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import numpy as np
import streamlit as st
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.resnet50 import preprocess_input
# Load the trained model
model_path = 'my_cnn.h5' # or '/content/my_model.keras'
model = load_model(model_path)
# Preprocess the image
def preprocess_image(img):
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
img_array = preprocess_input(img_array) # Ensure correct preprocessing for ResNet50
return img_array
# Make predictions and map to class labels
def classify_image(img):
img_array = preprocess_image(img)
predictions = model.predict(img_array)
predicted_class = np.argmax(predictions, axis=1) # Get the index of the highest probability
class_labels = {0: 'Aedes Aegypti', 1: 'Anopheles Stephensi', 2: 'Culex Quinquefasciatus'}
species = class_labels.get(predicted_class[0], "Unknown")
return species, predictions
# Streamlit application
def main():
st.title("Mosquito Species Classification")
st.write("Upload a mosquito image or select an example image to classify its species.")
# File uploader for image input
uploaded_file = st.file_uploader("Upload your own image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Load the uploaded image for display
img = image.load_img(uploaded_file, target_size=(224, 224))
st.image(img, caption='Uploaded Image', width=224)
# Classify the uploaded image
result, probabilities = classify_image(img)
st.write(f'Predicted mosquito species: **{result}**')
st.write(f'Prediction probabilities: {probabilities}')
# Example images
example_images = {
"Aedes Aegypti": "Aedes_aegypti_1_0_832.jpg",
"Anopheles Stephensi": "Anopheles_stephensi_1_0_364.jpg",
"Culex Quinquefasciatus": "Culex_quinquefasciatus_1_0_1307.jpg",
}
# Select an example image
selected_example = st.selectbox("Or select an example image:", list(example_images.keys()))
if selected_example:
img_path = example_images[selected_example]
img = image.load_img(img_path, target_size=(224, 224))
st.image(img, caption=f'Selected Example Image: {selected_example}', width=224)
# Classify the example image
result, probabilities = classify_image(img)
st.write(f'Predicted mosquito species: **{result}**')
st.write(f'Prediction probabilities: {probabilities}')
# Run the app
if __name__ == "__main__":
main()
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