Add base64 API endpoint for direct backend integration

- Added classify_base64 function to accept base64 images directly
- Exposed as API-only endpoint using gr.Interface with hidden UI
- Added stable API names to existing endpoints
- Enables vision backend to call Space without file uploads
- Based on GPT recommendation in codexmis.md

app.py

@@ -268,14 +268,16 @@ with gr.Blocks(title="MobileCLIP Image Classifier") as demo:
         classify_btn.click(
             fn=classify_image,
             inputs=[input_image, top_k_slider],
-            outputs=[output_text, output_chart]
+            outputs=[output_text, output_chart],
+            api_name="classify_image"
         )
         
         # Also trigger on image upload
         input_image.change(
             fn=classify_image,
             inputs=[input_image, top_k_slider],
-            outputs=[output_text, output_chart]
+            outputs=[output_text, output_chart],
+            api_name="classify_image_1"
         )
     
     with gr.Tab("🔧 Admin Panel"):
@@ -322,7 +324,8 @@ with gr.Blocks(title="MobileCLIP Image Classifier") as demo:
             upsert_btn.click(
                 fn=upsert_labels_admin,
                 inputs=[admin_token_input, new_items_input],
-                outputs=upsert_output
+                outputs=upsert_output,
+                api_name="upsert_labels_admin"
             )
         
         with gr.Accordion("🔄 Reload Label Version", open=False):
@@ -411,6 +414,40 @@ with gr.Blocks(title="MobileCLIP Image Classifier") as demo:
 
 print("Gradio interface created successfully!")
 
+# Add pure API endpoint for base64 classification (as suggested by GPT)
+def classify_base64(image_b64: str, top_k: int = 10):
+    """
+    API-only endpoint that accepts base64 images directly.
+    This enables direct API calls from backends without file uploads.
+    """
+    if handler is None:
+        return {"error": "handler not initialized"}
+    
+    try:
+        # Call handler directly with base64
+        result = handler({
+            "inputs": {
+                "image": image_b64,
+                "top_k": int(top_k)
+            }
+        })
+        return result
+    except Exception as e:
+        return {"error": str(e)}
+
+# Register the API endpoint (no UI)
+with demo:
+    gr.Interface(
+        fn=classify_base64,
+        inputs=[
+            gr.Textbox(label="image_b64", visible=False),
+            gr.Number(label="top_k", visible=False)
+        ],
+        outputs=gr.JSON(visible=False),
+        api_name="classify_base64",
+        visible=False
+    )
+
 if __name__ == "__main__":
     print("Launching Gradio app...")
     demo.launch()