Update app.py

app.py

@@ -1,165 +1,165 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib import image
-plt.style.use("fivethirtyeight")
-import PIL
-from PIL import Image, ImageFile
-import os, shutil
-from tqdm.auto import tqdm, trange
-import gradio as gr
-
-import torch, torchvision
-import torch.nn as nn
-from torchvision.transforms import v2 as v2
-import lightning.pytorch as pl
-from lightning.pytorch import LightningModule, LightningDataModule
-import tempfile
-
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
-import utils
-from utils.utils import prepare_image, make_preds_return_mask, load_model, get_gt
-
-
-def run_gradio_app():
-    def process_image(img_path_):
-        lightning_model = load_model()
-        image = prepare_image(path=img_path_)
-        mask_array = make_preds_return_mask(img=image, model=lightning_model)  # return numpy
-        gt_array = np.array(get_gt(img_path_))
-
-        # Visualization
-        gt_pil = Image.fromarray((gt_array * 255.0).astype(np.uint8)).resize((300,224))
-        mask_pil = Image.fromarray((mask_array * 255.0).astype(np.uint8)).resize((300,224))
-
-        # Flood ratio
-        flood_pixels = np.sum(mask_array > 0.5)
-        total_pixels = mask_array.size
-        flood_ratio = flood_pixels / total_pixels
-        if flood_ratio > 0.05:
-            status_text = (
-                f"Flood detected in approximately {flood_ratio*100:.2f}% of the area.\n\n"
-                "📌 Recommended Actions for Urban Planners:\n"
-                "- Prioritize evacuation and relief efforts in the most affected zones.\n"
-                "- Assess drainage and waterway capacity; reinforce weak infrastructure.\n"
-                "- Deploy flood barriers or temporary defenses where feasible.\n"
-                "- Coordinate with emergency services for rapid response.\n"
-                "- Use these flood maps for long-term planning: zoning, flood-resilient housing, "
-                "and sustainable water management strategies."
-            )
-        else:
-            status_text = (
-                "⚠️ No significant flooding detected.\n\n"
-                "📌 Recommendations:\n"
-                "- Continue monitoring the area, as conditions may change with rainfall.\n"
-                "- Maintain drainage systems to prevent localized flooding.\n"
-                "- Use this opportunity to strengthen flood preparedness measures "
-                "and update urban planning models with the latest satellite data."
-            )
-
-        # Save mask to temp file for download
-        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
-        mask_pil.save(temp_file.name)
-
-        return gt_pil, mask_pil, status_text, temp_file.name
-
-    title = "🌊 Flood Detection & Mapping (Sentinel-1 & Sentinel-2)"
-    
-    description = """
-    ## 🌍 Automated Flood Mapping & Evaluation Platform  
-
-    This platform leverages **Sentinel-2 satellite imagery** combined with a trained **UNet deep learning model** 
-    to deliver high-resolution flood detection and mapping. The system is designed to support **urban planners, 
-    emergency response teams, and environmental agencies** in making timely, data-driven decisions.  
-
-    ### 📊 What the System Provides
-    - **Automated Processing** of raw satellite imagery to generate accurate flood masks.  
-    - **Visual Insights** with side-by-side comparisons of **Ground Truth** and **Predicted Flood Masks**.  
-    - **Quantitative Summaries** including the percentage of land area inundated.  
-    - **Downloadable GIS-Ready Outputs** (flood masks) for integration into planning and analysis tools.  
-    - **Scalable Utility** across disaster response, environmental monitoring, climate adaptation, 
-    and risk assessment workflows.  
-
-    ### 🛠️ How to Use
-    1. Upload a **Sentinel-2 TIFF image** (preferably cloud-free).  
-    2. Let the model process and generate the predicted flood mask.  
-    3. Review the **visual outputs and textual summary** to assess flood extent.  
-    4. Download the mask for use in **GIS software** or further analysis.  
-
-    ⚡ *This tool bridges cutting-edge AI with remote sensing data to empower better decisions 
-    for disaster management and long-term resilience planning.*  
-    """
-
-    description2 = """
-    ### 🌐 Why This Matters
-    Flooding remains one of the most destructive natural disasters worldwide. Rapid, reliable detection 
-    is essential for:  
-    - Guiding **evacuation planning** and **emergency relief operations**.  
-    - Supporting **sustainable urban development** through flood-resilient zoning.  
-    - Enhancing **climate resilience strategies** for vulnerable regions.
