Update app.py

app.py

@@ -126,20 +126,41 @@ else:
 
 
 # ---------- DEFINE GRADIO INTERFACE ----------
+# --- UI (unchanged) ---
 iface = gr.Interface(
     fn=predict_from_json,
     inputs=gr.JSON(label="ratios JSON (dict of feature -> value)"),
     outputs="json",
     title="Static Fingerprint Model API",
-    description="POST your 21 ratios. Returns probabilities + predicted state.",
-    api_name="predict",        # <--- this line is what creates the API
+    description="POST your 21 ratios as a JSON dict. Returns probabilities + predicted state."
 )
 
-# ⚠️ Use gr.mount_gradio_app to make /gradio_api/ routes appear
-from fastapi import FastAPI
+# --- FastAPI app with a simple REST endpoint ---
+from fastapi import FastAPI, Request
+import gradio as gr
+
 app = FastAPI()
-app = gr.mount_gradio_app(app, iface, path="/")  # mount Gradio onto FastAPI root
+# Mount the Gradio UI at the root so your Space page still works
+app = gr.mount_gradio_app(app, iface, path="/")
+
+@app.post("/predict")
+async def api_predict(req: Request):
+    """
+    Accepts either:
+      1) raw dict:       {"autosuf_oper":1.2, ...}
+      2) gradio format:  {"data":[{...}]}
+    """
+    body = await req.json()
+    if isinstance(body, dict) and "data" in body and isinstance(body["data"], list) and body["data"]:
+        payload = body["data"][0]    # unwrap Gradio shape
+    elif isinstance(body, dict):
+        payload = body               # raw dict
+    else:
+        return {"error": "Invalid payload. Send a JSON object of feature->value OR {'data':[that_object]}"}
+
+    try:
+        return predict_from_json(payload)  # reuse your existing function
+    except Exception as e:
+        return {"error": f"{type(e).__name__}: {e}"}
 
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+# Spaces will auto-run with uvicorn; no need to call launch() here.