Create app.py

app.py

@@ -0,0 +1,60 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from typing import List
+
+app = FastAPI(
+    title="Indonesian Sentiment API",
+    version="1.0"
+)
+
+MODEL_NAME = "taufiqdp/indonesian-sentiment"
+
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_NAME,
+    trust_remote_code=True
+)
+model = AutoModelForSequenceClassification.from_pretrained(
+    MODEL_NAME,
+    trust_remote_code=True
+)
+model.eval()
+
+labels = ["negatif", "netral", "positif"]
+
+# 🔥 ROOT ENDPOINT (WAJIB ADA)
+@app.get("/")
+def root():
+    return {"status": "ok", "message": "Sentiment API is running"}
+
+# Schema batch
+class InputBatch(BaseModel):
+    texts: List[str]
+
+@app.post("/predict-batch")
+def predict_batch(data: InputBatch):
+    results = []
+
+    inputs = tokenizer(
+        data.texts,
+        return_tensors="pt",
+        truncation=True,
+        padding=True,
+        max_length=128
+    )
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    probs = torch.softmax(outputs.logits, dim=1)
+    preds = torch.argmax(probs, dim=1)
+
+    for text, idx, prob in zip(data.texts, preds, probs):
+        results.append({
+            "text": text,
+            "sentiment": labels[idx],
+            "score": round(prob[idx].item(), 4)
+        })
+
+    return {"results": results}