Update app.py

app.py

@@ -42,6 +42,7 @@ stability_api = client.StabilityInference(
 whisper_from_pipeline = pipeline("automatic-speech-recognition",model="openai/whisper-medium")
 EMBEDIDNGS = None
 DATAFRAME_FILE = None
+DOCSEARCH = None
 RANDOM_USER = ''.join(chr(random.randint(65,90)) for i in range(8))+f'{random.randint(1,10000000000)}'
 print(f'{RANDOM_USER} chat started')
 
@@ -140,9 +141,9 @@ def search_internet(user_query:str,*,key_number:int) -> str:
 
 def search_document_uploaded(user_query:str) -> str:
    print('Searching uploaded document......')
-   docsearch = FAISS.load_local(folder_path = f'/tmp/{RANDOM_USER}embeddings',embeddings=EMBEDIDNGS)
+   # docsearch = FAISS.load_local(folder_path = f'/tmp/{RANDOM_USER}embeddings',embeddings=EMBEDIDNGS)
    chain = load_qa_chain(OpenAI(), chain_type="stuff")
-   docs = docsearch.similarity_search(user_query)
+   docs = DOCSEARCH.similarity_search(user_query)
    return chain.run(input_documents=docs, question=user_query)
 
 
@@ -152,7 +153,7 @@ def ask_dataframes(user_query):
 ############# GET OPENAI RESPONSE
 def get_open_ai_reponse(user_query:str)->Union[tuple,str]:
   print(EMBEDIDNGS)
-  if EMBEDIDNGS is not None:
+  if (EMBEDIDNGS is not None) and (DOCSEARCH is not None):
     print('Searching document')
     return search_document_uploaded(user_query)
   
@@ -286,17 +287,22 @@ def build_embeddings(file_name,file_ext):
 
   global EMBEDIDNGS 
   EMBEDIDNGS = OpenAIEmbeddings(openai_api_key=os.environ['OPENAI_API_KEY'])
-  docsearch = FAISS.from_texts(texts, EMBEDIDNGS)
-  if not os.path.exists(f'/tmp/{RANDOM_USER}embeddings'):
-    os.mkdir(f'/tmp/{RANDOM_USER}embeddings')
-  docsearch.save_local(f'/tmp/{RANDOM_USER}embeddings')
-  print(f'Embeddings created to /tmp/{RANDOM_USER}embeddings')
+  global DOCSEARCH  
+  DOCSEARCH = FAISS.from_texts(texts, EMBEDIDNGS)
+  # if not os.path.exists(f'/tmp/{RANDOM_USER}embeddings'):
+  #   os.mkdir(f'/tmp/{RANDOM_USER}embeddings')
+  # docsearch.save_local(f'/tmp/{RANDOM_USER}embeddings')
+  # print(f'Embeddings created to /tmp/{RANDOM_USER}embeddings')
   
  
 def ask_questions_abt_dataframes(file,file_ext):
   print(file_ext)
   global EMBEDIDNGS
   EMBEDIDNGS = None
+  global DOCSEARCH
+  DOCSEARCH = None
+
+  
 
   reader_function = { '.csv': pd.read_csv, '.xlsx': pd.read_excel }.get(file_ext)
   print(reader_function.__name__)