app.py

import os
import platform
import pathlib
import gradio as gr
from PIL import Image
from ultralytics import YOLO

# Fix for Windows paths on Linux/Mac environments
plt_sys = platform.system()
if plt_sys != 'Windows':
    pathlib.WindowsPath = pathlib.PosixPath

# Load Models (V7 t/m V10 toegevoegd)
MODELS = {
    "CalvingCatcherV10": YOLO("calvingcatcherV10.pt"),
    "CalvingCatcherV9": YOLO("calvingcatcherV9.pt"),
    "CalvingCatcherV8": YOLO("calvingcatcherV8.pt"),
    "CalvingCatcherV7": YOLO("calvingcatcherV7.pt"),
    "CalvingCatcherV6": YOLO("calvingcatcherV6.pt"),
    "CalvingCatcherV5": YOLO("calvingcatcherV5.pt"),
    "CalvingCatcherV4": YOLO("calvingcatcherV4.pt"),
    "CalvingCatcherV3": YOLO("calvingcatcherV3.pt"),
    "CalvingCatcherV2": YOLO("calvingcatcherV2.pt")
}

# --- Setup Example Images ---
example_folder = "examples"
example_images = [
    os.path.join(example_folder, f)
    for f in os.listdir(example_folder)
    if f.lower().endswith(('.png', '.jpg', '.jpeg'))
] if os.path.exists(example_folder) else []
# ----------------------------

CUSTOM_CSS = """
@import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap');
@import url('https://fonts.googleapis.com/css2?family=Bebas+Neue&display=swap');

.bebas-font { 
    font-family: 'Bebas Neue', sans-serif !important; 
    text-transform: uppercase; 
    letter-spacing: 1px; 
}
.roboto-font { 
    font-family: 'Roboto', sans-serif !important; 
}
.primary-btn { 
    background-color: #386938 !important; 
    border: none !important; 
    color: white !important; 
    font-family: 'Bebas Neue', sans-serif !important;
    font-size: 1.2rem !important;
}
"""

DESCRIPTION_TEXT = """
**CalvingCatcher AI** is an informative open-source computer vision tool designed to monitor calving processes 24/7. 
It analyzes footage to detect critical signs and stages of birth in cattle.
"""

def predict(img, model_name, conf_threshold, selected_class_names):
    if img is None:
        return None
    
    selected_model = MODELS[model_name]
    model_classes = selected_model.names
    
    selected_indices = [
        idx for idx, name in model_classes.items() 
        if name in selected_class_names
    ]

    if not selected_indices:
        return img 

    results = selected_model(img, conf=conf_threshold, classes=selected_indices)
    res_plotted = results[0].plot()
    return Image.fromarray(res_plotted[:, :, ::-1])

def update_checkboxes(model_name):
    model = MODELS[model_name]
    names = list(model.names.values())
    return gr.update(choices=names, value=names)

# Build Gradio Interface
with gr.Blocks(css=CUSTOM_CSS, theme=gr.themes.Soft(primary_hue="green"), title="CalvingCatcher AI") as demo:
    
    with gr.Column(elem_classes="roboto-font"):
        gr.Markdown("# 🐮 CalvingCatcher AI", elem_classes="bebas-font")
        gr.Markdown(DESCRIPTION_TEXT)

        with gr.Row():
            with gr.Column():
                input_img = gr.Image(type="pil", label="Upload Barn Image")
                
                with gr.Row():
                    model_drop = gr.Dropdown(
                        choices=list(MODELS.keys()), 
                        value="CalvingCatcherV10",  # Aangepast naar V10
                        label="Select Model"
                    )
                    conf_slider = gr.Slider(
                        minimum=0.1, 
                        maximum=1.0, 
                        value=0.3, 
                        label="Confidence Threshold",
                        info="Higher values = more certainty, fewer detections."
                    )
                
                # initial_model aangepast naar V10 zodat de checkboxes direct kloppen
                initial_model = "CalvingCatcherV10"
                initial_choices = list(MODELS[initial_model].names.values())
                
                class_selector = gr.CheckboxGroup(
                    choices=initial_choices, 
                    value=initial_choices, 
                    label="Filter Detection Classes",
                    info="Uncheck classes you want to ignore."
                )
                
                predict_btn = gr.Button("Run Detection", variant="primary", elem_classes="primary-btn")

            with gr.Column():
                output_img = gr.Image(type="pil", label="Detection Result")

        # --- Display Examples if they exist ---
        if example_images:
            gr.Markdown("### 📸 Try an Example:", elem_classes="bebas-font")
            gr.Examples(
                examples=example_images,
                inputs=input_img,
                label=None
            )

    # Event Listeners
    model_drop.change(
        fn=update_checkboxes,
        inputs=model_drop,
        outputs=class_selector
    )

    predict_btn.click(
        fn=predict,
        inputs=[input_img, model_drop, conf_slider, class_selector],
        outputs=output_img
    )

if __name__ == "__main__":
    demo.launch()