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app.py

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+import gradio as gr
+import os
+import tempfile
+import zipfile
+import uuid
+import rasterio
+import numpy as np
+from rasterio.mask import mask
+import geopandas as gpd
+import matplotlib.pyplot as plt
+from io import BytesIO
+from PIL import Image
+import shutil
+
+
+def clip_geotiff(tif_file, shapefile_zip):
+    """
+    Clips a GeoTIFF file using a shapefile
+    """
+    temp_dir = None
+    try:
+        # Check if files were provided
+        if tif_file is None or shapefile_zip is None:
+            return None, None
+        
+        # Create unique temporary directory
+        temp_dir = tempfile.mkdtemp(prefix="geotiff_clip_")
+        img_dir = os.path.join(temp_dir, "image")
+        shp_dir = os.path.join(temp_dir, "shapefile")
+        out_dir = os.path.join(temp_dir, "output")
+        
+        # Create subdirectories
+        for directory in [img_dir, shp_dir, out_dir]:
+            os.makedirs(directory, exist_ok=True)
+
+        # Save TIFF file
+        tif_path = os.path.join(img_dir, f"input_{uuid.uuid4().hex}.tif")
+        with open(tif_path, "wb") as f:
+            f.write(tif_file.read())
+
+        # Save and extract shapefile ZIP
+        zip_path = os.path.join(shp_dir, f"shapefile_{uuid.uuid4().hex}.zip")
+        with open(zip_path, "wb") as f:
+            f.write(shapefile_zip.read())
+        
+        # Extract ZIP
+        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+            zip_ref.extractall(shp_dir)
+
+        # Find .shp file
+        shp_files = [f for f in os.listdir(shp_dir) if f.endswith(".shp")]
+        if not shp_files:
+            raise FileNotFoundError(".shp file not found in the provided ZIP.")
+        
+        shp_file = os.path.join(shp_dir, shp_files[0])
+
+        # Read shapefile using geopandas
+        gdf = gpd.read_file(shp_file)
+        
+        # Check if shapefile has valid geometries
+        if gdf.empty or gdf.geometry.isna().all():
+            raise ValueError("Shapefile does not contain valid geometries.")
+
+        # Open and process GeoTIFF file
+        with rasterio.open(tif_path) as src:
+            # Check for overlap between shapefile and raster
+            gdf_proj = gdf.to_crs(src.crs)
+            
+            # Perform clipping
+            out_image, out_transform = mask(src, gdf_proj.geometry, crop=True, nodata=src.nodata)
+            
+            # Update metadata
+            out_meta = src.meta.copy()
+            out_meta.update({
+                "height": out_image.shape[1],
+                "width": out_image.shape[2],
+                "transform": out_transform,
+                "nodata": src.nodata
+            })
+
+            # Save clipped GeoTIFF
+            output_filename = f"clipped_{uuid.uuid4().hex}.tif"
+            output_tif_path = os.path.join(out_dir, output_filename)
+            
+            with rasterio.open(output_tif_path, "w", **out_meta) as dest:
+                dest.write(out_image)
+
+        # Create PNG visualization in memory
+        # Prepare data for visualization
+        if out_image.shape[0] >= 3:
+            # If has 3 or more bands, use first 3 (RGB)
+            preview_array = out_image[:3]
+        else:
+            # If has less than 3 bands, repeat first band
+            preview_array = np.repeat(out_image[0:1], 3, axis=0)
+        
+        # Rearrange dimensions (bands, height, width) -> (height, width, bands)
+        preview_array = np.moveaxis(preview_array, 0, -1)
+        
+        # Normalize values to 0-255 if necessary
+        if preview_array.dtype != np.uint8:
+            # Normalize to 0-255
+            preview_min = np.nanmin(preview_array)
+            preview_max = np.nanmax(preview_array)
+            if preview_max > preview_min:
+                preview_array = ((preview_array - preview_min) / (preview_max - preview_min) * 255).astype(np.uint8)
+            else:
+                preview_array = np.zeros_like(preview_array, dtype=np.uint8)
+        
