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import gradio as gr
from ultralytics import YOLO
# Load the YOLO model
model = YOLO('best.pt')
def predict(img, confidence_threshold):
# Perform inference
results = model(img)
# Filter predictions based on the confidence threshold
# The results[0].boxes.data contains the detection results, including confidence scores
filtered_boxes = [box for box in results[0].boxes.data if box[4] >= confidence_threshold]
# Plot the results (with the filtered detections)
annotated_frame = results[0].plot(labels=filtered_boxes)
return annotated_frame
# Create the Gradio interface
iface = gr.Interface(
fn=predict,
inputs=[
gr.Image(label="Input Image", type="filepath"),
gr.Slider(minimum=0, maximum=1, value=0.5, label="Confidence Threshold", step=0.01)
],
outputs="image",
title="Coin Detector",
description="Upload an image to detect coins. Adjust the confidence threshold to filter results."
)
# Launch the Gradio interface
iface.launch(share=True)
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