app.py

import gradio as gr
from ultralytics import YOLO
from PIL import Image
from ultralytics import YOLO
from PIL import Image
from ultralytics.utils.plotting import Annotator,colors
import glob 
model = YOLO('Dental_model.pt')  # Replace 'yolov8n.pt' with your model file if using a custom one
pic_files=glob.glob('*.jpg')
names=model.model.names
def detect_objects(image):
    image1=image.copy()
    results = model.predict(image)
    whole=results[0].plot()
    classes=results[0].boxes.cls.cpu().tolist()
    boxes=results[0].boxes.xyxy.cpu()
    annotator = Annotator(image, line_width=3)
    annotator1=Annotator(image1, line_width=3)
    for box,cls in zip(boxes,classes):
        annotator.box_label(box, label=names[int(cls)], color=colors(int(cls)))
        annotator1.box_label(box, label=None, color=colors(int(cls)))
    return Image.fromarray(annotator.result()),Image.fromarray(annotator1.result())

# Gradio Interface
title = "YOLOv8 Object Detection"
description = "Upload an image to detect objects using a YOLOv8 model."

gradio_app =gr.Interface(fn=detect_objects, 
                     inputs=gr.Image(type="pil"), 
                     outputs=[gr.Image(type='pil', label="Dental Analysis"),
                              gr.Image(type='pil', label="Dental Analysis")],
                        examples=pic_files)

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