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import requests
import gradio as gr
from threading import Timer
def fetch_models():
# Example URL, replace with the actual API endpoint
url = "https://api.example.com/v1/models"
headers = {
"Authorization": f"Bearer {TOKEN}"
}
response = requests.get(url, headers=headers)
response.raise_for_status()
return response.json()
def loop_query_data():
global all_models, first_run
try:
models_dict = fetch_models()
models = models_dict.get('text-generation', []) + models_dict.get('text2text-generation', [])
all_models = models
except KeyError as e:
print(f"KeyError: {e} not found in the models dictionary")
all_models = []
def search_models(query, filters, use_cache=False):
# Add your logic here to filter models based on the query and filters
filtered_models = [model for model in all_models if query.lower() in model.lower()]
# Apply additional filtering based on the 'filters' parameter
return filtered_models
def display_table(use_cache=False):
# Create a table display of models
data = [{"Model Name": model} for model in all_models]
return data
first_run = True
all_models = []
loop_query_data()
with gr.Blocks() as demo:
gr.Markdown("## HF Serverless LLM Inference API Status")
gr.Markdown("Description of the API")
search_box = gr.Textbox(label="Search for a model", placeholder="Type model name here...")
filter_box = gr.CheckboxGroup(choices=["Free", "Pro Subscription", "Not Responding", "Text Completion", "Chat Completion", "Vision"], label="Filters")
table = gr.Dataframe(value=display_table(use_cache=True), headers="keys")
def update_filters(query, filters):
return search_models(query, filters, use_cache=True)
search_box.change(fn=update_filters, inputs=[search_box, filter_box], outputs=table)
filter_box.change(fn=update_filters, inputs=[search_box, filter_box], outputs=table)
def update_every_two_hours(first_run):
loop_query_data()
search_models(search_box.value, [], use_cache=first_run)
Timer(7200, update_every_two_hours, args=(False,)).start() # 7200 seconds = 2 hours
Timer(0, update_every_two_hours, args=(first_run,)).start()
demo.launch()
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