Update app.py

app.py

@@ -1,26 +1,31 @@
-import gradio as gr
+from fastapi import FastAPI
+from pydantic import BaseModel
 from transformers import pipeline
+from fastapi.middleware.cors import CORSMiddleware
 
-# Use a lighter text dialect classifier
+# Load the Arabic dialect classifier
 model_name = "IbrahimAmin/marbertv2-arabic-written-dialect-classifier"
 dialect = pipeline("text-classification", model=model_name)
 
-def detect_dialect(text):
-    if not text:
-        return "No input"
-    result = dialect(text)[0]
-    return f"{result['label']} ({round(result['score'], 3)})"
+# FastAPI app
+app = FastAPI(title="Arabic Dialect Detector API")
 
-iface = gr.Interface(
-    fn=detect_dialect,
-    inputs=gr.Textbox(label="Arabic text"),
-    outputs="text",
-    title="Arabic Dialect Detector",
+# Allow CORS (so n8n or browser can call it)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"]
 )
 
-# ✅ Launch with proper API + no SSR + queue enabled
-iface.launch(
-    share=True,      # generates public URL
-    server_name="0.0.0.0",
-    ssr_mode=False    # disable SSR
-)
+# Input schema
+class InputText(BaseModel):
+    text: str
+
+# API endpoint
+@app.post("/predict")
+def predict(input: InputText):
+    if not input.text:
+        return {"label": None, "score": None, "error": "No input text"}
+    result = dialect(input.text)[0]
+    return {"label": result['label'], "score": round(result['score'], 3)}