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2747c87 918ca03 de90842 918ca03 05cac47 918ca03 6a02817 05cac47 2747c87 05cac47 2747c87 05cac47 2747c87 05cac47 2747c87 05cac47 918ca03 05cac47 5ecab44 05cac47 2747c87 05cac47 2747c87 05cac47 2747c87 017095f 05cac47 2747c87 | 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 | import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import json
# Load model
@st.cache_resource
def load_model():
model = tf.keras.models.load_model("./src/intel_classifier_v2.h5")
return model
model = load_model()
# Load class indices
with open("./src/class_indices.json", "r") as f:
class_indices = json.load(f)
class_names = list(class_indices.keys())
# Main
def run():
st.title("Environment Image Classifier")
# Form input
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
# Display image
image = Image.open(uploaded_file).convert('RGB')
st.image(image, caption="Uploaded Image", use_container_width=True)
# Preprocess
img = image.resize((150, 150))
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Predict
prediction = model.predict(img_array)[0]
predicted_index = np.argmax(prediction)
predicted_class = class_names[predicted_index]
confidence = np.max(prediction)
# Output
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() |