| import streamlit as st
|
| import tensorflow as tf
|
| import numpy as np
|
| from PIL import Image
|
| import json
|
|
|
|
|
| @st.cache_resource
|
| def load_model():
|
| model = tf.keras.models.load_model("./src/intel_classifier_v2.h5")
|
| return model
|
|
|
| model = load_model()
|
|
|
|
|
| with open("./src/class_indices.json", "r") as f:
|
| class_indices = json.load(f)
|
|
|
| class_names = list(class_indices.keys())
|
|
|
|
|
|
|
| def run():
|
|
|
| st.title("Environment Image Classifier")
|
|
|
|
|
| with st.form(key='form_image_classifier'):
|
| uploaded_file = st.file_uploader(
|
| "Upload an image | Classes = (Buildings, Forest, Glacier, Mountain, Sea, Street)",
|
| type=["jpg", "jpeg", "png"],
|
| help="Upload an environment image to classify"
|
| )
|
|
|
| submitted = st.form_submit_button('Predict')
|
|
|
| if submitted:
|
|
|
| if uploaded_file is None:
|
| st.warning("Please upload an image first.")
|
| return
|
|
|
|
|
| image = Image.open(uploaded_file).convert('RGB')
|
| st.image(image, caption="Uploaded Image", use_container_width=True)
|
|
|
|
|
| img = image.resize((150, 150))
|
| img_array = np.array(img) / 255.0
|
| img_array = np.expand_dims(img_array, axis=0)
|
|
|
|
|
| prediction = model.predict(img_array)[0]
|
| predicted_index = np.argmax(prediction)
|
| predicted_class = class_names[predicted_index]
|
| confidence = np.max(prediction)
|
|
|
|
|
| st.subheader("Prediction Result")
|
|
|
| st.success(f"Predicted Class: {predicted_class}")
|
| st.write(f"Confidence: {confidence:.2%}")
|
|
|
| st.write("### Class Probabilities")
|
| for i, prob in enumerate(prediction):
|
| st.write(f"{class_names[i]}: {prob:.2%}")
|
|
|
| if __name__ == '__main__':
|
| run() |
