This project integrates a FastAPI backend with a Streamlit frontend to create a web-based application for uploading images, processing them with the YOLOv8 model for object detection, and displaying the results. The application supports image uploads in Base64 encoding, processes them using a pretrained YOLOv8 model, and returns processed image paths for display.

• Image Upload: Users can upload images via a Streamlit interface.
• Object Detection: Uses the YOLOv8 model to detect objects in uploaded images.
• Result Visualization: Processed images with detected objects are displayed.

• FastAPI: Backend server.
• Streamlit: Frontend interface.
• YOLOv8 (Ultralytics): Object detection.
• Pydantic: Data validation.
• Python: Programming language.

1. Clone the Repository

   git clone https://github.com/t56gary/YOLOv8_Web_App
   cd YOLOv8_Web_App

2. Install Required Libraries

   pip install fastapi uvicorn streamlit ultralytics[aio] pydantic aiofiles
   

3. Environment Setup
   • Ensure Python 3.8+ is installed.
   • CUDA environment (if running YOLOv8 with GPU support).

1. Start the Backend Server

   uvicorn backend:app --reload  # Replace 'backend' with your FastAPI script name if different
   

2. Run the Streamlit Frontend

   streamlit run frontend.py  # Replace 'frontend.py' with your Streamlit script name
   

3. Interacting with the Application
   • Navigate to the Streamlit URL (usually http://localhost:8501).
   • Use the file uploader to select and upload an image.
   • View the processed image and detection results displayed on the page.
   • The output of the YOLOv8 detection will be saved as a JSON file in the same folder where the backend is running.