Update app.py

app.py

@@ -1,5 +1,4 @@
 from fastapi import FastAPI, UploadFile, File, HTTPException
-from fastapi.responses import JSONResponse
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing import image
 import numpy as np
@@ -8,46 +7,38 @@ import io
 
 app = FastAPI(title="OralScan Model API")
 
-# Load the model when the app starts
-model = None
-
-@app.on_event("startup")
-async def load_model_on_startup():
-    global model
-    try:
-        model = load_model("model.keras")
-        print("✅ MobileNetV2 model loaded successfully!")
-    except Exception as e:
-        print(f"❌ Error loading model: {e}")
+# Load the model globally (simpler method for HF Spaces)
+try:
+    model = load_model("model.keras")
+    print("✅ MobileNetV2 model loaded successfully!")
+except Exception as e:
+    print(f"❌ Failed to load model: {e}")
+    model = None
 
 @app.get("/")
 def home():
+    if model is None:
+        return {"message": "API is running but model failed to load"}
     return {"message": "OralScan Model API is running! Upload image to /predict"}
 
 @app.post("/predict")
 async def predict(file: UploadFile = File(...)):
     if model is None:
-        raise HTTPException(status_code=500, detail="Model not loaded yet")
+        raise HTTPException(status_code=500, detail="Model failed to load. Please check logs.")
 
     try:
-        # Read uploaded image
         contents = await file.read()
         img = Image.open(io.BytesIO(contents)).convert("RGB")
-        
-        # Resize to 224x224
         img = img.resize((224, 224))
         
-        # Preprocess
         img_array = image.img_to_array(img)
         img_array = np.expand_dims(img_array, axis=0)
         img_array = img_array / 255.0
 
-        # Predict
-        predictions = model.predict(img_array)
+        predictions = model.predict(img_array, verbose=0)
         predicted_class = int(np.argmax(predictions[0]))
         confidence = float(np.max(predictions[0]) * 100)
 
-        # Your actual class names
         class_names = [
             "Oral Homogenous Leukoplakia",
             "Oral Non-Homogenous Leukoplakia",