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| title: PlantInfo | |
| emoji: 🦀 | |
| colorFrom: green | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.17.1 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # 🌿 AI Plant Guide - English & Arabic | |
| ## Overview | |
| AI Plant Guide is a user-friendly Gradio-based application that provides detailed plant information in both English and Arabic. By entering a plant name and selecting a language, users can access insights on scientific names, growing conditions, common uses, and care tips. | |
| ## Project Objectives | |
| - Provide an AI-powered plant guide with bilingual support. | |
| - Ensure user-friendly interaction through a web-based interface. | |
| - Leverage state-of-the-art NLP models for accurate and informative responses. | |
| - Offer quick and efficient responses using GPU acceleration. | |
| ## Implemented Pipelines | |
| 1. **User Input Handling**: | |
| - The user enters a plant name and selects a language. | |
| 2. **Model Selection**: | |
| - Based on the chosen language, the appropriate transformer model is loaded: | |
| - **Microsoft Phi-3-mini-4k-instruct** for English | |
| - **ALLaM-7B-Instruct-preview** for Arabic | |
| 3. **Text Generation Pipeline**: | |
| - The selected model generates plant details, including: | |
| - Scientific name | |
| - Growing conditions | |
| - Common uses | |
| - Care tips | |
| 4. **Output Display**: | |
| - The generated information is displayed in a user-friendly interface. | |
| ## Instructions for Using the Interface | |
| 1. Open the **Gradio UI** in your browser. | |
| 2. Select a language (**English/Arabic**). | |
| 3. Enter a plant name (e.g., **Lavender**, **Aloe Vera**). | |
| 4. Click **"🔍 Get Plant Info"** to generate information. | |
| 5. View the plant details in the output box. | |
| ## Justification for Model and Pipeline Choices | |
| - **Hugging Face Transformers**: Offers pre-trained state-of-the-art language models optimized for text generation. | |
| - **Gradio**: Provides an easy-to-use web interface without the need for extensive frontend development. | |
| - **PyTorch**: Ensures efficient model inference and flexibility for future enhancements. | |
| - **GPU Acceleration**: Speeds up response times, improving user experience. | |
| - **Pipeline-Based Design**: Ensures modularity and scalability for future improvements. | |
| ## Bilingual Implementation | |
| - The project supports **English and Arabic** via two specialized transformer models. | |
| - The UI allows users to choose their preferred language. | |
| - The prompt structure is adapted for each language to ensure high-quality responses. | |
| ## Technologies Used | |
| - **Gradio** for UI development | |
| - **Transformers (Hugging Face)** for text generation | |
| - **PyTorch** for model inference | |
| - **Microsoft Phi-3-mini-4k-instruct** (English) and **ALLaM-7B-Instruct-preview** (Arabic) models | |
| - **Spaces GPU Acceleration** | |
| ## Installation | |
| ### Prerequisites | |
| Ensure you have Python installed (>=3.8) and required dependencies. | |
| ### Steps | |
| 1. **Clone the repository**: | |
| ```bash | |
| git clone https://huggingface.co/spaces/NoufSaleh46/PlantInfo1 | |
| cd PlantInfo1 | |
| ``` | |
| 2. **Create a virtual environment (optional but recommended)**: | |
| ```bash | |
| python -m venv venv | |
| source venv/bin/activate # On Windows use `venv\Scripts\activate` | |
| ``` | |
| 3. **Install dependencies**: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 4. **Run the application**: | |
| ```bash | |
| python app.py | |
| ``` | |
| ## Deployment | |
| This project is deployed on **Hugging Face Spaces**. You can access it directly [here](https://huggingface.co/spaces/NoufSaleh46/PlantInfo1). | |