Update README.md

README.md

@@ -34,9 +34,9 @@ This repository contains **JarviSpore**, a mushroom image classification model t
 ## Model Training
 
 The model was trained using a structured dataset directory with data split as follows:
-- `train`: Balanced training dataset
-- `validation`: Validation set to monitor performance
-- `test`: Test set to evaluate final accuracy
+- train: Balanced training dataset
+- validation: Validation set to monitor performance
+- test: Test set to evaluate final accuracy
 
 Main training hyperparameters include:
 - **Batch Size**: 32
@@ -50,5 +50,62 @@ Training was tracked and logged via MLflow, including accuracy and loss curves,
 ### Prerequisites
 
 Ensure the following libraries are installed:
-```bash
+bash
 pip install tensorflow pillow matplotlib numpy
+
+
+### Loading the Model
+
+To load and use the model for predictions:
+
+python
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load the model
+model = tf.keras.models.load_model("path_to_model.h5")
+
+# Prepare an image for prediction
+def prepare_image(image_path):
+img = Image.open(image_path).convert("RGB")
+img = img.resize((224, 224))
+img_array = tf.keras.preprocessing.image.img_to_array(img)
+img_array = np.expand_dims(img_array, axis=0)
+return img_array
+
+# Prediction
+image_path = "path_to_image.jpg"
+img_array = prepare_image(image_path)
+predictions = model.predict(img_array)
+predicted_class = np.argmax(predictions[0])
+
+print(f"Predicted Class: {predicted_class}")
+
+
+### Grad-CAM Visualization
+
+The integrated *Grad-CAM* functionality allows interpretation of the model's predictions. To use it, select an image and apply the Grad-CAM function to display the heatmap overlaid on the original image, highlighting areas influencing the model.
+
+Grad-CAM example usage:
+
+python
+# Example usage of the make_gradcam_heatmap function
+heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name="last_conv_layer_name")
+
+# Superimpose the heatmap on the original image
+superimposed_img = superimpose_heatmap(Image.open(image_path), heatmap)
+superimposed_img.show()
+
+
+## Evaluation
+
+The model was evaluated on the test set with an average accuracy above random chance, showing promising results for a first from-scratch version.
+
+## Contributing
+
+Contributions to improve accuracy or add new features (e.g., other visualization techniques or advanced optimization) are welcome. Please submit a pull request with relevant modifications.
+
+## License
+
+This model is licensed under a controlled license: please refer to the LICENSE file for details. You may use this model for personal projects, but any modifications or redistribution must be approved.