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import gradio as gr
import torch
import cv2
import numpy as np
from PIL import Image
from ultralytics import YOLO
# Load YOLOv11 Model
model_path = "best.pt"
model = YOLO(model_path)
def predict(image):
image = np.array(image)
results = model(image, conf=0.85)
detected_classes = set() # Track unique detected classes
labels = []
# Draw bounding boxes and extract labels
for result in results:
for box in result.boxes:
x1, y1, x2, y2 = map(int, box.xyxy[0])
conf = box.conf[0]
cls = int(box.cls[0])
class_name = model.names[cls]
detected_classes.add(class_name) # Store detected class
label = f"{class_name} {conf:.2f}"
labels.append(label)
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# Define possible classes (adjust based on your dataset)
possible_classes = {"front", "back"}
# Identify missing class if any
missing_classes = possible_classes - detected_classes
if missing_classes:
labels.append(f"Missing: {', '.join(missing_classes)}")
return Image.fromarray(image), labels
# Gradio Interface
iface = gr.Interface(
fn=predict,
inputs="image",
outputs=["image", "text"], # Returning both image and detected labels
title="YOLOv11 Object Detection (Front & Back Card)"
)
iface.launch()
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