Update README.md

README.md

@@ -1,3 +1,50 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+# 🧠 Brain MRI Classification with TensorFlow and Streamlit
+
+## 🇬🇧 English
+
+This project is a deep learning-based solution for classifying Brain MRI images into four categories: **Glioma**, **Meningioma**, **Pituitary**, and **Normal**. The classification model is trained using TensorFlow/Keras and can be used either programmatically or with a Streamlit web interface.
+
+### 🧪 Model Information
+
+- Input shape: `(224, 224, 3)`
+- Model format: `.keras` (SavedModel)
+- Output classes:
+  - 0: Glioma
+  - 1: Meningioma
+  - 2: Normal
+  - 3: Pituitary
+
+---
+
+### 🧠 Example Usage (Python Script)
+
+```python
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Load the trained model
+model = load_model('brainmri.keras')
+
+# Load and preprocess the image
+img = image.load_img('example.jpeg', target_size=(224, 224))
+img_array = image.img_to_array(img) / 255.0
+img_array = np.expand_dims(img_array, axis=0)
+
+# Define class labels
+classes = {
+    0: "Glioma",
+    1: "Meningioma",
+    2: "Normal",
+    3: "Pituitary"
+}
+
+# Make prediction
+result = model.predict(img_array)
+predicted_class = np.argmax(result)
+
+# Print result
+print("Predicted Class:", classes.get(predicted_class))