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21b49f2 | 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 | import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from fastapi import FastAPI
from pydantic import BaseModel
# ----------------- Load model once -----------------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = "facebook/bart-large-mnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)
model.eval()
temperature = 1.2
# ----------------- FastAPI app -----------------
app = FastAPI(title="Text Classification API")
class InputData(BaseModel):
text: str
label: str
def classify(sequence, label):
hypothesis = f"This example is {label}."
inputs = tokenizer(
sequence,
hypothesis,
return_tensors="pt",
truncation=True,
max_length=512
).to(device)
with torch.no_grad():
logits = model(**inputs).logits
idx = torch.tensor([0, 2], device=logits.device)
logits = logits.index_select(1, idx)
probs = torch.softmax(logits / temperature, dim=1)
entropy = -torch.sum(probs * torch.log(probs + 1e-8))
if entropy > 0.8:
return "Uncertain"
return round(probs[0, 1].item(), 3)
# ----------------- API Endpoint -----------------
@app.post("/predict")
def predict(data: InputData):
result = classify(data.text, data.label)
return {"probability": result} |