Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,82 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+
+# 1. Page Configuration / Sayfa Ayarları (Centered layout seçildi)
+st.set_page_config(page_title="CNN Boundary Detector", layout="centered")
+
+# Sabitleme ve Titremeyi Önleme için CSS
+st.markdown("""
+    <style>
+    .stImage > img {
+        border-radius: 8px;
+        border: 1px solid #ddd;
+    }
+    /* Sütunlar arasındaki boşluğu ve hizalamayı koru */
+    [data-testid="stHorizontalBlock"] {
+        align-items: center;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+@st.cache_resource
+def load_my_model():
+    # Model ismini kendi dosya isminle değiştir (.h5 veya .keras)
+    return load_model("cnn_segmentation_model.keras", compile=False)
+
+model = load_my_model()
+
+# Header / Başlık (Ortalı)
+st.markdown("<h1 style='text-align: center;'>🎯 Boundary Detection System</h1>", unsafe_allow_html=True)
+st.markdown("<h3 style='text-align: center; color: gray;'>Kenar ve Sınır Tespit Sistemi</h3>", unsafe_allow_html=True)
+st.write("---")
+
+# 2. Upload Section / Yükleme Bölümü
+uploaded_file = st.file_uploader("Upload Image / Resim Yükleyin", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Görüntü İşleme
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    img = cv2.imdecode(file_bytes, 1)
+    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+    # Model Tahmini (168x168)
+    img_input = cv2.resize(img_rgb, (168, 168)) / 255.0
+    img_input = np.expand_dims(img_input, axis=0)
+    
+    with st.spinner('Analyzing... / Analiz ediliyor...'):
+        pred = model.predict(img_input, verbose=0)[0]
+        mask = pred.squeeze()
+
+        # Notebook stili parlatma (Normalization)
+        mask_norm = (mask - mask.min()) / (mask.max() - mask.min() + 1e-7)
+        mask_255 = (mask_norm * 255).astype(np.uint8)
+        
+        # Orijinal boyuta geri getir
+        mask_resized = cv2.resize(mask_255, (img_rgb.shape[1], img_rgb.shape[0]))
+
+        # Overlay (Yeşil Kenar)
+        overlay = img_rgb.copy()
+        # Eşik (Threshold) 120 olarak ayarlandı
+        overlay[mask_resized > 120] = [0, 255, 0] 
+        final_blend = cv2.addWeighted(img_rgb, 0.7, overlay, 0.3, 0)
+
+    # 3. YAN YANA VE ORTALANMIŞ GÖSTERİM
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.markdown("<p style='text-align: center; font-weight: bold;'>Original / Orijinal</p>", unsafe_allow_html=True)
+        st.image(img_rgb, use_container_width=True)
+
+    with col2:
+        st.markdown("<p style='text-align: center; font-weight: bold;'>Prediction / Tahmin</p>", unsafe_allow_html=True)
+        st.image(final_blend, use_container_width=True)
+
+    # Opsiyonel: Siyah Beyaz Maske
+    st.write("---")
+    with st.expander("Show Binary Mask / İkili Maskeyi Göster"):
+        st.image(mask_resized, width=400, caption="Grayscale Output")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+else:
+    st.info("Waiting for image upload... / Resim yüklenmesi bekleniyor...")