Update README.md

README.md

@@ -18,6 +18,8 @@ base_model:
 pipeline_tag: video-text-to-text
 ---
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65e21f5133d3600496498125/Jlt301igJbSI0K_wqCjVf.png)
+
 # Xbai-Epilepsy 1.0 Sürümü (TR)
 
 ## Tanım
@@ -49,4 +51,100 @@ It is available for personal use for everyone, primarily hospitals, health and s
 ### Classes
 
 - **No Epileptic Seizure**: The person is not in a seizure.
-- **Epilepsy Seizure Detected**: The person is having or about to have a seizure.
+- **Epilepsy Seizure Detected**: The person is having or about to have a seizure.
+  
+## ----------------------------------------
+
+# Kullanım / Usage
+
+```python
+import tensorflow as tf
+import numpy as np
+import pandas as pd
+from tensorflow.keras.models import load_model
+from tensorflow.keras import layers
+from PIL import Image
+import io
+
+
+def build_cnn(input_shape=(224, 224, 3)):
+    cnn_input = layers.Input(shape=input_shape)
+    x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(cnn_input)
+    x = layers.MaxPooling2D((2, 2))(x)
+    x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
+    x = layers.MaxPooling2D((2, 2))(x)
+    x = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(x)
+    x = layers.MaxPooling2D((2, 2))(x)
+    x = layers.Flatten()(x)
+    x = layers.Dense(256, activation='relu')(x)
+    x = layers.Dropout(0.5)(x)
+    cnn_output = layers.Dense(2, activation='softmax')(x)
+
+    cnn_model = models.Model(inputs=cnn_input, outputs=cnn_output)
+    return cnn_model
+
+
+def build_lstm(input_shape=(178, 2)):
+    lstm_input = layers.Input(shape=input_shape)
+    x = layers.LSTM(64, activation='relu')(lstm_input)
+    lstm_output = layers.Dense(2, activation='softmax')(x)
+
+    lstm_model = models.Model(inputs=lstm_input, outputs=lstm_output)
+    return lstm_model
+
+
+def bytes_to_image_array(byte_data):
+    image = Image.open(io.BytesIO(byte_data))
+    image = image.resize((224, 224))
+    image = image.convert('RGB')
+    image = np.array(image)
+    if image.shape == (224, 224, 3):
+        return image
+    else:
+        raise ValueError(f"Unexpected image shape: {image.shape}")
+
+
+model_path = 'model/path'
+multimodal_model = load_model(model_path)
+print("Successfully model uploaded.")
+
+multimodal_model.summary()
+
+data_path = 'image/parquet/data/path'
+data = pd.read_parquet(data_path)
+
+try:
+    X_images = np.array([bytes_to_image_array(img['bytes']) for img in data['image']])
+except ValueError as e:
+    print(e)
+
+eeg_data_path = 'epileptic/seizure/data/path'
+eeg_data = pd.read_csv(eeg_data_path)
+
+eeg_data.columns = eeg_data.columns.str.strip()
+eeg_data['data'] = eeg_data['data'].apply(eval)
+X_time_series = np.array(eeg_data['data'].to_list())
+y = eeg_data['label'].values
+
+X_images = X_images / 255.0
+
+X_test_images = X_images[:32]
+X_test_time_series = X_time_series[:32]
+y_test = y[:32]
+
+X_test_time_series = np.expand_dims(X_test_time_series, axis=-1)  # (None, 178, 1)
+X_test_time_series = np.repeat(X_test_time_series, 2, axis=-1)  # (None, 178, 2)
+
+test_loss, test_accuracy = multimodal_model.evaluate([X_test_images, X_test_time_series], y_test)
+print(f"Test Loss: {100 * test_loss}%")
+print(f"Test Accuracy: {100 * test_accuracy}%")
+
+y_pred = multimodal_model.predict([X_test_images, X_test_time_series])
+
+for i in range(50):
+    print(f"Sample {i+1}: Real Label: {y_test[i]}, Prediction: {np.argmax(y_pred[i])}")
+```
+
+# Python Sürümü / Python Version
+
+### 3.9 <=> 3.13