Spaces:
Running
Running
File size: 2,574 Bytes
646908a 69cb162 646908a fac322e 646908a fac322e 646908a fac322e 646908a fac322e 646908a fac322e 646908a fac322e 646908a fac322e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from huggingface_hub import hf_hub_download
from pydantic import BaseModel
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"
#MODEL_FILENAME = "GameNetModel.keras"
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:
# Step 1: Load image
image_bytes = await file.read()
img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
# 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) # 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))
# 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 JSONResponse(content={"error": str(e)}, status_code=500)
|