Update app.py

app.py

@@ -56,16 +56,59 @@ else:
 
 
 
+import pickle
+
+# Mengupdate hasil train dan validate terbaru
+history = {
+    'train_loss': [
+        0.9568, 0.6937, 0.5917, 0.5718, 0.5109,
+        0.4824, 0.4697, 0.3318, 0.2785, 0.2680,
+        0.2371, 0.2333, 0.2198, 0.2060, 0.1962,
+        0.1951, 0.1880, 0.1912, 0.1811, 0.1810
+    ],
+    'train_acc': [
+        0.7011, 0.7774, 0.8094, 0.8146, 0.8331,
+        0.8452, 0.8447, 0.8899, 0.9068, 0.9114,
+        0.9216, 0.9203, 0.9254, 0.9306, 0.9352,
+        0.9346, 0.9368, 0.9353, 0.9396, 0.9409
+    ],
+    'val_loss': [
+        0.4934, 0.3939, 0.4377, 0.3412, 0.2614,
+        0.2966, 0.2439, 0.1065, 0.0926, 0.0797,
+        0.0738, 0.0639, 0.0555, 0.0560, 0.0490,
+        0.0479, 0.0455, 0.0454, 0.0438, 0.0427
+    ],
+    'val_acc': [
+        0.8481, 0.8734, 0.8663, 0.8915, 0.9172,
+        0.9011, 0.9221, 0.9649, 0.9714, 0.9759,
+        0.9762, 0.9791, 0.9827, 0.9812, 0.9843,
+        0.9850, 0.9852, 0.9854, 0.9854, 0.9866
+    ]
+}
+
+# Simpan history sebagai file pickle
+with open('training_history.pkl', 'wb') as f:
+    pickle.dump(history, f)
+
+print('Training history saved as training_history.pkl')
+
 
 
+
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+import gradio as gr
+
 # Load your model
 def load_model():
     model = models.resnet50(weights='DEFAULT')  # Using default weights for initialization
     num_ftrs = model.fc.in_features
     model.fc = nn.Linear(num_ftrs, 12)  # Adjust to the number of classes you have
     
-    # Load the state dict without the weights_only argument
-    model.load_state_dict(torch.load('resnet50_garbage_classification.pth', map_location=torch.device('cpu')))
+    # Load the state dict
+    model.load_state_dict(torch.load('resnet50_garbage_classificationv1.2.pth', map_location=torch.device('cpu')))
     
     model.eval()  # Set to evaluation mode
     return model
@@ -87,18 +130,18 @@ class_names = ['battery', 'biological', 'brown-glass', 'cardboard',
 
 # Define bin colors for each class
 bin_colors = {
-    'battery': 'Merah (Red)',        # Limbah berbahaya (B3)
-    'biological': 'Hijau (Green)',   # Limbah organik
-    'brown-glass': 'Kuning (Yellow)',# Gelas berwarna coklat (anorganik/daur ulang)
-    'cardboard': 'Biru (Blue)',      # Kertas (daur ulang)
-    'clothes': 'Kuning (Yellow)',    # Pakaian (dimasukkan sebagai daur ulang)
-    'green-glass': 'Kuning (Yellow)',# Gelas berwarna hijau (anorganik/daur ulang)
-    'metal': 'Kuning (Yellow)',      # Logam (anorganik/daur ulang)
-    'paper': 'Biru (Blue)',          # Kertas (daur ulang)
-    'plastic': 'Kuning (Yellow)',    # Plastik (anorganik/daur ulang)
-    'shoes': 'Kuning (Yellow)',      # Sepatu (dimasukkan sebagai daur ulang)
-    'trash': 'Hitam (Black)',        # Limbah umum
-    'white-glass': 'Kuning (Yellow)' # Gelas berwarna putih (anorganik/daur ulang)
+    'battery': 'Merah (Red)',                # Limbah berbahaya (B3)
+    'biological': 'Hijau (Green)',           # Limbah organik
+    'brown-glass': 'Kuning (Yellow or trash banks / recycling centers)',  # Gelas berwarna coklat (anorganik/daur ulang)
+    'cardboard': 'Biru (Blue)',              # Kertas (daur ulang)
+    'clothes': 'Kuning atau Bank Sampah (Yellow or trash banks / recycling centers)',  # Pakaian (dimasukkan sebagai daur ulang)
+    'green-glass': 'Kuning (Yellow)',        # Gelas berwarna hijau (anorganik/daur ulang)
+    'metal': 'Kuning (Yellow)',              # Logam (anorganik/daur ulang)
+    'paper': 'Biru (Blue)',                  # Kertas (daur ulang)
+    'plastic': 'Kuning (Yellow)',            # Plastik (anorganik/daur ulang)
+    'shoes': 'Kuning atau Bank Sampah (Yellow or trash banks / recycling centers)',  # Sepatu (dimasukkan sebagai daur ulang)
+    'trash': 'Abu-abu (Gray)',               # Limbah umum
+    'white-glass': 'Kuning (Yellow or trash banks / recycling centers)'         # Gelas berwarna putih (anorganik/daur ulang)
 }
 
 # Define the prediction function
@@ -115,7 +158,7 @@ def predict(image):
     bin_color = bin_colors[class_name]  # Get the corresponding bin color
     return class_name, bin_color  # Return both class name and bin color
 
-# Make Gradio Interface
+# Buat antarmuka Gradio dengan deskripsi
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="numpy", label="Unggah Gambar"),
@@ -124,8 +167,9 @@ iface = gr.Interface(
         gr.Textbox(label="Tong Sampah yang Sesuai")  # 2 output with label
     ],
     title="Klasifikasi Sampah dengan ResNet50 v1",
-    description="Unggah gambar sampah, dan model akan mengklasifikasikannya ke dalam salah satu dari 12 kategori bersama dengan warna tempat sampah yang sesuai."
+    description="Unggah gambar sampah, dan model kami akan mengklasifikasikannya ke dalam salah satu dari 12 kategori bersama dengan warna tempat sampah yang sesuai. "
+                "<strong>Model ini bisa memprediksi jenis sampah dari ke-12 jenis berikut:</strong> Baterai, Sampah organik, Gelas Kaca Coklat, "
+                "Kardus, Pakaian, Gelas Kaca Hijau, Metal, Kertas, Plastik, Sepatu/sandal, Popok/pampers, Gelas Kaca bening."
 )
 
-
 iface.launch(share=True)