Delete app.py

app.py

@@ -1,222 +0,0 @@
-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)
-
-        # Handle TIFF file - tif_file is now a file path (string)
-        tif_path = os.path.join(img_dir, f"input_{uuid.uuid4().hex}.tif")
-        shutil.copy2(tif_file, tif_path)
-
-        # Handle shapefile ZIP - shapefile_zip is now a file path (string)
-        zip_path = os.path.join(shp_dir, f"shapefile_{uuid.uuid4().hex}.zip")
-        shutil.copy2(shapefile_zip, zip_path)
-        
-        # 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="filepath"  # Changed from "binary" to "filepath"
-            )
-            shapefile_input = gr.File(
-                label="📁 Shapefile ZIP (.zip)", 
-                file_types=[".zip"],
-                type="filepath"  # Changed from "binary" to "filepath"
-            )
-            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
-    )
-