README.md

---
license: apache-2.0
---
# Emotion Recognition Model

This repository hosts a custom-tuned Emotion Recognition model designed to analyze facial expressions in real-time video streams. The model predicts seven distinct emotions: **Angry**, **Disgust**, **Fear**, **Happy**, **Neutral**, **Sad**, and **Surprise**, and calculates a positivity level based on the predicted emotions.

## Model Details

- **Model Architecture:** The model is based on a fine-tuned VGG16 architecture using TensorFlow.
- **Input Size:** 224x224 pixels
- **Emotion Labels:**
  - Angry
  - Disgust
  - Fear
  - Happy
  - Neutral
  - Sad
  - Surprise

## How to Use

1. Clone the repository and install the dependencies:

```bash
pip install -r requirements.txt
```

2. Load the model and run predictions on images:

```python
from tensorflow.keras.models import load_model
import cv2
import numpy as np
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications.vgg16 import preprocess_input

model = load_model('emotion_model_finetuned224.h5')

# Example usage with an image
image = cv2.imread('example.jpg')
face = cv2.resize(image, (224, 224))
face = img_to_array(face)
face = preprocess_input(face)
face = np.expand_dims(face, axis=0)
predictions = model.predict(face)
print(predictions)
```

## Gradio Interface (Optional)

If you want to deploy the model using Gradio, you can use the `app.py` file provided:

```bash
pip install gradio
python app.py
```

Visit the Gradio interface at `http://localhost:7860` and upload an image to see the predicted emotion.

## Dependencies

- `tensorflow`
- `opencv-python`
- `numpy`
- `requests`
- `gradio` (for optional web interface)

## License

This project is licensed under the MIT License. Feel free to use and modify as needed.

## Contact

For questions or support, please open an issue on the GitHub repository or contact Ahmad directly at [migdady@gmail.com](mailto:migdady@gmail.com).