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import streamlit as st
import tensorflow as tf
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import numpy as np
from PIL import Image
import io
st.set_page_config(
page_title="Waste Classifier",
layout="centered"
)
@st.cache_resource
def load_model():
return tf.keras.models.load_model('CNN_Prak4_ML.h5')
def preprocess_image(img):
img = img.resize((244, 244))
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
img = img / 255.0
return img
LABEL_CLASS = {
0: "Cardboard",
1: "Glass",
2: "Metal",
3: "Paper",
4: "Textile Trash",
5: "Vegetation"
}
def main():
st.title("Waste Classifier")
st.write("Upload an image and the model will predict waste image")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
if st.button('Predict'):
model = load_model()
processed_image = preprocess_image(image)
with st.spinner('Predicting...'):
prediction = model.predict(processed_image)
pred_class = LABEL_CLASS[np.argmax(prediction)]
confidence = float(prediction.max()) * 100
st.success(f'Prediction: {pred_class.upper()}')
st.info(f'Confidence: {confidence:.2f}%')
st.write("Class Probabilities:")
for i, prob in enumerate(prediction[0]):
st.progress(float(prob))
st.write(f"{LABEL_CLASS[i]}: {float(prob)*100:.2f}%")
if __name__ == "__main__":
main() |