uploaded model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+segnet_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,82 @@
+import streamlit as st
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+from PIL import Image
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+
+# Load the trained model
+@st.cache_resource
+def load_trained_model():
+    model_path = "segnet_model.keras"  
+    return load_model(model_path)
+
+def predict_segmentation(model, image, target_size=(256, 256)):
+    image = image.resize(target_size)
+    image_array = img_to_array(image) / 255.0
+    image_array = np.expand_dims(image_array, axis=0)  
+
+    prediction = model.predict(image_array)[0]  
+    return prediction
+
+def create_mask_plot(mask, colormap, labels):
+    
+
+    fig, ax = plt.subplots(figsize=(5, 5))
+    ax.imshow(mask.squeeze(), cmap=colormap, vmin=0, vmax=len(labels) - 1)
+    ax.axis("off")
+    
+    # Add a legend
+    legend_patches = [
+        plt.Line2D([0], [0], color=colormap.colors[i], lw=4, label=label)
+        for i, label in enumerate(labels)
+    ]
+    ax.legend(handles=legend_patches, loc="upper right", bbox_to_anchor=(1.2, 1.0))
+    
+    # Convert the Matplotlib figure to a PIL Image
+    fig.canvas.draw()
+    image = np.array(fig.canvas.renderer.buffer_rgba())
+    plt.close(fig)
+    return Image.fromarray(image)
+
+# Streamlit App
+def main():
+    st.title("Flood Area Segmentation")
+    st.write("Upload an image to predict its segmentation mask.")
+
+    # Load the model (cached)
+    model = load_trained_model()
+
+    # File uploader
+    uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])
+    
+    if uploaded_file is not None:
+        # Load and display the uploaded image
+        image = Image.open(uploaded_file)
+
+        # Predict segmentation
+        with st.spinner("Predicting..."):
+            predicted_mask = predict_segmentation(model, image)
+
+        # Define custom colormap and labels
+        colormap = ListedColormap(["green", "blue"])  # Green: Non-Flooded, Blue: Flooded
+        labels = ["Non-Flooded Area", "Flooded Area"]
+
+        # Create the mask visualization
+        mask_image = create_mask_plot(predicted_mask, colormap, labels)
+
+        # Display results side by side
+        st.subheader("Results")
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.write("### Original Image")
+            st.image(image, caption="Original Image", use_container_width=True)
+
+        with col2:
+            st.write("### Predicted Mask")
+            st.image(mask_image, caption="Predicted Mask", use_container_width=True)
+
+if __name__ == "__main__":
+    main()

segnet_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7aef0d401c2621df674127e2801f42e1cd74057abb9089db1c2377fccad7fccd
+size 211961251