menambahkan keras load model from jso

app.py

@@ -1,47 +1,44 @@
 import os
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 import keras
-print("keras versio:", keras.__version__)
 import gradio as gr
 import numpy as np
 from keras.preprocessing import image
-from Model_Load import load_model_from_files
-from description import description
-from location import location
 from fastapi import FastAPI, File, UploadFile
 from fastapi.responses import JSONResponse
 from io import BytesIO
 from PIL import Image
-from tensorflow.keras.models import model_from_json
 import tensorflow as tf
 import logging
 from fastapi.middleware.cors import CORSMiddleware
-from keras.models import model_from_json
+from tensorflow.keras.models import load_model
 
-def load_model_from_files(json_path, weights_path):
-    with open(json_path, "r") as json_file:
-        loaded_model_json = json_file.read()
-    model = model_from_json(loaded_model_json) 
-    model.load_weights(weights_path)
-    return model
+# Import deskripsi dan lokasi
+from description import description
+from location import location
 
 # Nonaktifkan GPU (jika tidak digunakan)
 tf.config.set_visible_devices([], 'GPU')
 
 # Inisialisasi logger
 logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+logger = logging.getLogger(_name_)
+
+# Fungsi memuat model dari file .h5 langsung
+def load_model_from_file(h5_path):
+    return load_model(h5_path)
 
-# Load model dan label
-model = load_model_from_files("model.json", "my_model.h5")
+# Load model
+model = load_model_from_file("my_model.h5")
 
+# Daftar label
 labels = [
     "Benteng Vredeburg", "Candi Borobudur", "Candi Prambanan", "Gedung Agung Istana Kepresidenan",
     "Masjid Gedhe Kauman", "Monumen Serangan 1 Maret", "Museum Gunungapi Merapi",
     "Situs Ratu Boko", "Taman Sari", "Tugu Yogyakarta"
 ]
 
-# Fungsi preprocessing dan prediksi
+# Fungsi klasifikasi
 def classify_image(img):
     img = img.resize((224, 224))
     img_array = image.img_to_array(img)
@@ -81,7 +78,7 @@ def create_app():
 
     app.add_middleware(
         CORSMiddleware,
-        allow_origins=["http://localhost:9000"],  # atau sesuaikan dengan asal frontend
+        allow_origins=["http://localhost:9000"],  # atau sesuaikan
         allow_credentials=True,
         allow_methods=["*"],
         allow_headers=["*"],
@@ -115,8 +112,8 @@ def create_app():
     app = gr.mount_gradio_app(app, gradio_app, path="/gradio")
     return app
 
-# Hanya jalan jika dijalankan langsung, bukan import
-if __name__ == "__main__":
+# Run server jika dijalankan langsung
+if _name_ == "_main_":
     import uvicorn
     app = create_app()
-    uvicorn.run(app, host="127.0.0.1", port=8000)
+    uvicorn.run(app, host="127.0.0.1", port=8000)