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README.md

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+---
+tags:
+- emotion-detection
+- tflite
+- facial-expression
+- computer-vision
+license: mit
+---
+
+# Meme Emotion Detector 🎭
+
+TFLite model untuk deteksi ekspresi wajah real-time dengan 7 emosi.
+
+## Model Details
+
+- **Model Type:** TensorFlow Lite
+- **Task:** Image Classification (Facial Expression Recognition)
+- **Emotions:** angry, disgust, fear, happy, sad, surprise, neutral
+- **Input:** 48x48 grayscale image
+- **Framework:** TensorFlow Lite
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import tensorflow as tf
+import numpy as np
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="maftuh-main/meme-emotion-detector",
+    filename="model.tflite"
+)
+
+# Load TFLite model
+interpreter = tf.lite.Interpreter(model_path=model_path)
+interpreter.allocate_tensors()
+
+# Get input/output details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Prepare image (48x48 grayscale, normalized)
+# face_img = ... (your face image)
+# face_resized = cv2.resize(face_img, (48, 48))
+# face_gray = cv2.cvtColor(face_resized, cv2.COLOR_BGR2GRAY)
+# face_input = face_gray.astype(np.float32) / 255.0
+# face_input = np.expand_dims(face_input, axis=[0, -1])
+
+# Run inference
+# interpreter.set_tensor(input_details[0]['index'], face_input)
+# interpreter.invoke()
+# predictions = interpreter.get_tensor(output_details[0]['index'])[0]
+
+# Get emotion
+emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
+# emotion_idx = np.argmax(predictions)
+# detected_emotion = emotions[emotion_idx]
+```
+
+## Training
+
+Model trained on FER2013 dataset atau custom facial expression dataset.
+
+## Files
+
+- `model.tflite` - TensorFlow Lite model (1.4 MB)
+
+## Applications
+
+- Real-time emotion detection
+- Meme/emoji overlay based on expression
+- Interactive applications
+- Computer vision projects
+
+## License
+
+MIT License