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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from fastapi.responses import JSONResponse
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import os
import re
import logging
app = FastAPI()
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(_name_)
# Set the cache directory for Hugging Face
os.environ['TRANSFORMERS_CACHE'] = os.getenv('TRANSFORMERS_CACHE', '/app/cache')
# Load model and tokenizer
model_name = "Bijoy09/MObilebert"
try:
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
logger.info("Model and tokenizer loaded successfully")
except Exception as e:
logger.error(f"Failed to load model or tokenizer: {e}")
raise RuntimeError(f"Failed to load model or tokenizer: {e}")
class TextRequest(BaseModel):
text: str
class BatchTextRequest(BaseModel):
texts: list[str]
# Regular expression to detect Bangla characters
bangla_regex = re.compile('[\u0980-\u09FF]')
def contains_bangla(text):
return bool(bangla_regex.search(text))
def remove_non_bangla(text):
return ''.join(bangla_regex.findall(text))
@app.post("/batch_predict/")
async def batch_predict(request: BatchTextRequest):
try:
model.eval()
# Prepare the batch results
results = []
for idx, text in enumerate(request.texts):
logger.info(f" texts: {text}")
# Check if text contains Bangla characters
if not contains_bangla(text):
results.append({"id": idx + 1, "text": text, "prediction": "other"})
continue
# Remove non-Bangla characters
modified_text = remove_non_bangla(text)
ogger.info(f"modified text: {modified_text}")
# Encode and predict for texts containing Bangla characters
inputs = tokenizer.encode_plus(
modified_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()
label = "Spam" if prediction == 1 else "Ham"
results.append({"id": idx + 1, "text": text, "prediction": label})
logger.info(f"Batch prediction results: {results}")
return JSONResponse(content={"results": results}, media_type="application/json; charset=utf-8")
except Exception as e:
logger.error(f"Batch prediction failed: {e}")
raise HTTPException(status_code=500, detail="Batch prediction failed. Please try again.")
@app.get("/")
async def root():
return {"message": "Welcome to the MobileBERT API"} |