Update newapi.py

newapi.py

@@ -5,17 +5,20 @@ import torch
 from torchvision import transforms
 from PIL import Image
 import io
+import os
 from huggingface_hub import hf_hub_download
 
 from models.TumorModel import TumorClassification, GliomaStageModel
 from utils import get_precautions_from_gemini
 
-# ✅ Let Hugging Face handle cache automatically — DO NOT manually create any folders
+# ✅ Create a writable cache directory inside the current working directory
+cache_dir = os.path.join(os.getcwd(), "cache")
+os.makedirs(cache_dir, exist_ok=True)
 
-# Initialize FastAPI app
+# ✅ Initialize FastAPI app
 app = FastAPI(title="Brain Tumor Detection API")
 
-# Enable CORS for all origins
+# ✅ Enable CORS for frontend requests
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -24,25 +27,27 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# ✅ Load Tumor Classification Model from Hugging Face
+# ✅ Load Tumor Classification Model
 btd_model_path = hf_hub_download(
     repo_id="Codewithsalty/brain-tumor-models",
-    filename="BTD_model.pth"
+    filename="BTD_model.pth",
+    cache_dir=cache_dir
 )
 tumor_model = TumorClassification()
 tumor_model.load_state_dict(torch.load(btd_model_path, map_location="cpu"))
 tumor_model.eval()
 
-# ✅ Load Glioma Stage Prediction Model from Hugging Face
+# ✅ Load Glioma Stage Model
 glioma_model_path = hf_hub_download(
     repo_id="Codewithsalty/brain-tumor-models",
-    filename="glioma_stages.pth"
+    filename="glioma_stages.pth",
+    cache_dir=cache_dir
 )
 glioma_model = GliomaStageModel()
 glioma_model.load_state_dict(torch.load(glioma_model_path, map_location="cpu"))
 glioma_model.eval()
 
-# Image preprocessing pipeline
+# ✅ Image preprocessing steps
 transform = transforms.Compose([
     transforms.Grayscale(),
     transforms.Resize((224, 224)),
@@ -50,19 +55,19 @@ transform = transforms.Compose([
     transforms.Normalize(mean=[0.5], std=[0.5]),
 ])
 
-# Health check endpoint
+# ✅ Health check route
 @app.get("/")
 async def root():
     return {"message": "Brain Tumor Detection API is running."}
 
-# Tumor type labels
+# ✅ Tumor labels
 labels = ['glioma', 'meningioma', 'notumor', 'pituitary']
 
-# Predict tumor type from uploaded image
+# ✅ Predict tumor type
 @app.post("/predict-image")
 async def predict_image(file: UploadFile = File(...)):
     img_bytes = await file.read()
-    img = Image.open(io.BytesIO(img_bytes)).convert("L")  # Ensure grayscale
+    img = Image.open(io.BytesIO(img_bytes)).convert("L")
     x = transform(img).unsqueeze(0)
 
     with torch.no_grad():
@@ -76,7 +81,7 @@ async def predict_image(file: UploadFile = File(...)):
         precautions = get_precautions_from_gemini(tumor_type)
         return {"tumor_type": tumor_type, "precaution": precautions}
 
-# Input model for glioma mutation data
+# ✅ Input format for glioma prediction
 class MutationInput(BaseModel):
     gender: str
     age: float
@@ -88,7 +93,7 @@ class MutationInput(BaseModel):
     cic: int
     pik3ca: int
 
-# Predict glioma stage based on mutations
+# ✅ 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
@@ -104,4 +109,7 @@ async def predict_glioma_stage(data: MutationInput):
         stages = ['Stage 1', 'Stage 2', 'Stage 3', 'Stage 4']
         return {"glioma_stage": stages[idx]}
 
-# ✅ No need to run uvicorn manually in Hugging Face Spaces
+# ✅ Optional: Only used when running locally (ignored on Spaces)
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("newapi:app", host="0.0.0.0", port=10000)