test cv extraction

app.py

@@ -28,8 +28,8 @@ load_dotenv()
 api_token = os.getenv("HF_TOKEN")
 huggingface_hub.login(token=api_token)
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b-it')
-model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b-it').to(device)
+tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-9b')
+model = AutoModelForCausalLM.from_pretrained('google/gemma-2-9b').to(device)
 
 #@spaces.GPU
 def read_pdf(file_path):
@@ -57,7 +57,7 @@ def read_pdf(file_path):
     return output
 
 
-@spaces.GPU(duration=19)
+@spaces.GPU(duration=30)
 def LLM_Inference(cv_text):
     text = f'''
     You are an AI designed to extract structured information from unstructured text. Your task is to analyze the content of a candidate's CV and extract the following details: