Update README.md

README.md

@@ -11,4 +11,81 @@ license: apache-2.0
 short_description: 'Fainal Project , Part 2 '
 ---
 
+# AI Study Summary
+
+This project provides a simple and interactive tool for text summarization in Arabic and English using the mT5 multilingual model. The tool leverages the Hugging Face `transformers` library and Gradio to create an easy-to-use web interface. The goal is to allow users to input a piece of text in either Arabic or English and receive a concise summary of it.
+
+## Project Objectives
+
+The objective of this project is to demonstrate how text summarization can be achieved in both Arabic and English using a single multilingual model. It aims to:
+- Provide an easy way for users to summarize text in Arabic and English.
+- Allow users to interact with the model via a Gradio interface.
+- Showcase the capabilities of the mT5 multilingual model for text summarization.
+
+## Implemented Pipelines
+
+The main pipeline of this application is as follows:
+
+1. **Text Input**: The user inputs a piece of text into a textbox and selects the language of the text (either Arabic or English) using a dropdown menu.
+   
+2. **Text Tokenization**: The input text is tokenized using a pre-trained tokenizer from the Hugging Face model `csebuetnlp/mT5_multilingual_XLSum`.
+   
+3. **Text Summarization**: The tokenized text is passed through the mT5 model, which generates a summary using beam search and length penalties to ensure concise and accurate summarization.
+
+4. **Output**: The model outputs a summarized version of the input text, which is displayed to the user on the interface.
+
+## How to Use the Interface
+1. Go to the [AI Study Summary Space](https://huggingface.co/spaces/your-username/ai-study-summary).
+2. In the textbox, enter the text you would like to summarize.
+3. Choose the language of the text from the dropdown menu (Arabic or English).
+4. Click "Submit" to generate the summary.
+5. Optionally, you can try pre-defined example texts by selecting them from the examples section.
+
+## Model and Pipeline Choices
+
+- **Model Choice**: 
+    The model used in this application is `csebuetnlp/mT5_multilingual_XLSum`. It is based on the mT5 (multilingual T5) architecture and is specifically fine-tuned for text summarization tasks in multiple languages, including both Arabic and English. The mT5 model is well-suited for summarization tasks as it is designed for multilingual and cross-lingual understanding, making it a perfect fit for this project.
+
+- **Pipeline Design**: 
+    The pipeline was chosen to ensure efficient and effective summarization while keeping the process straightforward and easy for users. The use of beam search ensures that the model generates high-quality summaries, while the length penalties ensure that the summaries are concise and not overly verbose.
+
+## Bilingual Implementation
+
+This application is designed to handle both Arabic and English text inputs. The mT5 model supports multiple languages, including Arabic and English, allowing for bilingual text summarization. The following points explain how the bilingual implementation is addressed:
+- The model itself, mT5, is pre-trained and fine-tuned on a multilingual corpus, allowing it to handle both Arabic and English with high accuracy.
+- The user is prompted to select the language of the input text (either Arabic or English) before generating the summary, ensuring that the correct language model is applied to the input text.
+
+If the application were to be extended to more languages in the future, the process would remain the same—only the language selection and model fine-tuning would need to be adjusted.
+
+## Requirements
+
+To run this project locally, you need Python 3.7 or higher. You also need to install the following Python libraries:
+
+
+`pip install gradio`
+`pip install transformers`
+`pip install torch`
+
+
+---
+
+- Gradio: Provides a simple interface for building and sharing machine learning demos.
+- Transformers: Hugging Face's library for pre-trained models, in this case, for multilingual text summarization.
+- Torch: PyTorch is used as the backend for the model.
+
+## Example Usage
+Input:
+- Text: "Artificial intelligence is a branch of computer science that aims to create intelligent machines that work and react like humans."
+- Language: English
+
+Output:
+- Summary: "AI is a branch of computer science aimed at creating intelligent machines that mimic human behavior."
+
+## License
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## Acknowledgements
+The summarization model used in this application is based on the mT5 Multilingual XLSum model by Hugging Face and is fine-tuned on the XLSum dataset for summarization in multiple languages.
+Gradio is used to build the web interface, making it easy to interact with the model.
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference