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# Description: This is the main file to run the Gradio interface for the object detection model.
from ultralytics import YOLO
from PIL import Image
import gradio as gr
from huggingface_hub import snapshot_download
import os
model_path = "best_int8_openvino_model"
# Example paths for Gradio
image_examples = [["DurianMangosteen1.jpg"], ["DurianMangosteen2.jpg"]]
# Load the model
def load_model(repo_id):
download_dir = snapshot_download(repo_id) # download the model from the Hugging Face Hub
print(download_dir)
path = os.path.join(download_dir, "best_int8_openvino_model") # path to the model
print(path)
detection_model = YOLO(path, task='detect') # load the model
return detection_model
# Predict the image
def predict(pilimg):
source = pilimg
# x = np.asarray(pilimg)
# print(x.shape)
result = detection_model.predict(source, conf=0.4, iou=0.6) # confidence threshold, intersection over union threshold
#print("Result: ", result)
if not result or len(result[0].boxes) == 0: # if no object detected
gr.Warning("No object detected in the image!")
else:
img_bgr = result[0].plot() # plot the image
out_pilimg = Image.fromarray(img_bgr[..., ::-1]) # RGB-order PIL image
return out_pilimg
REPO_ID = "ITI107-2024S2/8035531F" # The repo ID of the model
detection_model = load_model(REPO_ID)
title = "Detect Durian and Mangosteen (King and Queen of Fruits) In The Image"
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Input Image"),
outputs=gr.Image(type="pil", label="Object Detected Image"),
title=title,
examples=image_examples,
)
# Launch the interface
interface.launch(share=True)
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