Create app.py

app.py

@@ -0,0 +1,41 @@
+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"}