-    """
-
-
-    professional_theme = gr.themes.Base(
-        primary_hue="blue",
-        secondary_hue="slate",
-    ).set(
-        button_primary_background_fill="linear-gradient(90deg, #0F4C75, #3282B8)",  
-        button_primary_background_fill_hover="linear-gradient(90deg, #3282B8, #0F4C75)",
-        button_primary_text_color="white",
-        button_primary_shadow="0px 2px 6px rgba(0,0,0,0.15)",
-        block_title_text_color="#0F3057",
-        body_background_fill="#F8FAFC",     # light gray background
-        block_background_fill="#FFFFFF",    # white for content cards
-        block_shadow="0px 2px 6px rgba(0,0,0,0.1)",
-        border_color_primary="#D1D5DB",
-    )
-
-    # ----------------------------
-    # Gradio App Layout
-    # ----------------------------
-    with gr.Blocks(theme=professional_theme) as demo:
-        
-        # ---- HEADER BAR ----
-        with gr.Row():
-            with gr.Column(scale = 1):
-                gr.Markdown(
-                    f"<h1 style='color:#0F3057; margin-bottom:0'>Flood Detection & Mapping Platform</h1>"
-                    "<p style='font-size:18px; color:#555;'>Powered by Sentinel-2 Imagery & Deep Learning</p>"
-                )
-
-        # ---- DESCRIPTION ----
-        gr.Markdown(description)
-
-        # ---- INPUT & STATUS ----
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.File(file_types=["image"], label="Upload Sentinel Image")
-                run_btn = gr.Button("Run Flood Detection", variant="primary")
-            with gr.Column():
-                status_box = gr.Textbox(label="Detection Summary", interactive=False)
-                gr.Markdown(description2)
-
-        # ---- OUTPUT VISUALS ----
-        with gr.Row():
-            gt_out = gr.Image(type="pil", label="Ground Truth")
-            pred_out = gr.Image(type="pil", label="Predicted Flood Mask")
-
-        # ---- DOWNLOAD ----
-        with gr.Row():
-            download_mask = gr.File(label="Download Predicted Mask")
-
-        # ---- BUTTON ACTION ----
-        run_btn.click(
-            fn=process_image,
-            inputs=[input_img],
-            outputs=[gt_out, pred_out, status_box, download_mask]
-        )
-        # gr.Markdown(description2)
-
-    demo.launch(share=False)
-
-
-if __name__ == "__main__":
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import image
+plt.style.use("fivethirtyeight")
+import PIL
+from PIL import Image, ImageFile
+import os, shutil
+from tqdm.auto import tqdm, trange
+import gradio as gr
+
+import torch, torchvision
+import torch.nn as nn
+from torchvision.transforms import v2 as v2
+import lightning.pytorch as pl
+from lightning.pytorch import LightningModule, LightningDataModule
+import tempfile
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+import utils
+from utils.utils import prepare_image, make_preds_return_mask, load_model, get_gt
+
+
+def run_gradio_app():
+    def process_image(img_path_):
+        lightning_model = load_model()
+        image = prepare_image(path=img_path_)
+        mask_array = make_preds_return_mask(img=image, model=lightning_model)  # return numpy
+        gt_array = np.array(get_gt(img_path_))
+
+        # Visualization
+        gt_pil = Image.fromarray(gt_array).resize((500,500))
+        mask_pil = Image.fromarray((mask_array * 255.0).astype(np.uint8)).resize((500,500))
+
+        # Flood ratio
+        flood_pixels = np.sum(mask_array > 0.5)
+        total_pixels = mask_array.size
+        flood_ratio = flood_pixels / total_pixels
+        if flood_ratio > 0.05:
+            status_text = (
+                f"Flood detected in approximately {flood_ratio*100:.2f}% of the area.\n\n"
+                "📌 Recommended Actions for Urban Planners:\n"
+                "- Prioritize evacuation and relief efforts in the most affected zones.\n"
+                "- Assess drainage and waterway capacity; reinforce weak infrastructure.\n"
+                "- Deploy flood barriers or temporary defenses where feasible.\n"
+                "- Coordinate with emergency services for rapid response.\n"
+                "- Use these flood maps for long-term planning: zoning, flood-resilient housing, "
+                "and sustainable water management strategies."
+            )
+        else:
+            status_text = (
+                "⚠️ No significant flooding detected.\n\n"
+                "📌 Recommendations:\n"
+                "- Continue monitoring the area, as conditions may change with rainfall.\n"
+                "- Maintain drainage systems to prevent localized flooding.\n"
+                "- Use this opportunity to strengthen flood preparedness measures "
+                "and update urban planning models with the latest satellite data."
