this is pushing mew visual model to resnet50

app.py

@@ -12,6 +12,7 @@ from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Input
 from tensorflow.keras.optimizers import Adam
 from PIL import Image
 import rasterio
+import matplotlib.pyplot as plt
 from tensorflow.keras.applications import ResNet50
 from tensorflow.keras.models import Model
 
@@ -104,7 +105,27 @@ def predict_deep_learning(model_name, file):
             predictions = model.predict(preprocessed_patches)
             predictions = predictions.reshape((n_patches_y, n_patches_x))
 
-            return predictions
+            # 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/prediction_overlay.png')
+
+            return '/tmp/prediction_overlay.png'
         else:
             return "No file uploaded"
     else:
@@ -123,7 +144,7 @@ inputs_deep_learning = [
     gr.Dropdown(choices=list(deep_learning_models.keys()), label='Model'),
     gr.File(label='Upload TIFF File')
 ]
-outputs_deep_learning = gr.Textbox(label='Predictions')
+outputs_deep_learning = gr.Image(label='Prediction Overlay')
 
 with gr.Blocks() as demo:
     with gr.Tab("Traditional ML Models"):