linting

app.py

@@ -1,21 +1,15 @@
-from huggingface_hub import hf_hub_url, list_datasets
-from dotenv import load_dotenv
 import os
+from datetime import datetime, timedelta
+from sys import platform
+
+import gradio as gr
+import pandas as pd
+from diskcache import Cache
+from dotenv import load_dotenv
 from httpx import Client
-from datetime import datetime
-from datetime import timedelta
+from huggingface_hub import hf_hub_url, list_datasets
 from tqdm.auto import tqdm
 from tqdm.contrib.concurrent import thread_map
-import pandas as pd
-import gradio as gr
-
-from huggingface_hub import hf_hub_url
-from diskcache import Cache
-from sys import platform
-import gradio as gr
-
-# check if running on macos i.e. local dev
-
 
 load_dotenv()
 
@@ -159,35 +153,6 @@ def filter_df_by_max_age(max_age_days=None):
     return df
 
 
-# def filter_df(
-#     min_age_days=None,
-#     max_age_days=None,
-#     min_likes=None,
-#     max_likes=None,
-#     min_len=None,
-#     max_len=None,
-#     min_downloads=None,
-#     max_downloads=None,
-# ):
-#     if min_age_days is not None or max_age_days is not None:
-#         df = filter_df_by_date(min_age_days, max_age_days)
-#     else:
-#         df = prep_dataframe()
-#     if min_likes:
-#         df = df[df["likes"] >= min_likes]
-#     if max_likes:
-#         df = df[df["likes"] <= max_likes]
-#     if min_len:
-#         df = df[df["len"] >= min_len]
-#     if max_len:
-#         df = df[df["len"] <= max_len]
-#     if min_downloads:
-#         df = df[df["downloads"] >= min_downloads]
-#     if max_downloads:
-#         df = df[df["downloads"] <= max_downloads]
-#     return df
-
-
 with gr.Blocks() as demo:
     max_age_days = gr.Slider(
         label="Max Age (days)", value=7, minimum=0, maximum=90, step=1, interactive=True