Fix HF Spaces configuration: README frontmatter, Dockerfile Python version, and model loading from Hub

Dockerfile

@@ -1,5 +1,5 @@
-# Gunakan image Python 3.9 atau 3.10
-FROM python:3.13.11
+# Gunakan image Python 3.10
+FROM python:3.10-slim
 
 # Set working directory awal
 WORKDIR /code

README.md

@@ -1,10 +1,22 @@
-j---
+---
 title: Batik Classifier
-emoji: 🔥
+emoji: 🎨
 colorFrom: blue
 colorTo: green
 sdk: docker
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🎨 Batik Classifier
+
+Klasifikasi Motif Batik Indonesia menggunakan Model VGG16 Deep Learning.
+
+## Features
+
+- Upload gambar batik untuk mengetahui motif dan asalnya
+- Mendukung 111 motif batik dari berbagai daerah di Indonesia
+- Top-5 prediction dengan confidence score
+
+## Model
+
+Model: [RimsJ/Batik-Classifier](https://huggingface.co/RimsJ/Batik-Classifier)

app.py

@@ -10,6 +10,8 @@ from PIL import Image
 import json
 import numpy as np
 from typing import Tuple, Dict
+from huggingface_hub import hf_hub_download
+import os
 
 # Global variables
 model = None
@@ -37,8 +39,15 @@ def load_model():
         model.classifier[3] = nn.Linear(4096, num_classes)
         model.classifier = nn.Sequential(*list(model.classifier.children())[:4])
 
+        # Download model from Hugging Face Hub
+        print("📥 Downloading model from Hugging Face Hub...")
+        model_path = hf_hub_download(
+            repo_id="RimsJ/Batik-Classifier",
+            filename="vgg16_batik_best.pth"
+        )
+
         # Load trained weights
-        checkpoint = torch.load('models/vgg16_batik_best.pth', map_location=device)
+        checkpoint = torch.load(model_path, map_location=device)
 
         # Extract state_dict
         if isinstance(checkpoint, dict) and 'model_state_dict' in checkpoint:

requirements.txt

@@ -7,3 +7,4 @@ torch==2.6.0
 torchvision==0.21.0
 Pillow==11.1.0
 numpy==2.2.2
+huggingface_hub