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https://github.com/kiritoind/emojify-with-lstm
Long Short-Term Memory (LSTM) model to predict emojis based on the input sentence
https://github.com/kiritoind/emojify-with-lstm
emojis lstm neural-network python tensorflow
Last synced: about 1 month ago
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Long Short-Term Memory (LSTM) model to predict emojis based on the input sentence
- Host: GitHub
- URL: https://github.com/kiritoind/emojify-with-lstm
- Owner: kiritoInd
- License: mit
- Created: 2024-08-29T06:04:06.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-08-29T06:07:56.000Z (3 months ago)
- Last Synced: 2024-10-11T04:41:40.321Z (about 1 month ago)
- Topics: emojis, lstm, neural-network, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 77.1 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE
Awesome Lists containing this project
README
# Emojify Your Sentence with LSTM
This project uses a Long Short-Term Memory (LSTM) model to predict emojis based on the input sentence. The model has been trained with an accuracy of 86% and is saved in the `emojify.h5` file. Additionally, a Flask application is provided to submit POST requests for emoji conversion. There is also a Jupyter Notebook available to help you get familiar with the project.
## Preview
## Project Structure
- `emojfier.h5`: The trained LSTM model.
- `Server.py`: The Flask application for handling POST requests.
- `EmojifierUsingLSTMs.ipynb`: A Jupyter Notebook to get familiar with the project and understand the model training process.
- `emo_utils.py`: utils.
## Getting Started
### Prerequisites
- Python 3.x
- Flask
- TensorFlow
- Jupyter Notebook
### Installation
1. Clone the repository:
```bash
git clone https://github.com/kiritoInd/Emojify-With-LSTM.git
cd Emojify-With-LSTM
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
### Usage
#### Running the Flask Application
1. Start the Flask server:
```bash
python server.py
```
2. Send a POST request to the server to convert a sentence to an emoji:
```bash
curl -X POST -H "Content-Type: application/json" -d '{"sentence": "your sentence here"}' http://127.0.0.1:5000/predict
```
#### Using the Jupyter Notebook
1. Open the Jupyter Notebook:
```bash
jupyter notebook notebook.ipynb
```
2. Follow the instructions in the notebook to understand the model training process and how to use the model for predictions.
## Model Details
The LSTM model is trained on a dataset of sentences and their corresponding emojis. The model architecture and training process are detailed in the Jupyter Notebook.
```
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type) ┃ Output Shape ┃ Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ input_layer (InputLayer) │ (None, 10) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ embedding_1 (Embedding) │ (None, 10, 50) │ 20,000,050 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ lstm (LSTM) │ (None, 10, 128) │ 91,648 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout) │ (None, 10, 128) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ lstm_1 (LSTM) │ (None, 128) │ 131,584 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout) │ (None, 128) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense) │ (None, 5) │ 645 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ activation (Activation) │ (None, 5) │ 0 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
```
## Contributing
Feel free to fork this repository and submit pull requests. For major changes, please open an issue first to discuss what you would like to change.
## License
This project is licensed under the MIT License.