Update app.py

app.py

@@ -5,7 +5,11 @@ from huggingface_hub import InferenceClient
 from threading import Timer
 
 HUGGINGFACE_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")
-def get_available_free():
+def get_available_free(use_cache = False):
+    if use_cache:
+        if os.path.exists("data.csv"):
+            print("Loading data from file...")
+            return pd.read_csv("data.csv").to_dict(orient='list')
     models_dict = InferenceClient(token=HUGGINGFACE_TOKEN).list_deployed_models("text-generation-inference")
     models = models_dict['text-generation'] + models_dict['text2text-generation']
     models_vision = models_dict['image-text-to-text']
@@ -59,8 +63,8 @@ def get_available_free():
     pd.DataFrame(models_conclusion).to_csv("data.csv", index=False)
     return models_conclusion
 
-def update_data():
-    data = get_available_free()
+def update_data(use_cache = False):
+    data = get_available_free(use_cache)
     df = pd.DataFrame(data)
     
     status_mapping = {"✓": 0, "⌀": 1, "---": 2}
@@ -75,8 +79,8 @@ def update_data():
     
     return df
 
-def display_table(search_query=""):
-    df = update_data()
+def display_table(search_query="", use_cache = False):
+    df = update_data(use_cache)
     if search_query:
         filtered_df = df[df["Model"].str.contains(search_query, case=False)]
     else:
@@ -116,8 +120,8 @@ def color_status(api_value, cell_value):
             return 'background-color: red'
     return ''
 
-def search_models(query):
-    return display_table(query)
+def search_models(query, use_cache = False):
+    return display_table(query, use_cache)
 
 description = """
 This is a space that retrieves the status of all supported HF LLM Serverless Inference APIs.
@@ -138,7 +142,7 @@ with gr.Blocks() as demo:
     gr.Markdown("## HF Serverless LLM Inference API Status")
     gr.Markdown(description)
     search_box = gr.Textbox(label="Search for a model", placeholder="Type model name here...")
-    table = gr.Dataframe(value=display_table(), headers="keys")
+    table = gr.Dataframe(value=display_table(True), headers="keys")
     
     search_box.change(fn=search_models, inputs=search_box, outputs=table)