melakukan perubahan pada app.py menyesuaikan library dan menginstal ulang requirements.txt

app.py

@@ -1,35 +1,51 @@
 import os
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-import keras
+
 import gradio as gr
 import numpy as np
-from keras.preprocessing import image
+from tensorflow.keras.preprocessing import image
 from fastapi import FastAPI, File, UploadFile
 from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
 from io import BytesIO
 from PIL import Image
 import tensorflow as tf
 import logging
-from fastapi.middleware.cors import CORSMiddleware
-from tensorflow.keras.models import load_model
+
+from tensorflow.keras.models import load_model, model_from_json
+from tensorflow.keras import mixed_precision
+from tensorflow.keras.saving import get_custom_objects, register_keras_serializable
+from tensorflow.keras.mixed_precision import Policy
+
+# @register_keras_serializable(package="keras")
+# class DTypePolicy(Policy):
+#     pass
+
+# from tensorflow.keras.saving import get_custom_objects
+# get_custom_objects()["DTypePolicy"] = DTypePolicy
 
 # Import deskripsi dan lokasi
 from description import description
 from location import location
 
 # Nonaktifkan GPU (jika tidak digunakan)
-tf.config.set_visible_devices([], 'GPU')
+# tf.config.set_visible_devices([], 'GPU')
 
 # Inisialisasi logger
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
 
-# Fungsi memuat model dari file .h5 langsung
-def load_model_from_file(h5_path):
-    return load_model(h5_path)
+# ========== Fungsi Load Model dari File JSON + H5 ==========
+def load_model_from_file(json_path, h5_path):
+    with open(json_path, "r") as f:
+        json_config = f.read()
+        model = model_from_json(json_config)
+    model.load_weights(h5_path)
+    return model
+
+# ========== Load Model ==========
+model = load_model_from_file("model.json", "my_model.h5")
 
-# Load model
-model = load_model_from_file("my_model.h5")
 
 # Daftar label
 labels = [
@@ -40,71 +56,77 @@ labels = [
 
 # Fungsi klasifikasi
 def classify_image(img):
-    img = img.resize((224, 224))
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array = img_array / 255.0
-
-    pred = model.predict(img_array)[0]
-    confidence = np.max(pred)
-    predicted_label = labels[np.argmax(pred)]
-
-    akurasi = float(confidence)
-    if confidence < 0.8:
-        label_output = "Tidak dapat dikenali (Confidence: {:.2f}%)".format(confidence * 100)
-        deskripsi = (
-            "Tolong arahkan ke objek yang jelas agar bisa diidentifikasikan. "
-            "Pastikan anda berada di salah satu tempat seperti:\n"
-            "- Benteng Vredeburg\n- Candi Borobudur\n- Candi Prambanan\n"
-            "- Gedung Agung Istana Kepresidenan Yogyakarta\n- Masjid Gedhe Kauman\n"
-            "- Monumen Serangan 1 Maret\n- Museum Gunungapi Merapi\n- Situs Ratu Boko\n"
-            "- Taman Sari\n- Tugu Yogyakarta"
-        )
-        lokasi = "-"
-    else:
-        label_output = f"{predicted_label} (Confidence: {confidence * 100:.2f}%)"
-        deskripsi = description.get(predicted_label, "Deskripsi belum tersedia.")
-        lokasi = location.get(predicted_label, None)
-        if lokasi:
-            lokasi = f'<a href="{lokasi}" target="_blank">Lihat Lokasi di Google Maps</a>'
+    try:
+        img = img.resize((224, 224))
+        img_array = image.img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = img_array / 255.0
+
+        pred = model.predict(img_array)[0]
+        confidence = np.max(pred)
+        predicted_label = labels[np.argmax(pred)]
+
+        akurasi = float(confidence)
+        if confidence < 0.8:
+            label_output = "Tidak dapat dikenali (Confidence: {:.2f}%)".format(confidence * 100)
+            deskripsi = (
+                "Tolong arahkan ke objek yang jelas agar bisa diidentifikasikan. "
+                "Pastikan anda berada di salah satu tempat seperti:\n"
+                "- Benteng Vredeburg\n- Candi Borobudur\n- Candi Prambanan\n"
+                "- Gedung Agung Istana Kepresidenan Yogyakarta\n- Masjid Gedhe Kauman\n"
+                "- Monumen Serangan 1 Maret\n- Museum Gunungapi Merapi\n- Situs Ratu Boko\n"
+                "- Taman Sari\n- Tugu Yogyakarta"
+            )
+            lokasi = "-"
         else:
-            lokasi = "Lokasi tidak ditemukan"
+            label_output = f"{predicted_label} (Confidence: {confidence * 100:.2f}%)"
+            deskripsi = description.get(predicted_label, "Deskripsi belum tersedia.")
+            lokasi = location.get(predicted_label, None)
+            if lokasi:
+                lokasi = f'<a href="{lokasi}" target="_blank">Lihat Lokasi di Google Maps</a>'
+            else:
+                lokasi = "Lokasi tidak ditemukan"
+
+        return label_output, deskripsi, lokasi, akurasi
 
