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# STEP 1: Install dependencies
# Note: Use requirements.txt when deploying
import torch
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from PIL import Image, ImageDraw, ImageFont
import gradio as gr
# STEP 2: Load YOLOS model & processor
model_name = "hustvl/yolos-base"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForObjectDetection.from_pretrained(model_name)
model.eval()
if torch.cuda.is_available():
model.to(torch.float16).to("cuda")
# STEP 3: Detection function with object name return
def detect_yolos(image, threshold=0.5):
image = image.convert("RGB")
inputs = processor(images=image, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model(**inputs)
target_sizes = torch.tensor([image.size[::-1]], device=model.device)
results = processor.post_process_object_detection(outputs, threshold=threshold, target_sizes=target_sizes)[0]
draw = ImageDraw.Draw(image)
font = ImageFont.load_default()
detected_labels = []
for score, label_idx, box in zip(results["scores"], results["labels"], results["boxes"]):
label = model.config.id2label[label_idx.item()]
detected_labels.append(label)
box = [round(i, 2) for i in box.tolist()]
draw.rectangle(box, outline="green", width=2)
draw.text((box[0], box[1] - 10), f"{label}: {score:.2f}", fill="green", font=font)
label_summary = ", ".join(set(detected_labels)) if detected_labels else "No objects detected."
return image, label_summary
# STEP 4: Gradio UI
demo = gr.Interface(
fn=detect_yolos,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Slider(0, 1, value=0.5, label="Confidence Threshold")
],
outputs=[
gr.Image(type="pil", label="Image with Detections"),
gr.Textbox(label="Detected Object Names")
],
title="📦 YOLOS Object Detection + Label List",
description="Detects objects using YOLOS and lists all object names in a textbox."
)
demo.launch()
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