Update README.md

README.md

@@ -10,4 +10,67 @@ pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Sentiment Analyzer
+
+## Overview
+Welcome to **Sentiment Analyzer**, a text-to-text AI project developed as Part 1 of the AI Integration Project using Hugging Face pipelines and Gradio. This application takes user input in either Arabic or English, analyzes the sentiment expressed in the text, and returns a sentiment label (e.g., "1 star" to "5 stars") along with a confidence score. The goal is to demonstrate a functional, bilingual sentiment analysis pipeline integrated with an interactive Gradio interface, deployed on Hugging Face Spaces.
+
+## Project Objectives
+- Build a text-to-text pipeline using a Hugging Face model for sentiment analysis.
+- Integrate the pipeline with a user-friendly Gradio interface.
+- Deploy the solution on Hugging Face Spaces for accessibility.
+- Ensure support for both Arabic and English inputs, showcasing bilingual capabilities.
+
+## Implemented Pipeline
+- **Task**: Sentiment Analysis (Text-to-Text)
+- **Model**: `nlptown/bert-base-multilingual-uncased-sentiment`
+- **Framework**: Hugging Face Transformers
+- **Interface**: Gradio
+- **Input**: User-provided text (comments or reviews) in Arabic or English.
+- **Output**: Sentiment label (e.g., "1 star", "2 stars", etc.) and a confidence score (0 to 1).
+
+The pipeline processes text input, leverages a pretrained multilingual model to classify sentiment, and displays the results in an intuitive format via Gradio.
+
+## Instructions for Using the Interface
+1. **Enter Your Comment**: Type your text (e.g., a review or opinion) into the text box.
+2. **Select Language**: Choose "Arabic" or "English" from the radio buttons to indicate your input language.
+3. **Click Analyze**: Press the "Analyze" button to process your text.
+4. **View Results**: The sentiment label (e.g., "4 stars") appears in the "Sentiment" box, and the confidence score (between 0 and 1) is shown on the slider.
+
+### Example Inputs
+- English: "The product is amazing!" → "5 stars" (Positive)
+- Arabic: "الخدمة سيئة جداً" → "1 star" (Negative)
+- Try the built-in examples below the interface for a quick demo!
+
+## Model and Pipeline Choices
+- **Model Selection**: The `nlptown/bert-base-multilingual-uncased-sentiment` model was chosen because:
+  - It’s pretrained on a large, diverse dataset, enabling accurate sentiment classification.
+  - It supports multiple languages, including Arabic and English, aligning with the bilingual requirement.
+  - It outputs sentiment as star ratings (1 to 5), which are user-friendly and widely understood.
+- **Pipeline Justification**: The Hugging Face `pipeline` API simplifies integration, providing a robust, out-of-the-box solution for sentiment analysis. Paired with Gradio, it ensures an interactive experience without complex custom coding.
+- **Performance**: The model runs efficiently, with GPU support enabled if available (via PyTorch), ensuring quick responses for users.
+
+## Bilingual Implementation
+The project fully supports both Arabic and English:
+- **Model Capability**: The underlying BERT-based model is multilingual and pretrained on texts including Arabic and English, allowing it to process both languages natively without additional translation steps.
+- **Interface Design**: The Gradio interface uses the "Tajawal" font for Arabic readability alongside Arial for English, ensuring a seamless experience for bilingual users.
+- **Testing**: The pipeline has been tested with Arabic and English inputs (see examples above), confirming consistent performance across both languages.
+
+No justification for excluding bilingual support is needed, as it’s fully implemented.
+
+## Deployment
+- **Platform**: Hosted on Hugging Face Spaces.
+- **Setup**: The project uses a Gradio app (`app.py`) with dependencies listed in `requirements.txt` (`gradio`, `transformers`, `torch`).
+- **Access**: Publicly available via this Space’s URL—feel free to try it out!
+
+## How to Run Locally (Optional)
+If you’d like to run this project locally:
+1. Clone the repository from this Space.
+2. Install dependencies: `pip install -r requirements.txt`.
+3. Run the app: `python app.py`.
+4. Open the displayed local URL in your browser.
+
+## Contact
+For questions or feedback, reach out to `Tuwaigcoursega@gmail.com`.
+
+Thank you for exploring Sentiment Analyzer—we hope you find it insightful and intuitive!