Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,108 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import tensorflow as tf
+import numpy as np
+import cv2
+from PIL import Image
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout
+
+# --- 1. SAYFA AYARLARI / PAGE CONFIG ---
+st.set_page_config(page_title="Fish Classifier / Balık Sınıflandırıcı", page_icon="🐟", layout="wide")
+
+# --- 2. MODEL YÜKLEME / LOAD MODEL ---
+@st.cache_resource
+def load_my_model():
+    # Mimariyi manuel kuruyoruz (Sürüm çakışmasını önlemek için)
+    base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(170, 170, 3))
+    base_model.trainable = False
+    
+    model = Sequential([
+        base_model,
+        GlobalAveragePooling2D(),
+        Dense(256, activation='relu'),
+        Dropout(0.5),
+        Dense(9, activation='softmax')
+    ])
+    
+    # Ağırlıkları yükle
+    try:
+        model.load_weights('fish_transfer_model.h5')
+    except:
+        model = tf.keras.models.load_model('fish_transfer_model.h5')
+    return model
+
+model = load_my_model()
+
+# --- 3. SÖZLÜK / DICTIONARY ---
+translate = {
+    'Black Sea Sprat': 'Karadeniz Çaça',
+    'Gilt-Head Bream': 'Çipura',
+    'Hourse Mackerel': 'İstavrit',
+    'Red Mullet': 'Barbun',
+    'Red Sea Bream': 'Mercan',
+    'Sea Bass': 'Levrek',
+    'Shrimp': 'Karides',
+    'Striped Red Mullet': 'Tekir',
+    'Trout': 'Alabalık'
+}
+class_labels = sorted(list(translate.keys()))
+
+# --- 4. SOL PANEL / SIDEBAR ---
+with st.sidebar:
+    st.title("🔍 Species List / Tür Listesi")
+    st.write("Supported Fish Types / Desteklenen Balıklar:")
+    for en, tr in translate.items():
+        st.write(f"🔹 **{en}** / {tr}")
+    
+    st.markdown("---")
+    st.info("Model: MobileNetV2\n\nAccuracy / Doğruluk: %99")
+
+# --- 5. ANA EKRAN / MAIN SCREEN ---
+st.title("🐟 Fish Classification System / Akıllı Balık Tanımlama")
+st.subheader("Deep Learning Project / Derin Öğrenme Projesi")
+
+# Sabit konteyner (Titremeyi önler)
+main_container = st.container()
+
+with main_container:
+    uploaded_file = st.file_uploader("Upload an image... / Bir resim yükleyin...", type=["jpg", "png", "jpeg"])
+
+    if uploaded_file is not None:
+        col1, col2 = st.columns([1, 1])
+        
+        # Sol Kolon: Resim
+        image = Image.open(uploaded_file)
+        col1.image(image, caption="Uploaded Image / Yüklenen Resim", use_container_width=True)
+        
+        # Sağ Kolon: Tahmin
+        with col2:
+            st.write("### Analysis Result / Analiz Sonucu")
+            
+            # --- ÖN İŞLEME / PREPROCESSING ---
+            img = np.array(image.convert('RGB'))
+            img = cv2.resize(img, (170, 170))
+            img = img / 255.0
+            img = np.expand_dims(img, axis=0)
+            
+            # --- TAHMİN / PREDICTION ---
+            preds = model.predict(img)
+            idx = np.argmax(preds)
+            prob = np.max(preds) * 100
+            
+            label_en = class_labels[idx]
+            label_tr = translate[label_en]
+            
+            # --- GÖRSEL KUTLAMA / CELEBRATION ---
+            st.success(f"**Result / Sonuç:** {label_en} / {label_tr}")
+            st.balloons() # Balonlar burada uçuyor! 🎈
+            
+            st.metric(label="Confidence / Güven Oranı", value=f"%{prob:.2f}")
+            
+            # Olasılık Grafiği / Chart
+            st.write("Probabilities / Olasılıklar:")
+            chart_data = {f"{k} / {translate[k]}": float(preds[0][i]) for i, k in enumerate(class_labels)}
+            st.bar_chart(chart_data)
 
-"""
-# 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])),
-    ))
+# --- 6. ALT BİLGİ / FOOTER ---
+st.markdown("---")