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94a098d fdfb1fb 94a098d fdfb1fb 94a098d fdfb1fb 94a098d fdfb1fb 94a098d fdfb1fb 94a098d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import gradio as gr
from ultralytics import YOLO
from PIL import Image, ImageOps, ImageEnhance
import numpy as np
import tempfile
# Load your model
model = YOLO("model/best.pt")
def preprocess(image):
"""Safe preprocessing for PIL or numpy input."""
if isinstance(image, np.ndarray):
image = Image.fromarray(image)
image = ImageOps.exif_transpose(image).convert("RGB")
# Optional resize for performance
w, h = image.size
max_dim = max(w, h)
if max_dim > 1024:
scale = 1024 / max_dim
image = image.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
# Light contrast enhancement
image = ImageEnhance.Contrast(image).enhance(1.05)
return image
def detect(image, conf=0.4, iou=0.5):
"""Run YOLO detection on a single model Space."""
image = preprocess(image)
results = model.predict(image, conf=conf, iou=iou)
boxes = results[0].boxes
# Convert YOLO output to numpy RGB
output = results[0].plot()[:, :, ::-1] # BGR → RGB
if len(boxes) > 0:
diagnosis = "⚠️ Swelling detected."
else:
diagnosis = "🟢 No swelling detected."
return [output, diagnosis]
# Gradio Interface
interface = gr.Interface(
fn=detect,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Slider(0, 1, value=0.5, step=0.05, label="Confidence Threshold"),
gr.Slider(0, 1, value=0.5, step=0.05, label="NMS IoU Threshold"),
],
outputs=[
gr.Image(label="Swelling Detection Result"),
gr.Textbox(label="Diagnosis")
],
title="Swelling Detection"
)
interface.launch()
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