Update app.py

app.py

@@ -1,20 +1,37 @@
 from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 import torch
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import os
+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')
 
+# Enable CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 # 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):
@@ -23,6 +40,7 @@ class TextRequest(BaseModel):
 @app.post("/predict/")
 async def predict(request: TextRequest):
     try:
+        logger.info(f"Received text: {request.text}")
         model.eval()
         inputs = tokenizer.encode_plus(
             request.text,
@@ -33,11 +51,14 @@ async def predict(request: TextRequest):
             return_attention_mask=True,
             return_tensors='pt'
         )
+        logger.info(f"Tokenized inputs: {inputs}")
         with torch.no_grad():
             logits = model(inputs['input_ids'], attention_mask=inputs['attention_mask']).logits
+            logger.info(f"Model logits: {logits}")
             prediction = torch.argmax(logits, dim=1).item()
         return {"prediction": "Spam" if prediction == 1 else "Ham"}
     except Exception as e:
+        logger.error(f"Prediction failed: {e}")
         raise HTTPException(status_code=500, detail=f"Prediction failed: {e}")
 
 @app.get("/")