set up spaces zero-gpu

pdf_processor.py

@@ -1,5 +1,6 @@
 import json
 import tempfile
+import spaces
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_ollama import OllamaEmbeddings
@@ -143,7 +144,7 @@ class PDFProcessor:
             )
         return current_llm, embeding_model
     
-
+    @spaces.GPU
     def process_pdf(self, pdf_file, chunk_size, chunk_overlap, ai_model, type_model, api_key, project_id_watsonx):
         defined_chunk_size = 1000
         defined_chunk_overlap = 150
@@ -195,7 +196,7 @@ class PDFProcessor:
         else:
             return TRANSLATIONS[self.language]["load_pdf_first"]
         
-
+    @spaces.GPU
     def get_qa_response(self, message, history, ai_model, type_model, api_key, project_id_watsonx, k=4):
         current_llm, _ = self.set_llm(ai_model, type_model, api_key, project_id_watsonx)
 
@@ -219,7 +220,7 @@ class PDFProcessor:
 
         return result["result"] + "\n\nSources: " + page_labels_text
         
-
+    @spaces.GPU
     def summarizer_by_k_top_n(self, ai_model, type_model, api_key, project_id_watsonx, k, summary_prompt, just_get_documments=False):
         if not self.vectorstore:
             return TRANSLATIONS[self.language]["load_pdf_first"]
@@ -256,7 +257,7 @@ class PDFProcessor:
         return self.summarizer_by_k_top_n(ai_model, type_model, api_key, project_id_watsonx, k, final_summary_prompt, just_get_documments)
     
     
-
+    @spaces.GPU
     def get_specialist_opinion(self, ai_model, type_model, api_key, project_id_watsonx, specialist_prompt):
         questions_prompt = PromptTemplate(
             input_variables=["text", "specialist_prompt", "language"],