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:

   git clone https://github.com/kiritoInd/Emojify-With-LSTM.git
   cd Emojify-With-LSTM

2. Install the required packages:

   pip install -r requirements.txt

Usage

Running the Flask Application

1. Start the Flask server:

   python server.py

2. Send a POST request to the server to convert a sentence to an emoji:

   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:

   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.