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The project includes a trained model, a FastAPI backend, and a Streamlit frontend—all deployed and accessible online.\n\n![screenshot](./screenshot1.png)\n![screenshot](./screenshot2.png)\n\n## ✨ Features\n\n- **Machine Learning Model**: MLP classifier trained on the classic Iris dataset\n- **RESTful API**: FastAPI backend with comprehensive endpoints\n- **Web Interface**: Interactive Streamlit UI for real-time predictions\n- **Cloud Deployment**: Fully deployed on modern platforms:\n  - Model hosted on Hugging Face\n  - API deployed on Render\n  - UI deployed on Streamlit Cloud\n\n## 🚀 Live Demos\n\n- **Web Interface**: [Streamlit App](https://iris-flowers-project.streamlit.app/)\n- **Model Repository**: [Hugging Face Model](https://huggingface.co/jospin6/iris-classification)\n\n## 🛠️ Tech Stack\n\n- **Machine Learning**: PyTorch, Scikit-learn, Pandas, NumPy\n- **Backend**: FastAPI, Uvicorn, Pydantic\n- **Frontend**: Streamlit\n- **Deployment**: Hugging Face Hub, Render, Streamlit Cloud\n- **Environment Management**: Pipenv\n\n## 📦 Getting Started\n\n### Prerequisites\n\n- Python 3.8 or higher\n- Pipenv (recommended) or pip\n\n### Installation\n\n1. **Clone the repository**\n   ```bash\n   git clone https://github.com/Jospin6/iris-flowers.git\n   cd iris-flowers\n\n2. **Install dependencies with Pipenv**\n    ```bash\n    pip install -r requirements.txt\n\n### Running Locally\n\n1. **Start the API server**\n    ```bash\n    cd api\n    uvicorn main:app --reload --host 0.0.0.0 --port 8000\n\n2. **Start the Streamlit app**\n    ```bash\n    cd frontend\n    streamlit run app.py\n\n\n### 👨‍💻 Author\nBuilt with ❤️ by Jospin Ndagano","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjospin6%2Firis-flowers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjospin6%2Firis-flowers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjospin6%2Firis-flowers/lists"}