app.py

import PIL.Image as Image
import gradio as gr
from ultralytics import ASSETS, YOLO

model = YOLO("best.pt")

def predict_image(img, conf_threshold, iou_threshold):
    results = model.predict(
        source=img,
        conf=conf_threshold,
        iou=iou_threshold,
        show_labels=True,
        show_conf=True,
        imgsz=640,
    )

    # Assuming 'results' has a property or method to get detected labels count
    # If not directly available, you might need to parse the results accordingly
    fruits_count = sum(1 for _ in results if _.label == "fruit")  # Example, adjust based on actual results structure

    # Only handling the last result for simplicity, adjust according to your needs
    for r in results:
        im_array = r.plot()
        im = Image.fromarray(im_array[..., ::-1])
    
    caption = f"Detected fruits: {fruits_count}"  # Modify this line according to the actual object you're detecting
    return im, caption

iface = gr.Interface(
    fn=predict_image,
    inputs=[
        gr.Image(type="pil", label="Upload Image"),
        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold")
    ],
    outputs=[gr.Image(type="pil", label="Result"), gr.Textbox(label="Caption")],
    title="My Yield | 🌱",
    description="Estimate the amount of plants per year",
)

if __name__ == '__main__':
    iface.launch()