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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
app = FastAPI()
# Load model and tokenizer
model_name = "Bijoy09/your_mobilebert_model_repo" # replace with your Hugging Face repo name
try:
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
except Exception as e:
raise RuntimeError(f"Failed to load model or tokenizer: {e}")
class TextRequest(BaseModel):
text: str
@app.post("/predict/")
async def predict(request: TextRequest):
try:
model.eval()
inputs = tokenizer.encode_plus(
request.text,
add_special_tokens=True,
max_length=64,
truncation=True,
padding='max_length',
return_attention_mask=True,
return_tensors='pt'
)
with torch.no_grad():
logits = model(inputs['input_ids'], attention_mask=inputs['attention_mask']).logits
prediction = torch.argmax(logits, dim=1).item()
return {"prediction": "Spam" if prediction == 1 else "Ham"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Prediction failed: {e}")
@app.get("/")
async def root():
return {"message": "Welcome to the MobileBERT API"}
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