test auto reload

app.py

@@ -5,11 +5,30 @@ from gradio_rangeslider import RangeSlider
 import calendar
 import datetime
 import numpy as np
+from huggingface_hub import HfApi
+from apscheduler.schedulers.background import BackgroundScheduler
 
 from src.filter_utils import filter, filter_cols
 from src.process_data import merge_data
 import assets.text_content as tc
 
+""" 
+CONSTANTS
+"""
+# For restarting the gradio application every 24 Hrs
+TIME = 10  # in seconds # Reload will not work locally - requires HFToken # The app launches locally as expected - only without the reload utility
+
+"""
+AUTO RESTART HF SPACE
+"""
+HF_TOKEN = os.environ.get("H4_TOKEN", None)
+api = HfApi()
+
+def restart_space():
+    api.restart_space(repo_id=tc.HF_REPO, token=HF_TOKEN)
+
+
+
 # Main Leaderboard containing everything
 # text_leaderboard = pd.read_csv(os.path.join('assets', 'merged_data.csv'))
 text_leaderboard = merge_data()
@@ -193,9 +212,9 @@ with llm_calc_app:
             ############# Modality selection checkbox ###############
             with gr.Row():
                 multimodal_checkbox = gr.CheckboxGroup(
-                    choices=[tc.SINGLE_IMG, tc.MULT_IMG, tc.AUDIO, tc.VIDEO],
+                    choices=[tc.TEXT, tc.SINGLE_IMG, tc.MULT_IMG, tc.AUDIO, tc.VIDEO],
                     value=[],
-                    label="Additional Modalities 📷🎧🎬",
+                    label="Modalities 📝📷🎧🎬",
                 )
                 
             
@@ -339,4 +358,13 @@ with llm_calc_app:
 
     llm_calc_app.load()
 llm_calc_app.queue()
-llm_calc_app.launch()
+
+# Add scheduler to auto-restart the HF space at every TIME interval and update every component each time
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, 'interval', seconds=TIME)
+scheduler.start()
+
+# Log current start time and scheduled restart time
+print(datetime.datetime.now())
+print(f"Scheduled restart at {datetime.datetime.now() + datetime.timedelta(seconds=TIME)}")
+

assets/text_content.py

@@ -29,6 +29,7 @@ LICENSE_NAME = "License Name"
 LICENSE_URL = "License URL"
 SINGLE_IMG = "Single Image"
 MULT_IMG = "Multi Image"
+TEXT = "Text-Only"
 AUDIO = "Audio"
 VIDEO = "Video"
 INPUT = "Input $/1M tokens"
@@ -42,6 +43,7 @@ COMM = "Commercial"
 
 TITLE = """<h1 align="center" id="space-title"> LLM Calculator ⚖️⚡ 📏💰</h1>"""
 
+HF_REPO = "Koshti10/LLMCalc"
 # Date Picker (set as Dropdown until datetime object is fixed)
 START_YEAR = "2020"
 MONTH_MAP = {

requirements.txt

@@ -1,4 +1,5 @@
 pandas==2.2.3
 gradio_rangeslider==0.0.7
 gradio==4.44.1
-pycountry==24.6.1
+pycountry==24.6.1
+apscheduler==3.10.4

src/filter_utils.py

@@ -93,6 +93,8 @@ def filter(df, language_list, parameters, input_price, output_price, multimodal,
         df = df[(df[tc.OUTPUT] >= output_price[0]) & (df[tc.OUTPUT] <= output_price[1])]
 
     if not df.empty:  # Check if df is non-empty
+        if tc.TEXT in multimodal:
+            df = df[(df[tc.SINGLE_IMG] == False) & (df[tc.MULT_IMG] == False) & (df[tc.AUDIO] == False) & (df[tc.VIDEO] == False) ]
         if tc.SINGLE_IMG in multimodal:
             df = df[df[tc.SINGLE_IMG] == True]
         if tc.MULT_IMG in multimodal: