Add README config

README.md

@@ -1,357 +1,7 @@
-<!-- anchor tag for back-to-top links -->
-<a name="readme-top"></a>
-
-<!-- HEADER IMAGE  -->
-<img src="images/header_image.png">
-
-<!-- SHORT SUMMARY  -->
-Implemented a hate speech detector for social media comments using deep learning. The fine-tuned BERT model achieved 78% accuracy on the Ethos Hate Speech Dataset, outperforming SimpleRNN/LSTM baselines, and was deployed via a web application and API.
-
----
-
-## Table of Contents
-<ol>
-  <li>
-    <a href="#about-the-project">About The Project</a>
-    <ul>
-      <li><a href="#summary">Summary</a></li>
-      <li><a href="#built-with">Built With</a></li>
-    </ul>
-  </li>
-  <li>
-    <a href="#motivation">Motivation</a>
-  </li>
-  <li>
-    <a href="#data">Data</a>
-  </li>
-  <li>
-    <a href="#model-building">Model Building</a>
-  </li>
-  <li>
-    <a href="#model-performance">Model Performance</a>
-  </li>
-    <ul>
-      <li><a href="#accuracy">Accuracy</a></li>
-      <li><a href="#classification-report">Classification Report</a></li>
-      <li><a href="#confusion-matrix">Confusion Matrix</a></li>
-      <li><a href="#illustrative-examples">Illustrative Examples</a></li>
-    </ul>
-  <li>
-    <a href="#model-deployment">Model Deployment</a>
-  </li>
-    <ul>
-      <li><a href="#web-application">Web Application</a></li>
-      <li><a href="#api">API</a></li>
-    </ul>
-  <li>
-    <a href="#getting-started">Getting Started</a>
-    <ul>
-      <li><a href="#prerequisites-for-model-training">Prerequisites for Model Training</a></li>
-      <li><a href="#prerequisites-for-model-deployment">Prerequisites for Model Deployment</a></li>
-    </ul>
-  </li>
-  <li>
-    <a href="#appendix">Appendix</a>
-    <ul>
-      <li><a href="#simplernn-preprocessing-model-architecture-and-hyperparameters">SimpleRNN: Preprocessing, Model Architecture and Hyperparameters</a></li>
-    </ul>
-    <ul>
-      <li><a href="#lstm-preprocessing-model-architecture-and-hyperparameters">LSTM: Preprocessing, Model Architecture and Hyperparameters</a></li>
-    </ul>
-    <ul>
-      <li><a href="#fine-tuned-bert-preprocessing-model-architecture-and-hyperparameters">Fine-Tuned BERT: Preprocessing, Model Architecture and Hyperparameters</a></li>
-    </ul>
-  </li>
-</ol>
-
-
-<!-- ABOUT THE PROJECT -->
-## About The Project
-
-### Summary
-+ Motivation: Develop a hate speech detector for social media comments. 
-+ Data: Utilized the [ETHOS Hate Speech Detection Dataset](https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset).
-+ Models: The fine-tuned BERT model demonstrated superior performance (78.0% accuracy) compared to the SimpleRNN (66.3%) and LSTM (70.7%) models.
-+ Deployment: The fine-tuned BERT model was prepared for production by integrating it into a web application and an API endpoint.
-
-### Built With
-* [![TensorFlow][TensorFlow-badge]][TensorFlow-url]
-* [![scikit-learn][scikit-learn-badge]][scikit-learn-url]
-* [![NumPy][NumPy-badge]][NumPy-url]
-* [![Pandas][Pandas-badge]][Pandas-url]
-* [![Matplotlib][Matplotlib-badge]][Matplotlib-url]
-* [![Flask][Flask-badge]][Flask-url]
-* [![Python][Python-badge]][Python-url]
-* [![Spyder][Spyder-badge]][Spyder-url]
-* ![HTML5][HTML5-badge]
-* ![CSS3][CSS3-badge]
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-
-<!-- Motivation -->
-## Motivation
-+ Problem: Hate speech is on the rise globally, especially on social media platforms (source: [United Nations](https://www.un.org/en/hate-speech/understanding-hate-speech/what-is-hate-speech)).
-+ Project goal: Utilize deep learning for hate speech detection in social media comments.
-+ Definition of hate speech: Insulting public speech directed at specific individuals or groups on the basis of characteristics such as race, religion, ethnic origin, national origin, sex, disability, sexual orientation, or gender identity ([Mollas, Chrysopoulou, Karlos, & Tsoumakas, 2022](https://link.springer.com/article/10.1007/s40747-021-00608-2)).
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-
-<!-- Data -->
-## Data
-+ 998 comments from YouTube and Reddit validated using the Figure-Eight crowdsourcing platform.
-+ Dataset: [ETHOS Hate Speech Detection Dataset](https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset).
-+ Balanced data: 43.4% hate speech.
-+ Comment length: Mean = 112 words (std = 160).
-
-![Comment length histogram](plots/histogram_comment_length.png)
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-
-<!-- Model Building -->
-## Model Building
-Benchmark models ([Mollas, Chrysopoulou, Karlos, & Tsoumakas, 2022](https://link.springer.com/article/10.1007/s40747-021-00608-2)):
-+ Random Forest: 65.0% Accuracy
-+ Support Vector Machine: 66.4% Accuracy
-
-Comparison of three deep learning models:
-+ SimpleRNN
-  + Preprocessing, model architecture and hyperparameters: [See details](#simplernn-preprocessing-model-architecture-and-hyperparameters)
-+ LSTM
-  + Preprocessing, model architecture and hyperparameters: [See details](#lstm-preprocessing-model-architecture-and-hyperparameters)
-+ Fine-tuned BERT
-  + Implementation with TensorFlow Hub
-  + Small BERT model: [small_bert/bert_en_uncased_L-4_H-512_A-8](https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/2)
-  + Preprocessing, model architecture and hyperparameters: [See details](#fine-tuned-bert-preprocessing-model-architecture-and-hyperparameters)
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-
-<!-- Model Performance -->
-## Model Performance
-### Accuracy
-|                   | SimpleRNN | LSTM     | Fine-Tuned BERT |
-|-------------------|-----------|----------|-----------------|
-| Training Accuracy | 91.8%     | 100%     | 99.9%           |
-| Test Accuracy     | 66.3%     | 70.7%    | 78.0%           |
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-### Classification Report
-The following classification reports present the performance metrics of the trained models on the test data.
-
-**SimpleRNN**
-|                 | Precision | Recall | F1 Score |
-|-----------------|-----------|--------|----------|
-| No Hate Speech  | 0.69      | 0.71   | 0.70     |
-| Hate Speech     | 0.63      | 0.61   | 0.62     |
-| Accuracy        |           |        | 0.66     |
-
-**LSTM**
-|                 | Precision | Recall | F1 Score |
-|-----------------|-----------|--------|----------|
-| No Hate Speech  | 0.73      | 0.75   | 0.74     |
-| Hate Speech     | 0.68      | 0.66   | 0.67     |
-| Accuracy        |           |        | 0.71     |
-
-**Fine-Tuned BERT**
-|                 | Precision | Recall | F1 Score |
-|-----------------|-----------|--------|----------|
-| No Hate Speech  | 0.84      | 0.74   | 0.79     |
-| Hate Speech     | 0.72      | 0.83   | 0.77     |
-| Accuracy        |           |        | 0.78     |
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-### Confusion Matrix
-Confusion matrices of the three models based on the test data.
-| SimpleRNN              | LSTM              | Fine-Tuned BERT              |
-|-----------------------|-----------------------|-----------------------|
-| ![Model 1 Confusion Matrix](./plots/model1_confusion_matrix.png) | ![Model 2 Confusion Matrix](./plots/model2_confusion_matrix.png) | ![Model 3 Confusion Matrix](./plots/model3_confusion_matrix.png) |
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-### Illustrative Examples
-Here are some example comments from the test data along with their true label and model predictions.
-
-|          | True Label | SimpleRNN | LSTM    | Fine-Tuned BERT |
-|----------|----------|----------|----------|----------|
-|  Women deserve to be abused, I guess. | Hate | Hate | Hate | Hate |
-| Christians are all demonic. | Hate | No Hate | Hate | Hate |
-| Muslims want war! So take it to them first | Hate | Hate | No Hate | Hate |
-| Fat lesbians | Hate | Hate | Hate | Hate |
-| What imaginary gender are you? | Hate | No Hate | No Hate | Hate |
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-
-<!-- MODEL DEPLOYMENT -->
-## Model Deployment
-I deployed the fine-tuned BERT model through both a web application and an API.
-
-### Web Application
-Utilizing the Flask framework, HTML, and CSS, I created a web application with a user-friendly interface for users to input text and receive predictions on whether it contains hate speech.
-
-<img src="./images/model_deployment_01.PNG" alt="Deployment example 1" style="width: 300px;"> <img src="./images/model_deployment_02.PNG" alt="Deployment example 2" style="width: 300px;">
-
-### API
-I developed an API endpoint to enable integration with other applications or services by leveraging the Flask framework and utilized <a href="https://www.postman.com/">Postman</a> for testing and documenting the API.
-
-API documentation: [See here](https://documenter.getpostman.com/view/28394113/2s946eBERv)
-
-![Model deployment API](/images/model_deployment_api.gif)
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-
-<!-- GETTING STARTED -->
-## Getting Started
-
-### Prerequisites for Model Training
-This is a list of the Python packages you need.  
-<ul>
-  <li>TensorFlow</li>
-  <li>TensorFlow Hub</li>
-  <li>TensorFlow Text</li>
-  <li>Scikit-Learn</li>
-  <li>NumPy</li>
-  <li>Pandas</li>
-  <li>Matplotlib</li>
-</ul>
-
-### Prerequisites for Model Deployment
-This is a list of the Python packages you need. 
-<ul>
-  <li>TensorFlow</li>
-  <li>TensorFlow Text</li>
-  <li>NumPy</li>
-  <li>Flask</li>
-  <li>Flask-WTF</li>
-  <li>WTForms</li>
-  <li>Python-dotenv</li>
-</ul>
-
-To enhance security, create a `.env` file and create a secret key for the Flask application. Store the secret key in the `.env` file and utilize the `python-dotenv` library to retrieve it. 
-  ```
-  SECRET_KEY = "Your_secret_key_here"
-  ```
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-
-<!-- APPENDIX -->
-## Appendix
-### SimpleRNN: Preprocessing, Model Architecture and Hyperparameters
-
-**Preprocessing**   
-Tokenizer vocabulary size: 5000  
-Padded sequence length: 15  
-Embedding dimension: 50
-
-**Model Architecture**
-| Layer (type) | Output Shape    | Param # | Activation |
-| ------------ | --------------- | ------- | ---------- |
-| Embedding    | (None, 15, 50)  | 250050  |            |
-| SimpleRNN    | (None, 15, 128) | 22912   | tanh       |
-| SimpleRNN    | (None, 128)     | 32896   | tanh       |
-| Dense        | (None, 64)      | 8256    | relu       |
-| Dense        | (None, 1)       | 65      | sigmoid    |
-
-Total params: 314,179  
-Trainable params: 314,179  
-Non-trainable params: 0
-
-**Hyperparameters**  
-Optimizer: Adam  
-Learning rate: 0.001  
-Loss: Binary Crossentropy  
-Epochs: 100  
-Batch size: 8  
-Dropout rate: 50%  
-Early stopping metric: Accuracy
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-### LSTM: Preprocessing, Model Architecture and Hyperparameters
-
-
-**Preprocessing**   
-Tokenizer vocabulary size: 5000  
-Padded sequence length: 150  
