Update newapi.py

newapi.py

@@ -11,16 +11,15 @@ from huggingface_hub import hf_hub_download
 from models.TumorModel import TumorClassification, GliomaStageModel
 from utils import get_precautions_from_gemini
 
-# ✅ Writable cache directory inside project
-cache_dir = "./hf_cache"
+# ✅ Use Hugging Face's built-in writable cache directory
+cache_dir = "/home/user/.cache/huggingface"
 
-# ✅ Create the directory if it doesn't exist
-os.makedirs(cache_dir, exist_ok=True)
+# No need to call os.makedirs — directory already exists
 
 # Initialize FastAPI app
 app = FastAPI(title="Brain Tumor Detection API")
 
-# Enable CORS for all origins
+# Enable CORS
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -29,7 +28,7 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Load Tumor Classification Model
+# Load tumor classification model
 btd_model_path = hf_hub_download(
     repo_id="Codewithsalty/brain-tumor-models",
     filename="BTD_model.pth",
@@ -39,7 +38,7 @@ tumor_model = TumorClassification()
 tumor_model.load_state_dict(torch.load(btd_model_path, map_location="cpu"))
 tumor_model.eval()
 
-# Load Glioma Stage Prediction Model
+# Load glioma stage model
 glioma_model_path = hf_hub_download(
     repo_id="Codewithsalty/brain-tumor-models",
     filename="glioma_stages.pth",
@@ -49,7 +48,7 @@ glioma_model = GliomaStageModel()
 glioma_model.load_state_dict(torch.load(glioma_model_path, map_location="cpu"))
 glioma_model.eval()
 
-# Image preprocessing pipeline
+# Image preprocessing
 transform = transforms.Compose([
     transforms.Grayscale(),
     transforms.Resize((224, 224)),
@@ -57,15 +56,13 @@ transform = transforms.Compose([
     transforms.Normalize(mean=[0.5], std=[0.5]),
 ])
 
-# Health check
 @app.get("/")
 async def root():
     return {"message": "Brain Tumor Detection API is running."}
 
-# Tumor type labels
+# Labels
 labels = ['glioma', 'meningioma', 'notumor', 'pituitary']
 
-# Predict tumor type from image
 @app.post("/predict-image")
 async def predict_image(file: UploadFile = File(...)):
     img_bytes = await file.read()
@@ -83,7 +80,7 @@ async def predict_image(file: UploadFile = File(...)):
         precautions = get_precautions_from_gemini(tumor_type)
         return {"tumor_type": tumor_type, "precaution": precautions}
 
-# Mutation input model
+# Mutation input
 class MutationInput(BaseModel):
     gender: str
     age: float
@@ -95,7 +92,6 @@ class MutationInput(BaseModel):
     cic: int
     pik3ca: int
 
-# Predict glioma stage
 @app.post("/predict-glioma-stage")
 async def predict_glioma_stage(data: MutationInput):
     gender_val = 0 if data.gender.lower() == 'm' else 1
@@ -111,7 +107,7 @@ async def predict_glioma_stage(data: MutationInput):
         stages = ['Stage 1', 'Stage 2', 'Stage 3', 'Stage 4']
         return {"glioma_stage": stages[idx]}
 
-# Run locally (ignored on Spaces, used only for dev/testing)
+# For local development only
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run("newapi:app", host="0.0.0.0", port=10000)