import gradio as gr
import tensorflow as tf
import pandas as pd
import numpy as np
from datetime import datetime
from PIL import Image

MODEL_PATH = "best_model.h5" 
class_names = ['layak', 'rusak']
history_data = []

try:
    best_model = tf.keras.models.load_model(MODEL_PATH)
    print("✅ Model berhasil dimuat.")
except Exception as e:
    print(f"❌ Gagal memuat model: {e}")
    # Dummy model untuk mencegah crash saat build jika file belum ada
    best_model = None

# --- 2. Fungsi Helper ---
def get_detailed_info(label, confidence):
    if label == 'layak':
        if confidence > 0.85:
            return "### ✅ STATUS: SANGAT LAYAK\n**Analisis:** Bangunan dalam kondisi prima. Struktur utama terlihat utuh dan sangat aman untuk dihuni."
        return "### ⚠️ STATUS: LAYAK (DENGAN CATATAN)\n**Analisis:** Bangunan aman dihuni, namun ditemukan indikasi kerusakan minor. Disarankan pengecekan rutin pada area retakan."
    else:
        if confidence > 0.85:
            return "### 🚨 STATUS: RUSAK PARAH\n**Analisis:** BAHAYA! Ditemukan kerusakan struktur fatal. Segera kosongkan area dan hubungi pihak berwenang."
        return "### 🚧 STATUS: RUSAK RINGAN\n**Analisis:** Terdeteksi kerusakan fisik pada beberapa bagian. Perlu perbaikan teknis sebelum bangunan dinyatakan aman sepenuhnya."

def predict_image(img):
    if best_model is None:
        return {}, "Model belum dimuat."
    
    # Preprocessing
    img = img.resize((224, 224))
    img_array = tf.keras.preprocessing.image.img_to_array(img)
    img_array = tf.expand_dims(img_array, 0) / 255.0

    # Prediksi
    predictions = best_model.predict(img_array)[0]
    result = {class_names[i]: float(predictions[i]) for i in range(len(class_names))}

    top_label = max(result, key=result.get)
    description = get_detailed_info(top_label, result[top_label])
    return result, description

# --- 3. Fungsi Logic Dashboard ---
def handle_upload(img):
    if img is None: return {}, "_Menunggu foto bangunan..._"
    return predict_image(img)

def handle_report(img, location):
    if img is None:
        return {}, pd.DataFrame(history_data, columns=["Waktu", "Status", "Lokasi"]), None, "❌ Gagal: Foto kosong."

    output_dict, desc = predict_image(img)
    status = max(output_dict, key=output_dict.get).upper()
    now = datetime.now().strftime("%H:%M | %d-%m-%Y")

    history_data.insert(0, [now, status, location if location else "Pusat Kota"])
    df = pd.DataFrame(history_data, columns=["Waktu", "Status", "Lokasi"])

    return output_dict, df, None, "✅ Laporan berhasil disimpan ke riwayat!"

# --- 4. UI Layout HomeCheck ---
with gr.Blocks(title="HomeCheck AI") as demo:
    # Header Area
    with gr.Row():
        with gr.Column(scale=8):
            gr.Markdown("# 🏠 HomeCheck AI")
            gr.Markdown("### *Sistem Deteksi Kelayakan Bangunan Cerdas*")
        with gr.Column(scale=2):
            gr.Markdown("![Logo](https://img.icons8.com/fluency/96/home.png)")

    gr.Markdown("---") 

    with gr.Tabs():
        with gr.TabItem("🔍 Analisis Baru"):
            with gr.Row():
                # Kolom Kiri: Input
                with gr.Column(variant="panel"):
                    gr.Markdown("#### 📥 Input Data")
                    input_img = gr.Image(sources=["upload", "webcam"], type="pil", label="Foto Bangunan")
                    input_loc = gr.Textbox(
                        label="Titik Lokasi",
                        placeholder="Contoh: Perumahan Indah Blok A, Medan",
                        lines=1
                    )
                    btn_report = gr.Button("🚀 SIMPAN LAPORAN", variant="primary")

                # Kolom Kanan: Hasil
                with gr.Column():
                    gr.Markdown("#### 📊 Hasil Diagnosa")
                    output_label = gr.Label(num_top_classes=2, label="Probabilitas Akurasi")

                    with gr.Group():
                        output_description = gr.Markdown(
                            "**Instruksi:**\nSilakan ambil atau upload foto bagian bangunan yang ingin diperiksa.",
                        )

        with gr.TabItem("📜 Riwayat Pemeriksaan"):
            gr.Markdown("#### 📑 Log Laporan Tersimpan")
            output_history = gr.Dataframe(
                headers=["Waktu", "Status", "Lokasi"],
                datatype=["str", "str", "str"],
                interactive=False
            )

    gr.Markdown("---")
    gr.Markdown("© 2026 HomeCheck AI")

    # --- Interaction Logic ---
    input_img.change(fn=handle_upload, inputs=input_img, outputs=[output_label, output_description])

    btn_report.click(
        fn=handle_report,
        inputs=[input_img, input_loc],
        outputs=[output_label, output_history, input_img, output_description]
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)