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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 for Hugging Face === | |
| 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/" | |
| 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() | |
| # === Title === | |
| st.title("🕳️ Sinkhole Segmentation with EffV2-UNet") | |
| # === Confidence threshold and predict trigger === | |
| st.sidebar.header("Segmentation Settings") | |
| threshold = st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.5, step=0.01) | |
| # === Image input section === | |
| uploaded_image = st.file_uploader("📤 Upload an image", type=["png", "jpg", "jpeg", "tif", "tiff"]) | |
| # === Example selector with preview === | |
| example_dir = "examples" | |
| example_files = sorted([ | |
| f for f in os.listdir(example_dir) | |
| if f.lower().endswith((".jpg", ".jpeg", ".png", ".tif", ".tiff")) | |
| ]) | |
| selected_example_path = None | |
| if example_files: | |
| st.subheader("🖼️ Try with an Example Image") | |
| cols = st.columns(min(len(example_files), 4)) | |
| for i, file in enumerate(example_files): | |
| img_path = os.path.join(example_dir, file) | |
| img_preview = Image.open(img_path).convert("RGB").resize((128, 128)) | |
| with cols[i % len(cols)]: | |
| st.image(img_preview, caption=file, use_column_width=True) | |
| if st.button(f"Use {file}", key=file): | |
| selected_example_path = img_path | |
| # === Set image to process === | |
| if selected_example_path: | |
| uploaded_image = selected_example_path | |
| # === Run prediction if button clicked === | |
| if uploaded_image: | |
| if isinstance(uploaded_image, str): | |
| image = Image.open(uploaded_image).convert("RGB") | |
| else: | |
| image = Image.open(uploaded_image).convert("RGB") | |
| st.image(image, caption="Input Image", use_column_width=True) | |
| if st.button("Run Segmentation"): | |
| resized = image.resize((512, 512)) | |
| x = np.expand_dims(np.array(resized), axis=0) | |
| y = model.predict(x)[0, :, :, 0] | |
| st.text(f"Prediction min/max: {y.min():.5f} / {y.max():.5f}") | |
| mask_bin = (y > threshold).astype(np.uint8) * 255 | |
| mask_image = Image.fromarray(mask_bin) | |
| st.image(mask_image, caption=f"Segmentation Mask (Threshold = {threshold:.2f})", use_column_width=True) | |