update function with cpu

sumar_hf_space.py

@@ -1,22 +1,22 @@
-import torch
-from transformers import GPT2Tokenizer, T5ForConditionalGeneration
-
-
-tokenizer = GPT2Tokenizer.from_pretrained('RussianNLP/FRED-T5-Summarizer', eos_token='</s>')
-model = T5ForConditionalGeneration.from_pretrained('RussianNLP/FRED-T5-Summarizer')
-device = 'cuda'
-model.to(device)
-
-input_text = "<LM> Сократи текст.\n "
-
-def make_summarization(user_text):
-    processing_text = input_text + user_text
-    input_ids = torch.tensor([tokenizer.encode(processing_text)]).to(device)
-    outputs = model.generate(input_ids, eos_token_id=tokenizer.eos_token_id,
-                             num_beams=3,
-                             min_new_tokens=17,
-                             max_new_tokens=200,
-                             do_sample=True,
-                             no_repeat_ngram_size=4,
-                             top_p=0.9)
-    return tokenizer.decode(outputs[0][1:])
+import torch
+from transformers import GPT2Tokenizer, T5ForConditionalGeneration
+
+
+tokenizer = GPT2Tokenizer.from_pretrained('RussianNLP/FRED-T5-Summarizer', eos_token='</s>')
+model = T5ForConditionalGeneration.from_pretrained('RussianNLP/FRED-T5-Summarizer')
+device = 'cpu'
+model.to(device)
+
+input_text = "<LM> Сократи текст.\n "
+
+def make_summarization(user_text):
+    processing_text = input_text + user_text
+    input_ids = torch.tensor([tokenizer.encode(processing_text)]).to(device)
+    outputs = model.generate(input_ids, eos_token_id=tokenizer.eos_token_id,
+                             num_beams=3,
+                             min_new_tokens=17,
+                             max_new_tokens=200,
+                             do_sample=True,
+                             no_repeat_ngram_size=4,
+                             top_p=0.9)
+    return tokenizer.decode(outputs[0][1:])