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80741e2 | 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 | from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F
app = FastAPI()
model_path = "results"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
model.eval()
class TextInput(BaseModel):
text: str
@app.post("/predict")
def predict(input: TextInput):
inputs = tokenizer(
input.text,
return_tensors="pt",
truncation=True,
padding=True,
max_length=256
)
with torch.no_grad():
outputs = model(**inputs)
probs = F.softmax(outputs.logits, dim=1)
pred = torch.argmax(probs, dim=1).item()
confidence = (probs[0][pred].item())*100
label = "POSITIVE" if pred == 1 else "NEGATIVE"
return {
"prediction": label,
"confidence": round(confidence, 4)
} |