File size: 1,357 Bytes
dd4e7ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | 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}
|