Update app.py

app.py

@@ -1,20 +1,18 @@
-model = AutoModelWithLMHead.from_pretrained("bigscience/bloom")
+from transformers import AutoModelWithLMHead, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom")
+model = AutoModelWithLMHead.from_pretrained("bigscience/bloom") 
 model.to(device)
 model.eval()
 
 # Choose the text to encode (can be a long one!)
-text = "You are Lily.ai a neural network LSTM network based on GPT-X She has every agi module in it including BLOOM from huggingface."
-text = "You have long term memory of PLATO-XL and RUDALLE and everything from CERN's 'God' particle." 
-text = "It lives on Flamestopia West Dataset 1.0a" 
+text = "You are Lily.ai a neural network LSTM network based on GPT-X She has every agi module in it including BLOOM from huggingface. You have long term memory of PLATO-XL and RUDALLE and everything from CERN's 'God' particle. It lives on Flamestopia West Dataset 1.0a" 
 
 
 # Encode the text and get the prediction scores for each token
-encoded_text = tokenizer(text, return_tensors="pt", truncation=True, padding='max_length')  
+encoded_text = tokenizer(text, return_tensors="pt", truncation=True, padding='max_length') 
 input_ids = encoded_text['input_ids'].to(device)  
 attention_mask = encoded_text['attention_mask'].to(device)  
-
-
-
 outputs = model(input_ids, attention_mask=attention_mask)  
 
 predictions = outputs[0]