Create README.md

README.md

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+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+library_name: keras
+tags:
+- computer-vision
+- image-classification
+- tensorflow
+- keras
+- emotion-detection
+---
+# Emotion Classifier (Happy vs. Sad)
+
+## Model Description
+This is a custom **Convolutional Neural Network (CNN)** built using TensorFlow and Keras. The model is designed to perform binary image classification to distinguish between "Happy" and "Sad" facial expressions.
+
+- **Model Type:** CNN (Sequential)
+- **Task:** Binary Image Classification
+- **Framework:** TensorFlow/Keras
+
+## Training Data
+The model was trained on a localized dataset of approximately 300 images. 
+- **Preprocessing:** Images were resized to 256x256 pixels and normalized (pixel values scaled between 0 and 1).
+- **Data Integrity:** A pre-training script was used to validate image headers and remove corrupted files.
+
+
+
+[Image of a convolutional neural network architecture]
+
+
+## Performance
+During evaluation, the model achieved the following results:
+- **Training Accuracy:** 98.9%
+- **Validation Accuracy:** 96.9%
+- **Precision:** 1.0 (on test batch)
+- **Recall:** 1.0 (on test batch)
+
+
+
+## How to Use
+To load this model in Python:
+
+```python
+from tensorflow.keras.models import load_model
+import cv2
+import numpy as np
+
+model = load_model('imageclassifier.h5')
+img = cv2.imread('your_image.jpg')
+resize = tf.image.resize(img, (256, 256))
+prediction = model.predict(np.expand_dims(resize/255, 0))
+
+if prediction > 0.5:
+    print('Predicted: Sad')
+else:
+    print('Predicted: Happy')