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import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import os
# === Fix font/matplotlib warnings ===
os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"
os.environ["XDG_CACHE_HOME"] = "/tmp"
# === Custom loss and metrics ===
def weighted_dice_loss(y_true, y_pred):
smooth = 1e-6
y_true_f = tf.reshape(y_true, [-1])
y_pred_f = tf.reshape(y_pred, [-1])
intersection = tf.reduce_sum(y_true_f * y_pred_f)
return 1 - ((2. * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth))
def iou_metric(y_true, y_pred):
y_true = tf.cast(y_true > 0.5, tf.float32)
y_pred = tf.cast(y_pred > 0.5, tf.float32)
intersection = tf.reduce_sum(y_true * y_pred)
union = tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) - intersection
return intersection / (union + 1e-6)
def bce_loss(y_true, y_pred):
return tf.keras.losses.binary_crossentropy(y_true, y_pred)
# === Load Model ===
model_path = "final_model_after_third_iteration_WDL0.07_0.5155/"
@st.cache_resource
def load_model():
return tf.keras.models.load_model(
model_path,
custom_objects={
"weighted_dice_loss": weighted_dice_loss,
"iou_metric": iou_metric,
"bce_loss": bce_loss
}
)
model = load_model()
# === Inference Function ===
def run_prediction(image):
image = image.convert("RGB").resize((512, 512))
x = np.expand_dims(np.array(image), axis=0)
y = model.predict(x)[0, :, :, 0]
y_norm = (y - y.min()) / (y.max() - y.min() + 1e-6)
mask = (y_norm * 255).astype(np.uint8)
return Image.fromarray(mask)
# === Streamlit UI ===
st.title("🕳️ Sinkhole Segmentation with EffV2-UNet")
example_dir = "examples"
example_files = sorted([f for f in os.listdir(example_dir) if f.lower().endswith((".jpg", ".png"))])
# Display examples in columns
cols = st.columns(len(example_files))
for i, filename in enumerate(example_files):
with cols[i]:
img_path = os.path.join(example_dir, filename)
example_img = Image.open(img_path)
st.image(example_img, caption=filename, use_column_width=True)
if st.button(f"Run on {filename}"):
st.subheader("Original Image")
st.image(example_img, use_column_width=True)
st.subheader("Predicted Mask")
result = run_prediction(example_img)
st.image(result, use_column_width=True)
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