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40b9560 d5dd5e5 40b9560 d5dd5e5 | 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 | import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
IMG_SIZE = 128
# Class Labels
# Based on: {'ALB':0, 'YFT':1, 'SHARK':2, 'NoF':3, 'LAG':4, 'BET':5, 'OTHER':6, 'DOL':7}
LABELS = ['ALB', 'YFT', 'SHARK', 'NoF', 'LAG', 'BET', 'OTHER', 'DOL']
# Page Config
st.set_page_config(page_title="🐟 Fish Species Detection", page_icon="🐟")
# --- LOAD MODEL ---
@st.cache_resource
def load_model():
try:
model = tf.keras.models.load_model('src/fish.h5')
return model
except Exception as e:
st.error(f"Error loading model: {e}")
return None
model = load_model()
# --- IMAGE PREPROCESSING ---
def process_image(image):
image = image.resize((IMG_SIZE, IMG_SIZE))
img_array = np.array(image)
img_array = img_array / 255.0
img_array = np.expand_dims(img_array, axis=0)
return img_array
# --- USER INTERFACE ---
st.title("🐟 Fish Species Classification")
st.write("The Nature Conservancy Fisheries Monitoring Model")
uploaded_file = st.file_uploader("Upload a fish image.", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
image = Image.open(uploaded_file).convert('RGB')
st.image(image, caption='Uploaded Image', use_column_width=True)
if st.button('Predict'):
if model is None:
st.error("Model could not be loaded, prediction impossible.")
else:
with st.spinner('Analyzing...'):
# Process image
processed_img = process_image(image)
# Make prediction
predictions = model.predict(processed_img)
# Get the highest probability class
predicted_class_idx = np.argmax(predictions[0])
predicted_class = LABELS[predicted_class_idx]
confidence = np.max(predictions[0])
# Display Results
st.success(f"Prediction: **{predicted_class}**")
st.info(f"Confidence: **{confidence * 100:.2f}%**")
# Probability Distribution Chart
st.write("---")
st.write("Probability Distribution:")
# Create a dictionary for the chart
chart_data = {label: float(predictions[0][i]) for i, label in enumerate(LABELS)}
st.bar_chart(chart_data) |