+        # Handle nodata values
+        if out_meta.get('nodata') is not None:
+            nodata_mask = np.any(out_image == out_meta['nodata'], axis=0)
+            preview_array[nodata_mask] = [0, 0, 0]  # Black for nodata
+
+        # Create matplotlib figure
+        plt.style.use('default')  # Ensure default style
+        fig, ax = plt.subplots(figsize=(10, 8), dpi=100)
+        ax.imshow(preview_array)
+        ax.set_title(f"Clipped GeoTIFF - {output_filename}", fontsize=12, pad=20)
+        ax.axis('off')
+        
+        # Save to memory buffer
+        buf = BytesIO()
+        plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0.1, dpi=150)
+        plt.close(fig)  # Important: close figure to free memory
+        buf.seek(0)
+
+        # Convert to PIL image
+        pil_image = Image.open(buf).convert("RGB")
+        buf.close()
+
+        return pil_image, output_tif_path
+
+    except Exception as e:
+        error_msg = f"Error during processing: {str(e)}"
+        print(f"[ERROR] {error_msg}")
+        
+        # Create error image
+        error_image = Image.new('RGB', (400, 200), color='white')
+        from PIL import ImageDraw, ImageFont
+        draw = ImageDraw.Draw(error_image)
+        try:
+            font = ImageFont.truetype("arial.ttf", 16)
+        except:
+            font = ImageFont.load_default()
+        
+        draw.text((10, 10), "Processing Error:", fill='red', font=font)
+        draw.text((10, 40), str(e)[:50] + "..." if len(str(e)) > 50 else str(e), fill='black', font=font)
+        
+        return error_image, None
+    
+    finally:
+        # Clean up temporary directory
+        if temp_dir and os.path.exists(temp_dir):
+            try:
+                shutil.rmtree(temp_dir)
+            except:
+                pass  # Ignore cleanup errors
+
+
+# Gradio Interface
+with gr.Blocks(title="GeoTIFF Clipper", theme=gr.themes.Soft()) as app:
+    gr.Markdown("""
+    # 🛰️ GeoTIFF Clipping with Shapefile
+    
+    This tool allows you to clip GeoTIFF images using shapefiles as clipping masks.
+    
+    **Instructions:**
+    1. Upload a GeoTIFF file (.tif)
+    2. Upload a ZIP file containing the shapefile (.shp, .dbf, .shx, .prj)
+    3. Click "Execute Clipping"
+    4. View the result and download the clipped file
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            geotiff_input = gr.File(
+                label="📁 GeoTIFF File (.tif)", 
+                file_types=[".tif", ".tiff"],
+                type="binary"
+            )
+            shapefile_input = gr.File(
+                label="📁 Shapefile ZIP (.zip)", 
+                file_types=[".zip"],
+                type="binary"
+            )
+            run_button = gr.Button("🚀 Execute Clipping", variant="primary", size="lg")
+
+    with gr.Row():
+        with gr.Column():
+            preview_output = gr.Image(
+                label="🖼️ Result Preview", 
+                type="pil",
+                height=400
+            )
+        with gr.Column():
+            tif_output = gr.File(
+                label="💾 Download Clipped GeoTIFF",
+                type="filepath"
+            )
+
+    # Connect function to button
+    run_button.click(
+        fn=clip_geotiff, 
+        inputs=[geotiff_input, shapefile_input], 
+        outputs=[preview_output, tif_output]
+    )
+
+    gr.Markdown("""
+    ---
+    **Notes:**
+    - The shapefile ZIP must contain at least .shp, .dbf and .shx files
+    - Coordinate systems will be automatically adjusted if necessary
+    - Preview is automatically generated for visualization
+    """)
+
+# Configure for Hugging Face Spaces
+if __name__ == "__main__":
+    app.launch(
+        server_name="0.0.0.0",  # Required for Hugging Face Spaces
+        server_port=7860,       # Default Hugging Face Spaces port
+        share=False             # Don't create additional public link
+    )
+

requirements.txt

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+gradio>=4.0.0
+rasterio>=1.3.0
+geopandas>=0.13.0
+matplotlib>=3.6.0
+numpy>=1.21.0
+Pillow>=9.0.0
+fiona>=1.8.0
+shapely>=2.0.0
+pyproj>=3.4.0
+