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import torch
import ultralytics
from ultralytics import YOLO
from ultralytics.nn.tasks import DetectionModel
from ultralytics.nn.modules.conv import Conv
import torch.nn as nn
import cv2
import gradio as gr
# ---- FIX for PyTorch 2.6+ ----
torch.serialization.add_safe_globals([DetectionModel, nn.Sequential, Conv])
# ---- Load trained YOLO model ----
model = YOLO("res.pt") # Ensure your model file is in the same folder
# ---- Prediction function ----
def predict(image):
# Run inference
results = model.predict(source=image, conf=0.25)
# Draw boxes on the image
result_image = results[0].plot()
# Convert BGR → RGB for Gradio
return cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
# ---- Gradio Interface ----
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="filepath", label="Upload Bone X-ray"),
outputs=gr.Image(type="numpy", label="Detection Result"),
title="Human Bone Fracture Detection",
description=(
"Upload an X-ray image to detect human bone fractures using YOLOv8.<br><br>"
"📸 **Dataset Source:** "
"<a href='https://www.kaggle.com/datasets/jockeroika/human-bone-fractures-image-dataset' target='_blank'>"
"Human Bone Fractures Image Dataset</a>"
)
)
if __name__ == '__main__':
iface.launch()
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