Update API documentation to clarify queue system requirements

app.py

@@ -28,40 +28,11 @@ with gr.Blocks(title="Nomic Text Embeddings") as app:
     text_input.submit(embed, inputs=text_input, outputs=output)
     
     # Add API usage guide
-    gr.Markdown("## API Usage with cURL")
+    gr.Markdown("## API Usage")
     gr.Markdown("""
-    You can use this API programmatically with cURL. Here are two methods:
+    You can use this API programmatically. Hugging Face Spaces use a queue system for API calls.
     
-    ### Method 1: Using the Gradio Client API
-    ```bash
-    curl -X POST https://ipepe-nomic-embeddings.hf.space/api/predict \
-      -H "Content-Type: application/json" \
-      -d '{
-        "fn_index": 0,
-        "data": ["Your text to embed goes here"]
-      }'
-    ```
-    
-    ### Method 2: Direct API endpoint
-    ```bash
-    curl -X POST https://ipepe-nomic-embeddings.hf.space/run/predict \
-      -H "Content-Type: application/json" \
-      -d '{
-        "data": ["Your text to embed goes here"]
-      }'
-    ```
-    
-    The response will be in JSON format:
-    ```json
-    {
-      "data": [[0.123, -0.456, 0.789, ...]], 
-      "duration": 0.123
-    }
-    ```
-    
-    Replace `https://ipepe-nomic-embeddings.hf.space` with your actual Space URL if different.
-    
-    ### Python Example
+    ### Recommended: Python Client
     ```python
     from gradio_client import Client
     
@@ -72,6 +43,32 @@ with gr.Blocks(title="Nomic Text Embeddings") as app:
     )
     print(result)  # Returns the embedding array
     ```
+    
+    ### Using cURL (requires session handling)
+    For direct HTTP requests, you need to:
+    1. Join the queue
+    2. Poll for results
+    
+    This is more complex with cURL. Here's a simplified example using the Gradio Python client instead:
+    
+    ```bash
+    # Install the client first
+    pip install gradio_client
+    
+    # Then use Python
+    python -c "from gradio_client import Client; client = Client('ipepe/nomic-embeddings'); print(client.predict('Your text here', api_name='/predict'))"
+    ```
+    
+    ### JavaScript/Node.js Example
+    ```javascript
+    import { client } from "@gradio/client";
+    
+    const app = await client("ipepe/nomic-embeddings");
+    const result = await app.predict("/predict", ["Your text to embed goes here"]);
+    console.log(result.data);
+    ```
+    
+    The response will contain the embedding array as a list of floats.
     """)
 
 if __name__ == '__main__':