Update app.py

app.py

@@ -1,125 +1,123 @@
-import gradio as gr
-import tensorflow as tf
-import pandas as pd
-import numpy as np
-from datetime import datetime
-from PIL import Image
-
-# --- 1. Konfigurasi & Load Model ---
-# Sesuaikan nama file model dengan yang ada di screenshot Anda
-MODEL_PATH = "best_model" 
-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__":
+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)