Upload app.py

app.py

@@ -0,0 +1,132 @@
+import joblib
+import rasterio
+import numpy as np
+import gradio as gr
+import matplotlib.pyplot as plt
+import os
+
+
+model = joblib.load('isolation_forest_ndvi.pkl')
+print("Model loaded successfully!")
+
+
+TRAINING_FOLDER = 'NDVI_Images'
+training_files = sorted([f for f in os.listdir(TRAINING_FOLDER) if f.endswith('.tif')])
+print(f"Found {len(training_files)} training files")
+
+
+def predict_anomaly(ndvi_file):
+
+    print(f"Reading uploaded file: {ndvi_file}")
+    with rasterio.open(ndvi_file) as src:
+        user_ndvi = src.read(1)
+        print(f"User NDVI shape: {user_ndvi.shape}")
+
+    # Stack
+    full_stack = [user_ndvi]
+
+    # Only take up to 23 more images (or less if we don't have 24 total)
+    num_additional = min(23, len(training_files))
+    for i in range(num_additional):
+        file_path = os.path.join(TRAINING_FOLDER, training_files[i])
+        with rasterio.open(file_path) as src:
+            full_stack.append(src.read(1))
+
+    print(f"Total images in stack: {len(full_stack)}")
+
+    ndvi_stack = np.array(full_stack)  # shape: (time, height, width)
+    time, height, width = ndvi_stack.shape
+    print(f"Stack shape: {ndvi_stack.shape}")
+
+    # Reshape for model
+    X_new = ndvi_stack.reshape(time, height * width).T
+    print(f"Reshaped for prediction: {X_new.shape}")
+
+    # Predict anomalies
+    y_pred = model.predict(X_new)  # -1 = anomaly, 1 = normal
+    print(f"Predictions - Unique values: {np.unique(y_pred, return_counts=True)}")
+
+    anomaly_map = y_pred.reshape(height, width)
+    return anomaly_map, user_ndvi
+
+
+def show_anomaly(ndvi_file):
+    """Generate and display anomaly map"""
+    try:
+        anomaly_map, original_ndvi = predict_anomaly(ndvi_file)
+
+        # Create a figure with two subplots
+        fig, axes = plt.subplots(1, 2, figsize=(14, 6))
+
+        im1 = axes[0].imshow(original_ndvi, cmap='RdYlGn', vmin=-1, vmax=1)
+        axes[0].set_title('Original NDVI', fontsize=14, fontweight='bold')
+        axes[0].axis('off')
+        plt.colorbar(im1, ax=axes[0], fraction=0.046, pad=0.04)
+
+        # Plot anomaly map
+        anomaly_visual = (anomaly_map + 1) / 2  # Maps -1→0 (anomaly), 1→1 (normal)
+
+        im2 = axes[1].imshow(anomaly_visual, cmap='RdYlGn', vmin=0, vmax=1)
+        axes[1].set_title('Anomaly Detection\n(Red=Anomaly, Green=Normal)',
+                          fontsize=14, fontweight='bold')
+        axes[1].axis('off')
+        plt.colorbar(im2, ax=axes[1], fraction=0.046, pad=0.04,
+                     label='0=Anomaly, 1=Normal')
+
+        # Calculate statistics
+        num_anomalies = np.sum(anomaly_map == -1)
+        total_pixels = anomaly_map.size
+        pct_anomaly = (num_anomalies / total_pixels) * 100
+
+        fig.suptitle(f'Anomaly Detection Results\n{num_anomalies:,} anomalous pixels ({pct_anomaly:.2f}%)',
+                     fontsize=16, fontweight='bold', y=1.02)
+
+        plt.tight_layout()
+
+        # Save the figure
+        output_path = 'anomaly_result.png'
+        plt.savefig(output_path, dpi=150, bbox_inches='tight')
+        plt.close()
+
+        print(f"Saved result to {output_path}")
+        print(f"Anomaly statistics: {num_anomalies}/{total_pixels} pixels ({pct_anomaly:.2f}%)")
+
+        return output_path
+
+    except Exception as e:
+        print(f"Error in show_anomaly: {str(e)}")
+        import traceback
+        traceback.print_exc()
+
+        # Return an error image
+        fig, ax = plt.subplots(figsize=(8, 6))
+        ax.text(0.5, 0.5, f'Error processing image:\n{str(e)}',
+                ha='center', va='center', fontsize=12, color='red')
+        ax.axis('off')
+        error_path = 'error.png'
+        plt.savefig(error_path)
+        plt.close()
+        return error_path
+
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=show_anomaly,
+    inputs=gr.File(label="Upload NDVI GeoTIFF file (.tif)", file_types=['.tif']),
+    outputs=gr.Image(label="Anomaly Detection Result", type="filepath"),
+    title="🌿 Vegetation Anomaly Detection",
+    description="""
+    Upload an NDVI GeoTIFF image to detect vegetation anomalies using Isolation Forest.
+
+    - **Red areas**: Anomalous vegetation patterns
+    - **Green areas**: Normal vegetation patterns
+
+    The model uses temporal patterns from 24 months of training data to identify unusual changes.
+    """,
+    examples=None
+)
+
+if __name__ == "__main__":
+    print("\nStarting Gradio interface...")
+    print("Make sure 'isolation_forest_ndvi.pkl' and 'NDVI_Images/' folder are in the same directory")
+    iface.launch(share=False)