feat: added img_classifier

features/image_classifier/routes.py

@@ -1,26 +1,25 @@
 from slowapi import Limiter
 from config import ACCESS_RATE
-from fastapi import APIRouter, File, Request, Depends, HTTPException, UploadFile
+from fastapi import APIRouter, File, Request, Depends, UploadFile
 from fastapi.security import HTTPBearer
-from slowapi import Limiter
 from slowapi.util import get_remote_address
 from .controller import Classify_Image_router
+
 router = APIRouter()
 limiter = Limiter(key_func=get_remote_address)
 security = HTTPBearer()
 
+
 @router.post("/analyse")
 @limiter.limit(ACCESS_RATE)
 async def analyse(
-    request: Request,
-    file: UploadFile = File(...),
-    token: str = Depends(security)
+    request: Request, file: UploadFile = File(...), token: str = Depends(security)
 ):
     result = await Classify_Image_router(file)  # await the async function
     return result
 
+
 @router.get("/health")
 @limiter.limit(ACCESS_RATE)
 def health(request: Request):
     return {"status": "ok"}
-

features/nepali_text_classifier/inferencer.py

@@ -7,7 +7,9 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 def classify_text(text: str):
     model, tokenizer = get_model_tokenizer()
-    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)
+    inputs = tokenizer(
+        text, return_tensors="pt", padding=True, truncation=True, max_length=512
+    )
     inputs = {k: v.to(device) for k, v in inputs.items()}
 
     with torch.no_grad():
@@ -17,7 +19,7 @@ def classify_text(text: str):
         pred = torch.argmax(probs, dim=1).item()
         prob_percent = probs[0][pred].item() * 100
 
-    return {"label": "Human" if pred == 0 else "AI", "confidence": round(prob_percent, 2)}
-
-
-
+    return {
+        "label": "Human" if pred == 0 else "AI",
+        "confidence": round(prob_percent, 2),
+    }