Load the model and the tokenizer

functions.py

@@ -22,9 +22,6 @@ def extract_text(url: str):
 
 
 def summarize_text(text: str):
-    # Pending to load the model first
-    return ''
-    
     print(['summarize_text', 'start'])
     input_text = f'<s>Instruction: Elabora un resume del siguiente texto.\nInput: {text}\nOutput: '
     batch = tokenizer(input_text, return_tensors='pt')
@@ -53,4 +50,4 @@ def load_model(peft_model_id):
     return model, tokenizer
 
 
-# model, tokenizer = load_model("hackathon-somos-nlp-2023/opt-6.7b-lora-sag-t3000-v300-v2")
+model, tokenizer = load_model("hackathon-somos-nlp-2023/opt-6.7b-lora-sag-t3000-v300-v2")