Create app.py

app.py

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+import os
+import gradio as gr
+import pandas as pd
+from huggingface_hub import InferenceClient
+from threading import Timer
+
+HUGGINGFACE_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")
+def get_available_free():
+    models = InferenceClient().list_deployed_models("text-generation-inference")['text-generation']
+    models_conclusion = {
+        "Model": [],
+        "API": [],
+        "Text Completion": [],
+        "Chat Completion": []
+    }
+    for m in models:
+        text_available = False
+        chat_available = False
+        pro_sub = False
+        try:
+            InferenceClient(m, timeout=10, token="HUGGINGFACE_TOKEN").text_generation("Hi.", max_new_tokens=1)
+            text_available = True
+            InferenceClient(m, timeout=10, token="HUGGINGFACE_TOKEN").chat_completion(messages=[{'role': 'user', 'content': 'Hi.'}], max_tokens=1)
+            chat_available = True
+        except Exception as e:
+            print(e)
+            if e and "Model requires a Pro subscription" in str(e):
+                pro_sub = True
+            if e and "Rate limit reached" in str(e):
+                print("Rate Limited!!")
+                if os.path.exists("data.csv"):
+                    print("Loading data from file...")
+                    return pd.read_csv("data.csv").to_dict(orient='list')
+                return []
+        models_conclusion["Model"].append(m)
+        models_conclusion["API"].append("Free" if chat_available or text_available else ("Pro Subscription" if pro_sub else "Not Responding"))
+        models_conclusion["Chat Completion"].append("---" if (pro_sub or (not chat_available and not text_available)) else ("✓" if chat_available else "⌀"))
+        models_conclusion["Text Completion"].append("---" if (pro_sub or (not chat_available and not text_available)) else ("✓" if text_available else "⌀"))
+    pd.DataFrame(models_conclusion).to_csv("data.csv", index=False)
+    return models_conclusion
+
+def update_data():
+    data = get_available_free()
+    df = pd.DataFrame(data)
+    return df
+
+def display_table(search_query=""):
+    df = update_data()
+    if search_query:
+        filtered_df = df[df["Model"].str.contains(search_query, case=False)]
+    else:
+        filtered_df = df
+    
+    styled_df = filtered_df.style.apply(apply_row_styles, axis=1, subset=["Model", "API", "Text Completion", "Chat Completion"])
+    return styled_df
+
+def apply_row_styles(row):
+    api_value = row["API"]
+    return [
+        color_status(api_value, row["Model"]),
+        color_status(api_value, row["API"]),
+        color_status(api_value, row["Text Completion"]),
+        color_status(api_value, row["Chat Completion"])
+    ]
+
+def color_status(api_value, cell_value):
+    if cell_value == "---":
+        if api_value == "Free":
+            return 'background-color: green'
+        elif api_value == "Pro Subscription":
+            return 'background-color: blue'
+        elif api_value == "Not Responding":
+            return 'background-color: red'
+    else:
+        if cell_value == "Free":
+            return 'background-color: green'
+        elif cell_value == "Pro Subscription":
+            return 'background-color: blue'
+        elif cell_value == "Not Responding":
+            return 'background-color: red'
+        elif cell_value == "✓":
+            return 'background-color: green'
+        elif cell_value == "⌀":
+            return 'background-color: red'
+    return ''
+
+def search_models(query):
+    return display_table(query)
+
+description = "This is a space that retrieves the status of all supported HF LLM Serverless Inference APIs.\nUpdates every 2 hours."
+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")
+    
+    search_box.change(fn=search_models, inputs=search_box, outputs=table)
+    
+    def update_every_two_hours():
+        search_models(search_box.value)
+        Timer(7200, update_every_two_hours).start()
+        
+    Timer(7200, update_every_two_hours).start()
+    
+demo.launch()