Update app.py

app.py

@@ -67,7 +67,7 @@ def train_random_forest_model():
         B2_resized = cv2.resize(B2_image, target_size, interpolation=cv2.INTER_NEAREST)
         return B2_resized.reshape(-1, 1)
 
-    data_dir = 'Data'  # Update to your local directory containing TIFF files
+    data_dir = 'Data'
     X_list = []
     y_list = []
 
@@ -164,19 +164,24 @@ def predict_deep_learning(model_name, file):
             predictions = model.predict(preprocessed_patches)
             predictions = predictions.reshape((n_patches_y, n_patches_x))
 
-            threshold = np.percentile(predictions, 90)
+            # Set a threshold to highlight areas with higher predicted yields
+            threshold = np.percentile(predictions, 90)  # Adjust the percentile as needed
 
+            # Create an overlay image to visualize predictions
             overlay = np.zeros_like(img_data, dtype=np.float32)
             for i in range(n_patches_y):
                 for j in range(n_patches_x):
                     if predictions[i, j] > threshold:
                         overlay[i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size] = predictions[i, j]
 
+            # Plot the overlay on the original image
             plt.figure(figsize=(10, 10))
             plt.imshow(img_data, cmap='gray', alpha=0.5)
             plt.imshow(overlay, cmap='jet', alpha=0.5)
             plt.title('Crop Yield Prediction Overlay')
             plt.colorbar()
+
+            # Save the plot to a file
             plt.savefig('/tmp/dl_prediction_overlay.png')
 
             return '/tmp/dl_prediction_overlay.png'