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ReadMe.txt

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+# 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 Ahmed directly at [migdady@gmail.com](mailto:migdady@gmail.com).
+