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 fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
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

# 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__)

# Fungsi memuat model dari file .h5 langsung
def load_model_from_file(h5_path):
    return load_model(h5_path)

# 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 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>'
        else:
            lokasi = "Lokasi tidak ditemukan"

    return label_output, deskripsi, lokasi, akurasi

# 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
        })

    gradio_app = gr.Interface(
        fn=classify_image,
        inputs=gr.Image(type="pil", label="Upload Gambar"),
        outputs=[
            gr.Textbox(label="Output Klasifikasi"),
            gr.Textbox(label="Deskripsi Lengkap", lines=20, max_lines=50),
            gr.HTML(label="Link Lokasi"),
        ],
        flagging_mode="never",
        title="Klasifikasi Gambar",
        description="Upload gambar, sistem akan mengklasifikasikan dan memberikan deskripsi mengenai gambar tersebut."
    )

    app = gr.mount_gradio_app(app, gradio_app, path="/gradio")
    return app

# Run server jika dijalankan langsung
if _name_ == "_main_":
    import uvicorn
    app = create_app()
    uvicorn.run(app, host="127.0.0.1", port=8000)