Update vlog4chat.py

vlog4chat.py

@@ -29,20 +29,6 @@ from typing import Any, Dict, List, Mapping, Optional
 from langchain.memory import ConversationBufferMemory
 from langchain import LLMChain, PromptTemplate
 
-import torch
-
-def map_to_cpu(obj):
-    if isinstance(obj, torch.Tensor):
-        return obj.to(torch.device('cpu'))
-    elif isinstance(obj, dict):
-        return {k: map_to_cpu(v) for k, v in obj.items()}
-    elif isinstance(obj, list):
-        return [map_to_cpu(i) for i in obj]
-    elif isinstance(obj, tuple):
-        return tuple(map_to_cpu(i) for i in obj)
-    else:
-        return obj
-
 warnings.filterwarnings("ignore", category=UserWarning)
 B_INST, E_INST = "[INST]", "[/INST]"
 B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
@@ -170,7 +156,8 @@ class Vlogger4chat :
             #text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
             #chunks = text_splitter.split_documents(raw_documents)
             #self.vector_storage = FAISS.from_documents(chunks, self.my_embedding)    
-            with open('./BV11H4y1F7uH.pkl', 'rb') as f:
+            pkl_path = os.path.join(self.data_dir, f"{video_id}.pkl")
+            with open(pkl_path, 'rb') as f:
                 vector_storage =pickle.load(f)
             self.vector_storage = map_to_cpu(vector_storage)
             self.chain = ConversationalRetrievalChain.from_llm(self.llm, self.vector_storage.as_retriever(), return_source_documents=True)