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import gradio as gr
from ultralytics import YOLO
from PIL import Image
import os
import cv2
import torch
def load_model(model_input):
model = YOLO(model_input)
if torch.cuda.is_available():
model.to('cuda')
print("Using GPU for inference")
else:
print("Using CPU for inference")
return model
def detect_objects_in_files(model_input, files):
"""
Processes uploaded images for object detection.
"""
if not files:
return "No files uploaded.", []
model = load_model(model_input)
results_images = []
for file in files:
try:
image = Image.open(file).convert("RGB")
results = model(image)
result_img_bgr = results[0].plot()
result_img_rgb = cv2.cvtColor(result_img_bgr, cv2.COLOR_BGR2RGB)
results_images.append(result_img_rgb)
# If you want that images appear one by one (slower)
#yield "Processing image...", results_images
except Exception as e:
return f"Error processing file: {file}. Exception: {str(e)}", []
del model
torch.cuda.empty_cache()
return "Processing completed.", results_images
interface = gr.Interface(
fn=detect_objects_in_files,
inputs=[
gr.File(label="Upload Model file"),
gr.Files(file_types=["image"], label="Select Images"),
],
outputs=[
gr.Textbox(label="Status"),
gr.Gallery(label="Results")
],
title="Object Detection on Images",
description="Upload images to perform object detection. The model will process each image and display the results."
)
if __name__ == "__main__":
interface.launch(server_name="0.0.0.0", server_port=8800)
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