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from ultralytics import YOLO
import gradio as gr
from PIL import Image
# 1. Load YOLOv8 model
model = YOLO("best_rockfall_model.pt")
# 2. Inference function (image + JSON)
def predict(image):
results = model.predict(image)
result = results[0]
# --- Prepare annotated image ---
annotated = result.plot() # numpy array (BGR)
annotated_img = Image.fromarray(annotated[..., ::-1]) # convert BGR → RGB
# --- Prepare JSON detections ---
detections = []
for box in result.boxes:
cls_id = int(box.cls[0])
label = result.names[cls_id]
conf = float(box.conf[0])
xyxy = box.xyxy[0].tolist() # [x1, y1, x2, y2]
detections.append({
"label": label,
"confidence": conf,
"bbox": xyxy
})
return annotated_img, detections
# 3. Gradio Interface
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=[
gr.Image(type="pil", label="Annotated Image"),
gr.JSON(label="Detections")
],
title="Rockfall Detection API (YOLOv8)",
description="Upload an image to detect rockfalls. The model returns both the annotated image and raw detections (labels, confidence, bounding boxes)."
)
if __name__ == "__main__":
demo.launch()
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