third commit

app.py

@@ -1,10 +1,12 @@
 import gradio as gr
 from transformers import pipeline
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
 
-# Gunakan model IndoBERT yang sudah fine-tuned untuk sentimen
+# Gunakan IndoBERT sentiment model
 analyzer = pipeline(
     "sentiment-analysis",
-    model="w11wo/indonesian-roberta-base-sentiment-classifier"
+    model="indobenchmark/indobert-base-p1"
 )
 
 def predict_sentiment(text):
@@ -13,12 +15,31 @@ def predict_sentiment(text):
     score = result['score']
     return f"Sentimen: {label}\nSkor: {score:.2f}"
 
+# Gradio UI (masih bisa diakses di web)
 demo = gr.Interface(
     fn=predict_sentiment,
     inputs=gr.Textbox(label="Masukkan teks bahasa Indonesia"),
     outputs="text",
     title="Analisis Sentimen Bahasa Indonesia",
-    description="Gunakan IndoBERT-RoBERTa untuk mendeteksi sentimen positif / negatif / netral"
+    description="Gunakan IndoBERT untuk mendeteksi sentimen positif / negatif / netral"
 )
 
-demo.launch()
+# FastAPI untuk REST API langsung
+app = FastAPI()
+
+@app.post("/predict")
+async def api_predict(request: Request):
+    body = await request.json()
+    text = body.get("text")
+    if not text:
+        return JSONResponse({"error": "Field 'text' wajib ada"}, status_code=400)
+
+    result = predict_sentiment(text)
+    return {"result": result}
+
+# Hubungkan Gradio dan FastAPI
+gr.mount_gradio_app(app, demo, path="/")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)