Clean code

app.py

@@ -1,14 +1,12 @@
 import gradio as gr
 import os
 from queue import SimpleQueue
-
 from langchain.callbacks.manager import CallbackManager
 from langchain.chat_models import ChatOpenAI
 from pydantic import BaseModel
 import requests
 import typing
 from typing import TypeVar, Generic
-import math
 import tqdm
 from langchain.chains import ConversationalRetrievalChain
 import os
@@ -16,8 +14,6 @@ from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.vectorstores import DeepLake
 import random
 import time
-import together
-
 import os
 from langchain.document_loaders import TextLoader
 from langchain.text_splitter import CharacterTextSplitter
@@ -75,13 +71,6 @@ class GithubResponse(BaseModel):
     repo: str
 
 
-# global repoName
-# global ticket_titles
-# global tickets
-# global ticket_choices
-tickets = []
-
-
 repo_name = gr.State()
 git_tickets = gr.State()
 git_titles = gr.State()
@@ -128,13 +117,17 @@ def index_repo(textbox: str, dropdown: str) -> Response:
     dataset_path = f"hub://{activeloop_username}/" + pathName
 
     try:
-        db = DeepLake(dataset_path=dataset_path,
-                embedding_function=embeddings,
-                token=os.environ['ACTIVELOOP_TOKEN'],
-                read_only=True,
-                num_workers=12,
-                runtime = {"tensor_db": True}
-            )
+        try:
+            db = DeepLake(dataset_path=dataset_path,
+                    embedding_function=embeddings,
+                    token=os.environ['ACTIVELOOP_TOKEN'],
+                    read_only=True,
+                    num_workers=12,
+                    runtime = {"tensor_db": True}
+                )
+        except Exception as e:
+            if "scheduled for deletion" in str(e):
+                dataset_path = f"hub://{activeloop_username}/" + pathName + str(random.randint(1,100))
 
         if db is None or len(db.vectorstore.dataset) == 0:
             print("Dataset doesn't exist, fetching data")