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Fish-Classification-System / src /streamlit_app.py

Update src/streamlit_app.py

1e7ccc7 verified 6 months ago

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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)

# --- 6. ALT BİLGİ / FOOTER ---
st.markdown("---")

	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)

	# --- 6. ALT BİLGİ / FOOTER ---
	st.markdown("---")