Update app.py

app.py

@@ -5,7 +5,7 @@ from langchain_community.embeddings import HuggingFaceEmbeddings
 from flashrank import Ranker, RerankRequest
 import numpy as np
 
-@st.cache_data
+@st.cache_data(allow_output_mutation=True)
 def get_embeddings():
     model_name = "BAAI/bge-large-en-v1.5"
     model_kwargs = {'device': 'cpu',"trust_remote_code":True}
@@ -18,7 +18,8 @@ def get_embeddings():
 
 baai_embeddings = get_embeddings()
 kadhal_Server = FAISS.load_local("./",baai_embeddings)
-ranker = Ranker(model_name="ms-marco-MiniLM-L-12-v2", cache_dir="./opt/")
+
+# ranker = Ranker(model_name="ms-marco-MiniLM-L-12-v2")
 
 
 st.header('kadhalTensor', divider='red')
@@ -48,11 +49,12 @@ with st.form("my_form"):
        
        docs = kadhal_Server.similarity_search(question_input,k=5)
        tobeReranked = [{"text":doc.page_content , "metadata":doc.metadata} for doc in docs]
-       rerankInput = RerankRequest(
-                        passages=tobeReranked,
-                        query=question_input,)
-       reranked = ranker.rerank(rerankInput)
-       sample = np.random.choice(reranked,2)
+       # rerankInput = RerankRequest(
+       #                  passages=tobeReranked,
+       #                  query=question_input,)
+       
+       # reranked = ranker.rerank(rerankInput)
+       sample = np.random.choice(tobeReranked,2)
        
        for out in sample: