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https://github.com/faisal-fida/tire-quality-prediction-webapp
This project is a web application that predicts tire quality using a machine learning model. It leverages Streamlit for the web interface and TensorFlow for the model predictions.
https://github.com/faisal-fida/tire-quality-prediction-webapp
machine-learning streamlit tensorflow webapp
Last synced: about 1 month ago
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This project is a web application that predicts tire quality using a machine learning model. It leverages Streamlit for the web interface and TensorFlow for the model predictions.
- Host: GitHub
- URL: https://github.com/faisal-fida/tire-quality-prediction-webapp
- Owner: faisal-fida
- Created: 2023-12-06T18:15:56.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-06T08:20:33.000Z (5 months ago)
- Last Synced: 2024-11-10T21:16:27.833Z (3 months ago)
- Topics: machine-learning, streamlit, tensorflow, webapp
- Language: Python
- Homepage:
- Size: 4.88 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Tire Quality Prediction WebApp
This project is a web application that predicts tire quality using a machine learning model. It leverages Streamlit for the web interface and TensorFlow for the model predictions.
## Features
- **User-Friendly Interface**: Allows users to upload an image of a tire to get a quality prediction.
- **Model Integration**: Downloads and loads a pre-trained TensorFlow model.
- **Real-Time Predictions**: Provides instant feedback on the quality of the uploaded tire image.
## File Structure
- `streamlit_app.py`: Main application file using Streamlit to set up the web interface.
- `download_model.py`: Script to download and extract the pre-trained model.
## Key Components
### Streamlit Application (`streamlit_app.py`)
- **Model Loading**:
- Downloads the model if not already present.
- Loads the model into the session state for reuse.
- **Image Preprocessing**:
- Converts uploaded images to a format suitable for the model.
- Resizes and normalizes the image.
- **Prediction**:
- Uses the model to predict tire quality.
- Displays the prediction result on the web interface.
### Model Download (`download_model.py`)
- **Download and Extraction**:
- Downloads the model zip file from a provided URL.
- Extracts the model files for use in the application.
## Challenges and Solutions
- **Model Integration**: Ensuring the model is properly downloaded and loaded into the application required handling file downloads and TensorFlow model loading.
- **Image Preprocessing**: Converting user-uploaded images to a format compatible with the model involved resizing and normalizing the images.
- **Real-Time Predictions**: Providing instant feedback required efficient processing and prediction mechanisms.
## Getting Started
1. Clone the repository.
2. Install the required dependencies.
3. Run the Streamlit app.
```bash
git clone https://github.com/faisal-fida/tyre-quality-prediction.git
cd tyre-quality-prediction
pip install -r requirements.txt
streamlit run streamlit_app.py
```
## Conclusion
This project demonstrates a practical application of integrating machine learning models into a web application, showcasing the use of Streamlit and TensorFlow to predict tire quality from images. The project highlights the complexities of model integration, image preprocessing, and real-time predictions.
Feel free to contribute or raise issues if you encounter any problems!