Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import requests
+from io import BytesIO
+
+# Load model
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model('fish_classification_model.h5')  # Model yükle
+
+model = load_model()
+
+# Class names
+class_names = ['Black Sea Sprat', 'Gilt Head Bream', 'Horse Mackerel', 'Red Mullet', 'Red Sea Bream', 'Sea Bass', 'Shrimp', 'Striped Red Mullet', 'Trout']  # Sınıf isimleri
+
+# Function to get fish emoji
+def get_fish_emoji(fish_name):
+    emoji_dict = {
+        'Black Sea Sprat': '🐟',
+        'Gilt Head Bream': '🐠',
+        'Horse Mackerel': '🐟',
+        'Red Mullet': '🐡',
+        'Red Sea Bream': '🐠',
+        'Sea Bass': '🐟',
+        'Shrimp': '🦐',
+        'Striped Red Mullet': '🐡',
+        'Trout': '🐟'
+    }
+    return emoji_dict.get(fish_name, '🐠')
+
+# Streamlit app
+st.set_page_config(page_title="Fish Species Classifier", page_icon="🐠", layout="wide")
+
+# Add a background image
+background_image = """
+<style>
+[data-testid="stAppViewContainer"] > .main {
+    background-image: url("https://images.unsplash.com/photo-1498574932731-e711f7092d07");
+    background-size: cover;
+    background-position: center center;
+    background-repeat: no-repeat;
+    background-attachment: local;
+}
+</style>
+"""
+st.markdown(background_image, unsafe_allow_html=True)
+
+# Custom CSS for better styling
+st.markdown("""
+    <style>
+    .big-font {
+        font-size:50px !important;
+        color: #0e1117;
+        text-align: center;
+    }
+    .result-font {
+        font-size:30px !important;
+        color: #0e1117;
+        text-align: center;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Title with emoji
+st.markdown('<p class="big-font">🐠 Fish Species Classification 🐟</p>', unsafe_allow_html=True)
+
+# File uploader
+uploaded_file = st.file_uploader("Upload a fish image", type=["jpg", "jpeg", "png"])
+
+# URL input
+image_url = st.text_input("Or enter an image URL")
+
+if uploaded_file is not None or image_url:
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+    else:
+        response = requests.get(image_url)
+        image = Image.open(BytesIO(response.content))
+    
+    st.image(image, caption='Uploaded Image', use_column_width=True)  # Yüklenen resmi göster
+    
+    # Preprocess image
+    image = image.resize((224, 224))
+    image_array = np.array(image) / 255.0
+    image_array = np.expand_dims(image_array, axis=0)  # Resmi ön işle
+    
+    # Make prediction
+    prediction = model.predict(image_array)
+    predicted_class = class_names[np.argmax(prediction)]
+    confidence = np.max(prediction)  # Tahmin yap
+    
+    # Display result with emoji
+    st.markdown(f'<p class="result-font">Predicted fish species: {predicted_class} {get_fish_emoji(predicted_class)}</p>', unsafe_allow_html=True)
+    st.markdown(f'<p class="result-font">Confidence: {confidence:.2f}</p>', unsafe_allow_html=True)
+
+    # Display bar chart of probabilities
+    st.subheader("Prediction Probabilities")
+    prob_df = pd.DataFrame({'Species': class_names, 'Probability': prediction[0]})
+    prob_df = prob_df.sort_values('Probability', ascending=False).reset_index(drop=True)
+    st.bar_chart(prob_df.set_index('Species'))
+
+# Add some information about the project
+st.sidebar.title("About")
+st.sidebar.info(
+    "This app uses a deep learning model to classify fish species. "
+    "Upload an image or provide a URL to get started!"
+)
+
+# Add a footer
+st.markdown(
+    """
+    <style>
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    .footer {
+        position: fixed;
+        left: 0;
+        bottom: 0;
+        width: 100%;
+        background-color: rgba(14, 17, 23, 0.5);
+        color: white;
+        text-align: center;
+    }
+    </style>
+    <div class="footer">
+        <p>Developed with ❤️ by AE</p>
+    </div>
+    """,
+    unsafe_allow_html=True
+)