Update app.py

app.py

@@ -1,9 +1,9 @@
 import streamlit as st
 import tensorflow as tf
 import numpy as np
+import matplotlib.pyplot as plt  # <--- Library baru untuk membuat Heatmap
 from PIL import Image
 
-st.set_page_config(layout="wide") # Agar tampilan lebih lebar
 st.title("🌊 Flood Segmentation")
 
 # 1. Load Model
@@ -15,23 +15,19 @@ def load_model():
 with st.spinner('Loading Model...'):
     model = load_model()
 
-# Penjelasan singkat
-st.write("Model ini akan memberikan lapisan merah transparan yang tegas di area yang terdeteksi sebagai banjir.")
-
 uploaded = st.file_uploader("Upload flood image", type=["jpg", "jpeg", "png"])
 
 if uploaded:
     # 2. Preprocessing Gambar
-    img_pil = Image.open(uploaded).convert("RGB")
-    # Simpan ukuran asli untuk resize balik nanti agar resolusi tinggi
-    original_size = img_pil.size 
-    
-    # Resize ke 256x256 untuk input model
-    img_resized = img_pil.resize((256, 256))
+    img = Image.open(uploaded).convert("RGB")
+    img_resized = img.resize((256, 256))
     
-    # Normalisasi & Preprocessing
+    # Normalisasi
     x = np.array(img_resized, dtype=np.float32) / 255.0
-    x = np.concatenate([x, x], axis=-1) # Stack jadi 6 channel
+    
+    # Stack gambar agar menjadi 6 channel (memenuhi syarat input model)
+    x = np.concatenate([x, x], axis=-1) 
+    
     x = np.expand_dims(x, axis=0) # Batch dimension
 
     # 3. Prediksi
@@ -40,48 +36,43 @@ if uploaded:
         output_dict = infer(tf.constant(x))
         pred_tensor = list(output_dict.values())[0]
         
-        # Ambil probabilitas (256x256)
+        # Ambil nilai probabilitas mentah (0.0 sampai 1.0)
         pred = pred_tensor.numpy()[0, :, :, 0]
         
-        # --- BAGIAN VISUALISASI YANG DIPERJELEAS ---
+        # --- BAGIAN PEMBUATAN HEATMAP ---
         
-        # 1. Thresholding Tegas
-        # Ubah probabilitas jadi biner: Banjir (True) atau Tidak (False)
-        # Nilai > 0.5 dianggap banjir pasti.
-        mask_binary = pred > 0.5
-
-        # 2. Buat Lapisan Merah Transparan
-        # Siapkan array kosong seukuran gambar resized
-        overlay_layer = np.zeros((256, 256, 4), dtype=np.uint8) # RGBA (4 channel)
-
-        # Di area yang masker-nya True (Banjir), isi dengan warna Merah Transparan
-        # R=255, G=0, B=0, Alpha=150 (agak transparan)
-        overlay_layer[mask_binary] = [255, 0, 0, 150]
+        # 1. Buat colormap (Jet: Biru=Rendah, Merah=Tinggi)
+        # plt.cm.jet mengembalikan RGBA (0-1), kita ambil RGB saja dan kali 255
+        heatmap_colored = plt.cm.jet(pred)[:, :, :3]
+        heatmap_img = (heatmap_colored * 255).astype(np.uint8)
         
-        # Ubah jadi gambar PIL
-        overlay_img_pil = Image.fromarray(overlay_layer, 'RGBA')
+        # 2. Buat Overlay (Gabungkan gambar asli dengan Heatmap)
+        # Kita ambil gambar asli yang sudah di-resize
+        original_img_arr = np.array(img_resized)
         
-        # 3. Gabungkan (Composite)
-        # Ambil gambar asli yang di-resize (background)
-        background = img_resized.convert("RGBA")
-        # Tempelkan lapisan merah di atasnya
-        final_composite_small = Image.alpha_composite(background, overlay_img_pil)
+        # Rumus blending: 60% Gambar Asli + 40% Heatmap
+        blended = (original_img_arr * 0.6 + heatmap_img * 0.4).astype(np.uint8)
         
-        # 4. Resize balik ke ukuran asli agar terlihat tajam di layar
-        final_result_highres = final_composite_small.resize(original_size, Image.Resampling.LANCZOS)
+        # Konversi ke format Image agar bisa ditampilkan Streamlit
+        final_heatmap = Image.fromarray(blended)
+        pure_heatmap = Image.fromarray(heatmap_img)
 
         # --------------------------------
         
         # 4. Tampilkan Hasil
-        col1, col2 = st.columns(2)
+        col1, col2, col3 = st.columns(3)
         
         with col1:
-            st.subheader("Original Image")
-            st.image(img_pil, use_container_width=True)
+            st.write("Original Image")
+            st.image(img_resized, use_container_width=True)
             
         with col2:
-            st.subheader("Sharp Detection Result")
-            st.image(final_result_highres, use_container_width=True, caption="Area Merah = Deteksi Banjir")
+            st.write("Pure Heatmap")
+            st.image(pure_heatmap, use_container_width=True, caption="Merah = Banjir")
+
+        with col3:
+            st.write("Overlay Result")
+            st.image(final_heatmap, use_container_width=True, caption="Gabungan")
             
     except Exception as e:
         st.error(f"Terjadi error: {e}")