Update app.py

app.py

@@ -10,7 +10,7 @@ import os
 
 app = FastAPI(title="PulmoProbe AI API")
 
-# Add CORS middleware to allow your Vercel app to call the API
+# Add CORS middleware to allow your frontend app to call the API
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -19,18 +19,21 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# --- Set the Hugging Face cache directory to a writable location ---
-# This ensures the application has permission to download and store the model.
-os.environ['HF_HOME'] = '/app/cache'
-print("Setting HF_HOME to /app/cache")
+# --- Set Hugging Face cache to a writable location ---
+os.environ['HF_HOME'] = '/tmp/huggingface'
+os.makedirs(os.environ['HF_HOME'], exist_ok=True)
+print(f"HF_HOME set to {os.environ['HF_HOME']}")
 
 # --- Download and Load Model from Hugging Face Hub ---
-# This points to your model repository
 MODEL_REPO_ID = "costaspinto/PulmoProbe"
 MODEL_FILENAME = "best_model.joblib"
 
 print("Downloading model from Hugging Face Hub...")
-model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILENAME)
+model_path = hf_hub_download(
+    repo_id=MODEL_REPO_ID,
+    filename=MODEL_FILENAME,
+    cache_dir=os.environ['HF_HOME']  # ensures container-safe caching
+)
 model = joblib.load(model_path)
 print("Model loaded successfully.")
 
@@ -50,7 +53,7 @@ class PatientData(BaseModel):
     other_cancer: int
     treatment_type: str
 
-# --- Define API Endpoints ---
+# --- API Endpoints ---
 @app.get("/")
 def read_root():
     return {"message": "Welcome to the PulmoProbe AI API"}
@@ -65,7 +68,7 @@ def predict(data: PatientData):
 
         return {
             "risk": risk_level,
-            "confidence": f"{confidence_high_risk * 100:.1f}"
+            "confidence": f"{confidence_high_risk * 100:.1f}%"
         }
     except Exception as e:
-        return {"error": str(e)}
+        return {"error": str(e)}