https://github.com/mohiteamit/simple_pipeline_using_iris
Iris Classification Pipeline, Streamlit App & FastAPI
https://github.com/mohiteamit/simple_pipeline_using_iris
fastapi iris-classification iris-dataset pipeline python sklean-classifier stremalit
Last synced: 7 months ago
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Iris Classification Pipeline, Streamlit App & FastAPI
- Host: GitHub
- URL: https://github.com/mohiteamit/simple_pipeline_using_iris
- Owner: mohiteamit
- License: mit
- Created: 2025-03-08T07:43:20.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-08T09:04:30.000Z (7 months ago)
- Last Synced: 2025-03-08T09:18:18.719Z (7 months ago)
- Topics: fastapi, iris-classification, iris-dataset, pipeline, python, sklean-classifier, stremalit
- Language: Python
- Homepage:
- Size: 32.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Iris Classification Pipeline, Streamlit App & FastAPI
This project demonstrates an end-to-end machine learning pipeline using the Iris dataset. It includes:
- A training script that builds an automated ML pipeline (with preprocessing, model training, evaluation, and saving).
- A Streamlit web application for model inference via a graphical interface.
- A FastAPI application to serve model predictions via REST API endpoints.
- A batch file that reads a `.env` file to initialize your virtual environment.
## Project Structure
```
├── app.py # Streamlit app for model inference
├── api.py # FastAPI app for serving predictions via REST API
├── train_model.py # Script to build, train, and save the ML pipeline
├── iris_pipeline.pkl # Saved model pipeline (generated by train_model.py)
├── .env # Environment variable file containing VENV path (to be created)
├── init_venv.bat # Batch file to initialize the virtual environment
├── requirements.txt # Python dependencies
└── README.md # This file
```
## Prerequisites
- **Python 3.6+**
- **pip** (Python package installer)
- **Windows OS** (for the provided batch file)
## Setup & Operation Order
### 1. Clone the Repository
Clone this repository and navigate into its directory:
```bash
git clone https://github.com/mohiteamit/simple_pipeline_using_iris.git
cd simple_pipeline_using_iris
```
### 2. Create the `.env` File
Create a file named `.env` in the project root with the following content.
Make sure to replace `` with the actual path to your virtual environment's activation script:
```dotenv
VENV=\Scripts\Activate
```
### 3. Create/Activate the Virtual Environment
You have two options:
#### Option A: Using the Provided Batch File
1. With the `.env` file in place, run the batch file to initialize your virtual environment:
```batch
init_venv.bat
```
#### Option B: Manually Activating Your Virtual Environment
If you prefer manual activation, run:
```batch
\Scripts\Activate
```
### 4. Install Dependencies
Once your virtual environment is active, install the required packages using `requirements.txt`:
```bash
pip install -r requirements.txt
```
### 5. Train the Model
Run the training script to load the Iris dataset, preprocess the data, train a RandomForest classifier (within a pipeline), evaluate its performance, and save the pipeline to disk:
```bash
python train_model.py
```
After running the script, a file named `iris_pipeline.pkl` will be generated.
### 6. Run the Streamlit App
Launch the Streamlit app to interact with the model via a graphical web interface:
```bash
streamlit run app.py
```
In your web browser, you can:
- Input iris feature values (sepal length, sepal width, petal length, petal width)
- Click the **Predict** button to view the predicted iris species
### 7. Run the FastAPI App
To serve predictions via a REST API, launch the FastAPI server with:
```bash
uvicorn api:app --reload
```
By default, the server will start at `http://127.0.0.1:8000`. You can access the interactive API documentation at `http://127.0.0.1:8000/docs` to test the `/predict` endpoint.
## Additional Notes
- **Automated Retraining:**
You may schedule the training script using tools like cron jobs, Windows Task Scheduler, or advanced scheduling tools such as Airflow if needed.
- **Modularity:**
The training and inference stages are separated. You can update the model by re-running `train_model.py` without affecting the Streamlit or FastAPI interfaces.
- **Customization:**
Feel free to extend the pipeline (e.g., add more preprocessing steps) or enhance the Streamlit app and FastAPI endpoints (e.g., add logging, error handling, or additional visualizations).
## App screenshot
## Contributing
Contributions and improvements are welcome. Please open an issue or submit a pull request for any suggestions or enhancements.
## License
This project is licensed under the MIT License.
## Contact
For questions or further information, please reach out to [mohite.amit@gmail.com].