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import gradio as gr
from ultralytics import YOLO
from PIL import Image, ImageDraw
import tempfile
import os
# Load your trained model
model = YOLO("best.pt") # Replace with your trained model .pt path
# Detection function with confidence filter
def detect_disease(image):
# Save input to a temporary file
temp = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
image.save(temp.name)
# Run YOLOv8 inference
results = model(temp.name)[0] # Get first result from list
# Filter predictions with confidence >= 0.5
filtered_boxes = []
for box in results.boxes.data:
x1, y1, x2, y2, score, cls_id = box.tolist()
if score >= 0.5:
filtered_boxes.append((x1, y1, x2, y2, score, int(cls_id)))
# Draw boxes on image
draw = ImageDraw.Draw(image)
class_names = model.names
for x1, y1, x2, y2, score, cls_id in filtered_boxes:
draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
draw.text((x1, y1 - 10), f"{class_names[cls_id]}: {score:.2f}", fill="red")
return image
# Gradio Interface
interface = gr.Interface(
fn=detect_disease,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="pil"),
title="🌿 Plant Disease Detector (YOLOv8)",
description="Upload a leaf image. This model will detect plant diseases using YOLOv8. Only results with confidence ≥ 50% are shown."
)
# Launch app
interface.launch()
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