import gradio as gr
from inference import combined_inference_live
from model_loader import EXTENDED_MODELS

# Gradio Web Interface
with gr.Blocks() as app:
    gr.Markdown("# Live Object Detection: Full vs. Slicing-Based Detection")

    gr.Markdown("""
    **How It Works**:
    - **Full Inference**: Runs a single pass over the entire frame.
    - **Slicing Detection**: Divides the frame into patches, detects objects, applies Non-Maximum Suppression (NMS).
    - **Outputs retain the original BGR format for color accuracy**.
    """)

    with gr.Row():
        webcam = gr.Image(sources="webcam", streaming=True, label="Webcam Feed")
        model_dd = gr.Dropdown(label="Select Model", choices=list(EXTENDED_MODELS.keys()), value="YOLOv8")

    with gr.Row():
        conf_slider = gr.Slider(0, 1.0, value=0.25, step=0.05, label="Confidence Threshold")
        slice_slider = gr.Slider(128, 1024, value=512, step=1, label="Slice Size")
        overlap_slider = gr.Slider(0.0, 0.5, value=0.2, step=0.05, label="Overlap Rate")

    fi_output = gr.Image(label="Full Inference")
    slice_output = gr.Image(label="Slicing + NMS")

    webcam.stream(
        fn=combined_inference_live,
        inputs=[webcam, model_dd, conf_slider, slice_slider, overlap_slider],
        outputs=[fi_output, slice_output]
    )

# Run the Gradio app
app.launch()