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from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
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 numpy as np
import json
import io
# Constants
IMG_SIZE = (300, 300)
MODEL_PATH = "GameNetModel.h5"
LABEL_MAP_PATH = "label_to_index.json"
GENRE_MAP_PATH = "game_genre_map.json"
# Load model & mappings
model = load_model(MODEL_PATH)
with open(LABEL_MAP_PATH) as f:
label_to_index = json.load(f)
index_to_label = {v: k for k, v in label_to_index.items()}
with open(GENRE_MAP_PATH) as f:
genre_map = json.load(f)
# Initialize FastAPI app
app = FastAPI()
# Enable CORS (important for frontend calls)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
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)}
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