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| import streamlit as st | |
| import numpy as np | |
| import tensorflow as tf | |
| from PIL import Image | |
| # Import preprocessing functions | |
| from keras.applications.efficientnet import preprocess_input as efficientnet_preprocess | |
| from keras.applications.resnet50 import preprocess_input as resnet_preprocess | |
| from keras.applications.vgg16 import preprocess_input as vgg_preprocess | |
| from keras.applications.mobilenet import preprocess_input as mobilenet_preprocess | |
| from keras.applications.inception_v3 import preprocess_input as inception_preprocess | |
| # Class names | |
| CLASS_NAMES = [ | |
| 'bass', 'black_sea_sprat', 'gilt_head_bream', 'hourse_mackerel', | |
| 'red_mullet', 'red_sea_bream', 'sea_bass', 'shrimp', | |
| 'striped_red_mullet', 'trout' | |
| ] | |
| st.set_page_config(page_title="🐟 Fish Species Classifier", layout="centered") | |
| st.title("🐟 Fish Species Classifier") | |
| st.markdown("Upload a fish image and select a model to predict its species.") | |
| # Upload image | |
| uploaded_file = st.file_uploader("Choose a fish image...", type=["jpg", "jpeg", "png"]) | |
| # Model selection | |
| model_options = { | |
| "EfficientNetB0": r"D:\image_classification\model\efficientnetb0_fish_finetuned.h5", | |
| "MobileNet": r"D:\image_classification\model\mobilenet_fish_finetuned.h5", | |
| "InceptionV3": r"D:\image_classification\model\inceptionv3_fish_finetuned.h5", | |
| "ResNet50": r"D:\image_classification\model\resnet50_fish_finetuned.h5", | |
| "VGG16": r"D:\image_classification\model\vgg16_fish_finetuned.h5", | |
| "CNN from Scratch": r"D:\image_classification\model\fish_cnn_model.h5" | |
| } | |
| selected_model = st.selectbox("Select a model", list(model_options.keys())) | |
| # Show image preview | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file).convert("RGB") | |
| st.session_state["uploaded_image"] = image | |
| st.image(image, caption="Uploaded Image", width=300) | |
| elif "uploaded_image" in st.session_state: | |
| st.image(st.session_state["uploaded_image"], caption="Uploaded Image", width=300) | |
| # Show Submit button only when both are selected | |
| if uploaded_file and selected_model: | |
| submit = st.button("🔍 Submit for Prediction") | |
| if submit: | |
| # Resize and convert to array | |
| image = image.resize((224, 224)) | |
| img_array = tf.keras.utils.img_to_array(image) | |
| img_array = np.expand_dims(img_array, axis=0) | |
| # Choose preprocess function | |
| if "EfficientNet" in selected_model: | |
| preprocess_func = efficientnet_preprocess | |
| elif "ResNet" in selected_model: | |
| preprocess_func = resnet_preprocess | |
| elif "VGG" in selected_model: | |
| preprocess_func = vgg_preprocess | |
| elif "MobileNet" in selected_model: | |
| preprocess_func = mobilenet_preprocess | |
| elif "Inception" in selected_model: | |
| preprocess_func = inception_preprocess | |
| else: | |
| preprocess_func = lambda x: x # No preprocessing for custom CNN | |
| # Apply preprocessing | |
| img_array = preprocess_func(img_array) | |
| # Load model and predict | |
| model_path = model_options[selected_model] | |
| model = tf.keras.models.load_model(model_path) | |
| predictions = model.predict(img_array)[0] | |
| predicted_index = np.argmax(predictions) | |
| predicted_label = CLASS_NAMES[predicted_index] | |
| # Display result | |
| st.success(f"🧠 Predicted Species: **{predicted_label}**") | |
| st.markdown("🔢 Confidence Scores:") | |
| st.bar_chart({cls: float(score) for cls, score in zip(CLASS_NAMES, predictions)}) | |