Create app.py

app.py

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+import streamlit as st
+import torch
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+from unet_model import UNet  # Make sure unet_model.py is also uploaded
+
+# Load the model
+model = UNet()
+model.load_state_dict(torch.load("model.pth", map_location="cpu"))
+model.eval()
+
+st.title("🌊 Flood Prediction App")
+st.write("Upload a satellite image, and the model will predict flood-affected areas.")
+
+# Image uploader
+uploaded_file = st.file_uploader("Choose a satellite image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file:
+    # Load and display input image
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess image
+    transform = transforms.Compose([
+        transforms.Resize((256, 256)),
+        transforms.ToTensor()
+    ])
+    input_tensor = transform(image).unsqueeze(0)
+
+    # Predict flood mask
+    with torch.no_grad():
+        output = model(input_tensor)[0, 0].numpy()
+
+    # Binarize and display mask
+    mask = (output > 0.5).astype(np.uint8) * 255
+    mask_image = Image.fromarray(mask).resize(image.size)
+    st.image(mask_image, caption="Predicted Flood Mask", use_column_width=True)