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79f9a6b 96a429c 9525916 96a429c 9525916 fdd4d60 96a429c fdd4d60 96a429c fdd4d60 96a429c 9525916 96a429c 9525916 96a429c 9525916 96a429c 9525916 96a429c 9525916 96a429c 79f9a6b 9525916 96a429c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | 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)})
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