# import gradio as gr
# import spaces
# from huggingface_hub import hf_hub_download
# def download_models(model_id):
# hf_hub_download("SakshiRathi77/void-space-detection", filename=f"{model_id}", local_dir=f"./")
# return f"./{model_id}"
# @spaces.GPU
# def yolov9_inference(img_path, model_id, image_size, conf_threshold, iou_threshold):
# """
# Load a YOLOv9 model, configure it, perform inference on an image, and optionally adjust
# the input size and apply test time augmentation.
# :param model_path: Path to the YOLOv9 model file.
# :param conf_threshold: Confidence threshold for NMS.
# :param iou_threshold: IoU threshold for NMS.
# :param img_path: Path to the image file.
# :param size: Optional, input size for inference.
# :return: A tuple containing the detections (boxes, scores, categories) and the results object for further actions like displaying.
# """
# # Import YOLOv9
# import yolov9
# # Load the model
# model_path = download_models(model_id)
# model = yolov9.load(model_path, device="cuda:0")
# # Set model parameters
# model.conf = conf_threshold
# model.iou = iou_threshold
# # Perform inference
# results = model(img_path, size=image_size)
# # Optionally, show detection bounding boxes on image
# output = results.render()
# return output[0]
# def app():
# with gr.Blocks():
# with gr.Row():
# with gr.Column():
# img_path = gr.Image(type="filepath", label="Image")
# model_path = gr.Dropdown(
# label="Model",
# choices=[
# "state_dict.pt"
# ],
# value="state_dict.pt",
# )
# image_size = gr.Slider(
# label="Image Size",
# minimum=320,
# maximum=1280,
# step=32,
# value=640,
# )
# conf_threshold = gr.Slider(
# label="Confidence Threshold",
# minimum=0.1,
# maximum=1.0,
# step=0.1,
# value=0.4,
# )
# iou_threshold = gr.Slider(
# label="IoU Threshold",
# minimum=0.1,
# maximum=1.0,
# step=0.1,
# value=0.5,
# )
# yolov9_infer = gr.Button(value="Inference")
# with gr.Column():
# output_numpy = gr.Image(type="numpy",label="Output")
# yolov9_infer.click(
# fn=yolov9_inference,
# inputs=[
# img_path,
# model_path,
# image_size,
# conf_threshold,
# iou_threshold,
# ],
# outputs=[output_numpy],
# )
# # gr.Examples(
# # examples=[
# # [
# # "data/zidane.jpg",
# # "gelan-e.pt",
# # 640,
# # 0.4,
# # 0.5,
# # ],
# # [
# # "data/huggingface.jpg",
# # "yolov9-c.pt",
# # 640,
# # 0.4,
# # 0.5,
# # ],
# # ],
# # fn=yolov9_inference,
# # inputs=[
# # img_path,
# # model_path,
# # image_size,
# # conf_threshold,
# # iou_threshold,
# # ],
# # outputs=[output_numpy],
# # cache_examples=True,
# # )
# gradio_app = gr.Blocks()
# with gradio_app:
# gr.HTML(
# """
#
# YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
#
# """)
# gr.HTML(
# """
#
# Follow me for more!
#
# """)
# with gr.Row():
# with gr.Column():
# app()
# gradio_app.launch(debug=True)
# make sure you have the following dependencies
import gradio as gr
import torch
from torchvision import transforms
from PIL import Image
# Load the YOLOv9 model
model_path = "best.pt" # Replace with the path to your YOLOv9 model
model = torch.load(model_path)
# Define preprocessing transforms
preprocess = transforms.Compose([
transforms.Resize((640, 640)), # Resize image to model input size
transforms.ToTensor(), # Convert image to tensor
])
# Define a function to perform inference
def detect_void(image):
# Preprocess the input image
image = Image.fromarray(image)
image = preprocess(image).unsqueeze(0) # Add batch dimension
# Perform inference
with torch.no_grad():
output = model(image)
# Post-process the output if needed
# For example, draw bounding boxes on the image
# Convert the image back to numpy array
# and return the result
return output.squeeze().numpy()
# Define Gradio interface components
input_image = gr.inputs.Image(shape=(640, 640), label="Input Image")
output_image = gr.outputs.Image(label="Output Image")
# Create Gradio interface
gr.Interface(fn=detect_void, inputs=input_image, outputs=output_image, title="Void Detection App").launch()