+            )
+
+        # Save mask to temp file for download
+        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+        mask_pil.save(temp_file.name)
+
+        return gt_pil, mask_pil, status_text, temp_file.name
+
+    title = "🌊 Flood Detection & Mapping (Sentinel-1 & Sentinel-2)"
+    
+    description = """
+    ## 🌍 Automated Flood Mapping & Evaluation Platform  
+
+    This platform leverages **Sentinel-2 satellite imagery** combined with a trained **UNet deep learning model** 
+    to deliver high-resolution flood detection and mapping. The system is designed to support **urban planners, 
+    emergency response teams, and environmental agencies** in making timely, data-driven decisions.  
+
+    ### 📊 What the System Provides
+    - **Automated Processing** of raw satellite imagery to generate accurate flood masks.  
+    - **Visual Insights** with side-by-side comparisons of **Ground Truth** and **Predicted Flood Masks**.  
+    - **Quantitative Summaries** including the percentage of land area inundated.  
+    - **Downloadable GIS-Ready Outputs** (flood masks) for integration into planning and analysis tools.  
+    - **Scalable Utility** across disaster response, environmental monitoring, climate adaptation, 
+    and risk assessment workflows.  
+
+    ### 🛠️ How to Use
+    1. Upload a **Sentinel-2 TIFF image** (preferably cloud-free).  
+    2. Let the model process and generate the predicted flood mask.  
+    3. Review the **visual outputs and textual summary** to assess flood extent.  
+    4. Download the mask for use in **GIS software** or further analysis.  
+
+    ⚡ *This tool bridges cutting-edge AI with remote sensing data to empower better decisions 
+    for disaster management and long-term resilience planning.*  
+    """
+
+    description2 = """
+    ### 🌐 Why This Matters
+    Flooding remains one of the most destructive natural disasters worldwide. Rapid, reliable detection 
+    is essential for:  
+    - Guiding **evacuation planning** and **emergency relief operations**.  
+    - Supporting **sustainable urban development** through flood-resilient zoning.  
+    - Enhancing **climate resilience strategies** for vulnerable regions.
+    """
+
+
+    professional_theme = gr.themes.Base(
+        primary_hue="blue",
+        secondary_hue="slate",
+    ).set(
+        button_primary_background_fill="linear-gradient(90deg, #0F4C75, #3282B8)",  
+        button_primary_background_fill_hover="linear-gradient(90deg, #3282B8, #0F4C75)",
+        button_primary_text_color="white",
+        button_primary_shadow="0px 2px 6px rgba(0,0,0,0.15)",
+        block_title_text_color="#0F3057",
+        body_background_fill="#F8FAFC",     # light gray background
+        block_background_fill="#FFFFFF",    # white for content cards
+        block_shadow="0px 2px 6px rgba(0,0,0,0.1)",
+        border_color_primary="#D1D5DB",
+    )
+
+    # ----------------------------
+    # Gradio App Layout
+    # ----------------------------
+    with gr.Blocks(theme=professional_theme) as demo:
+        
+        # ---- HEADER BAR ----
+        with gr.Row():
+            with gr.Column(scale = 1):
+                gr.Markdown(
+                    f"<h1 style='color:#0F3057; margin-bottom:0'>Flood Detection & Mapping Platform</h1>"
+                    "<p style='font-size:18px; color:#555;'>Powered by Sentinel-2 Imagery & Deep Learning</p>"
+                )
+
+        # ---- DESCRIPTION ----
+        gr.Markdown(description)
+
+        # ---- INPUT & STATUS ----
+        with gr.Row():
+            with gr.Column():
+                input_img = gr.File(file_types=["image"], label="Upload Sentinel Image")
+                run_btn = gr.Button("Run Flood Detection", variant="primary")
+            with gr.Column():
+                status_box = gr.Textbox(label="Detection Summary", interactive=False)
+                gr.Markdown(description2)
+
+        # ---- OUTPUT VISUALS ----
+        with gr.Row():
+            gt_out = gr.Image(type="pil", label="Ground Truth")
+            pred_out = gr.Image(type="pil", label="Predicted Flood Mask")
+
+        # ---- DOWNLOAD ----
+        with gr.Row():
+            download_mask = gr.File(label="Download Predicted Mask")
+
+        # ---- BUTTON ACTION ----
+        run_btn.click(
+            fn=process_image,
+            inputs=[input_img],
+            outputs=[gt_out, pred_out, status_box, download_mask]
+        )
+        # gr.Markdown(description2)
+
+    demo.launch(share=False)
+
+
+if __name__ == "__main__":
     run_gradio_app()