update embeding model

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-import spaces
 from pdf_processor import PDFProcessor
 from utils import AI_MODELS, TRANSLATIONS
 

pdf_processor.py

@@ -1,5 +1,4 @@
 import json
-import spaces
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_ollama import OllamaEmbeddings
@@ -150,7 +149,7 @@ class PDFProcessor:
                     max_length=2048,
                 )
             embeding_model = HuggingFaceEmbeddings(
-                model_name="ibm-granite/granite-embedding-107m-multilingual",
+                model_name="ibm-granite/granite-embedding-278m-multilingual",
             )
         return current_llm, embeding_model
     
@@ -208,7 +207,6 @@ class PDFProcessor:
         else:
             return TRANSLATIONS[self.language]["load_pdf_first"], None
         
-    @spaces.GPU
     def get_qa_response(self, vectorstore, 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)
 
@@ -232,7 +230,6 @@ class PDFProcessor:
 
         return result["result"] + "\n\nSources: " + page_labels_text
         
-    @spaces.GPU
     def summarizer_by_k_top_n(self, vectorstore, ai_model, type_model, api_key, project_id_watsonx, k, summary_prompt, just_get_documments=False):
         print("Summarizer by k top n in language: ", self.language)
         if not vectorstore:
@@ -250,7 +247,6 @@ class PDFProcessor:
         final_summary = summary_chain.invoke({"texts": "\n".join([doc.page_content for doc in documents]), "language": self.language})
         return final_summary
 
-    @spaces.GPU
     def get_summary(self, vectorstore, ai_model, type_model, api_key, project_id_watsonx, just_get_documments=False, k=10):
 
         final_summary_prompt = PromptTemplate(
@@ -270,7 +266,6 @@ class PDFProcessor:
         return self.summarizer_by_k_top_n(vectorstore, ai_model, type_model, api_key, project_id_watsonx, k, final_summary_prompt, just_get_documments)
     
     
-    @spaces.GPU
     def get_specialist_opinion(self, vectorstore, ai_model, type_model, api_key, project_id_watsonx, specialist_prompt):
         questions_prompt = PromptTemplate(
             input_variables=["text", "specialist_prompt", "language"],