-Embedding dimension: 50
-
-**Model Architecture**
-| Layer (type) | Output Shape     | Param # | Activation |
-| ------------ | ---------------- | ------- | ---------- |
-| Embedding    | (None, 150, 50)  | 250050  |            |
-| LSTM         | (None, 150, 128) | 91648   | tanh       |
-| LSTM         | (None, 128)      | 131584  | tanh       |
-| Dense        | (None, 64)       | 8256    | relu       |
-| Dense        | (None, 1)        | 65      | sigmoid    |
-
-Total params: 481,603  
-Trainable params: 481,603  
-Non-trainable params: 0 
-
-**Hyperparameters**  
-Optimizer: Adam  
-Learning rate: 0.001  
-Loss: Binary Crossentropy  
-Epochs: 100  
-Batch size: 32  
-Dropout rate: 50%  
-Early stopping metric: Accuracy
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-### Fine-Tuned BERT: Preprocessing, Model Architecture and Hyperparameters
-
-**Preprocessing**   
-Text preprocessing for BERT models: https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3
-
-**Model Architecture**
-| Layer (type)  | Output Shape     | Param #  | Activation |
-| ------------- | ---------------- | -------- | ---------- |
-| Text Input    | [(None,)]        | 0        |            |
-| Preprocessing | input_type_ids: (None, 128)<br> input_mask: (None, 128)<br> input_word_ids: (None, 128) | 0        |            |
-| BERT          | (None, 512)      | 28763649 |            |
-| Dropout       | (None, 512)      | 0        |            |
-| Dense         | (None, 128)      | 65664    | relu       |
-| Dense         | (None, 1)        | 129      | sigmoid    |
-
-Total params: 28,829,442  
-Trainable params: 28,829,441  
-Non-trainable params: 1 
-
-**Hyperparameters**  
-Optimizer: Adam  
-Learning rate: 0.0001  
-Loss: Binary Crossentropy  
-Epochs: 100  
-Batch size: 8  
-Dropout rate: 50%  
-Early stopping metric: Accuracy
-
-<p align="right">(<a href="#readme-top">back to top</a>)</p>
-
-<!-- MARKDOWN LINKS -->
-[TensorFlow-badge]: https://img.shields.io/badge/TensorFlow-%23FF6F00.svg?style=for-the-badge&logo=TensorFlow&logoColor=white
-[TensorFlow-url]: https://www.tensorflow.org/
-[scikit-learn-badge]: https://img.shields.io/badge/scikit--learn-%23F7931E.svg?style=for-the-badge&logo=scikit-learn&logoColor=white
-[scikit-learn-url]: https://scikit-learn.org/stable/
-[NumPy-badge]: https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white
-[NumPy-url]: https://numpy.org/
-[Pandas-badge]: https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white
-[Pandas-url]: https://pandas.pydata.org/
-[Matplotlib-badge]: https://img.shields.io/badge/Matplotlib-%23ffffff.svg?style=for-the-badge&logo=Matplotlib&logoColor=black
-[Matplotlib-url]: https://matplotlib.org/
-[Flask-badge]: https://img.shields.io/badge/flask-%23000.svg?style=for-the-badge&logo=flask&logoColor=white
-[Flask-url]: https://flask.palletsprojects.com/en/2.3.x/
-[Python-badge]: https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54
-[Python-url]: https://www.python.org/
-[Spyder-badge]: https://img.shields.io/badge/Spyder-838485?style=for-the-badge&logo=spyder%20ide&logoColor=maroon
-[Spyder-url]: https://www.spyder-ide.org/
-[HTML5-badge]: https://img.shields.io/badge/html5-%23E34F26.svg?style=for-the-badge&logo=html5&logoColor=white
-[CSS3-badge]: https://img.shields.io/badge/css3-%231572B6.svg?style=for-the-badge&logo=css3&logoColor=white
+--- 
+title: Hate Speech Detector 
+sdk: gradio  
+sdk_version: "4.0.0"  
+app_file: app.py  
+pinned: false  
+---