-    return label_output, deskripsi, lokasi, akurasi
+    except Exception as e:
+        return "Error", str(e), "-"
 
 # Fungsi untuk membuat FastAPI app
 def create_app():
     app = FastAPI()
-
     app.add_middleware(
-        CORSMiddleware,
-        allow_origins=["http://localhost:9000"],  # atau sesuaikan
-        allow_credentials=True,
-        allow_methods=["*"],
-        allow_headers=["*"],
-    )
-
-    @app.post("/api/predict")
-    async def predict(file: UploadFile = File(...)):
-        contents = await file.read()
-        img = Image.open(BytesIO(contents)).convert("RGB")
-        label_output, deskripsi, lokasi, akurasi = classify_image(img)
-        return JSONResponse(content={
-            "label_output": label_output,
-            "deskripsi": deskripsi,
-            "lokasi": lokasi,
-            "confidence": akurasi
-        })
+    CORSMiddleware,
+    allow_origins=["*"],  # atau daftar domain yang sah
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+
+    # @app.post("/api/predict")
+    # async def predict(file: UploadFile = File(...)):
+    #     contents = await file.read()
+    #     img = Image.open(BytesIO(contents)).convert("RGB")
+    #     label_output, deskripsi, lokasi, akurasi = classify_image(img)
+    #     return JSONResponse(content={
+    #         "label_output": label_output,
+    #         "deskripsi": deskripsi,
+    #         "lokasi": lokasi,
+    #         "confidence": akurasi
+    #     })
 
     gradio_app = gr.Interface(
         fn=classify_image,
         inputs=gr.Image(type="pil", label="Upload Gambar"),
+        # outputs=["text", "text", "html"],
         outputs=[
             gr.Textbox(label="Output Klasifikasi"),
             gr.Textbox(label="Deskripsi Lengkap", lines=20, max_lines=50),
             gr.HTML(label="Link Lokasi"),
         ],
-        flagging_mode="never",
+        # flagging_mode="never",
         title="Klasifikasi Gambar",
         description="Upload gambar, sistem akan mengklasifikasikan dan memberikan deskripsi mengenai gambar tersebut."
     )
@@ -112,8 +134,10 @@ def create_app():
     app = gr.mount_gradio_app(app, gradio_app, path="/gradio")
     return app
 
+app = create_app()
+
 # Run server jika dijalankan langsung
 if __name__ == "__main__":
     import uvicorn
-    app = create_app()
+    # app = create_app()
     uvicorn.run(app, host="127.0.0.1", port=8000)

requirements.txt

@@ -1,10 +1,8 @@
-tensorflow==2.10.0
-gradio==4.25.0
-fastapi==0.110.0
+tensorflow==2.19.0
+numpy==1.26.4
+pillow==9.5.0
+protobuf==3.20.3
+grpcio==1.54.0
+gradio==5.33.2
+fastapi==0.115.12
 uvicorn==0.29.0
-python-multipart==0.0.9
-pillow==10.3.0
-numpy==1.24.3  # Gantilah versi ini
-python-dotenv==1.0.1
-torch
-keras==2.10.0