Create app.py

app.py

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+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from huggingface_hub import hf_hub_download
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.applications.efficientnet import preprocess_input
+
+from PIL import Image
+import json
+import io
+
+# ==== CONFIG ====
+REPO_ID = "MAS-AI-0000/GameNet-1"
+MODEL_FILENAME = "GameNetModel.h5"
+LABELS_FILENAME = "label_to_index.json"
+GENRE_FILENAME = "game_genre_map.json"
+IMG_SIZE = (300, 300)
+
+# ==== Load assets ====
+model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME)
+labels_path = hf_hub_download(repo_id=REPO_ID, filename=LABELS_FILENAME)
+genre_path = hf_hub_download(repo_id=REPO_ID, filename=GENRE_FILENAME)
+
+model = load_model(model_path)
+
+with open(labels_path, "r") as f:
+    label_to_index = json.load(f)
+index_to_label = {v: k for k, v in label_to_index.items()}
+
+with open(genre_path, "r") as f:
+    genre_map = json.load(f)
+
+# ==== FastAPI Setup ====
+app = FastAPI()
+
+# Optional: CORS if frontend is on different domain
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # change this in production
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Response schema
+class Prediction(BaseModel):
+    game: str
+    genre: str
+    confidence: float
+
+# Inference route
+@app.post("/predict", response_model=Prediction)
+async def predict(file: UploadFile = File(...)):
+    try:
+        image_bytes = await file.read()
+        img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        img = img.resize(IMG_SIZE)
+        arr = img_to_array(img)
+        arr = preprocess_input(arr)
+        arr = np.expand_dims(arr, axis=0)
+
+        preds = model.predict(arr)
+        class_idx = int(np.argmax(preds))
+        confidence = float(np.max(preds))
+
+        game = index_to_label[class_idx]
+        genre = genre_map.get(game, "Unknown")
+
+        return Prediction(game=game, genre=genre, confidence=confidence)
+
+    except Exception as e:
+        return {"error": str(e)}