Update app.py

app.py

@@ -16,8 +16,8 @@ import io
 
 # ==== CONFIG ====
 REPO_ID = "MAS-AI-0000/GameNet-1"
-#MODEL_FILENAME = "GameNetModel.h5"
-MODEL_FILENAME = "GameNetModel.keras"
+MODEL_FILENAME = "GameNetModel.h5"
+#MODEL_FILENAME = "GameNetModel.keras"
 LABELS_FILENAME = "label_to_index.json"
 GENRE_FILENAME = "game_genre_map.json"
 IMG_SIZE = (300, 300)
@@ -58,21 +58,28 @@ class Prediction(BaseModel):
 @app.post("/predict", response_model=Prediction)
 async def predict(file: UploadFile = File(...)):
     try:
+        # Step 1: Load image
         image_bytes = await file.read()
         img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-        img = img.resize(IMG_SIZE)
+
+        # Step 2: Resize for EfficientNetB3 (300x300)
+        img = img.resize(IMG_SIZE, Image.Resampling.BICUBIC)
+
+        # Step 3: Convert to array and preprocess
         arr = img_to_array(img)
-        arr = preprocess_input(arr)
+        arr = preprocess_input(arr)  # normalize like in Colab
         arr = np.expand_dims(arr, axis=0)
 
+        # Step 4: Inference
         preds = model.predict(arr)
         class_idx = int(np.argmax(preds))
         confidence = float(np.max(preds))
 
-        game = index_to_label[class_idx]
+        # Step 5: Get label and genre
+        game = index_to_label.get(class_idx, "Unknown")
         genre = genre_map.get(game, "Unknown")
 
         return Prediction(game=game, genre=genre, confidence=confidence)
 
     except Exception as e:
-        return {"error": str(e)}
+        return JSONResponse(content={"error": str(e)}, status_code=500)