов
+    if "<0x" in summary or len(summary.strip()) < 10:
+        return "Model output invalid or unstable. Try different input."
+
+    return summary