import gradio as gr
import cv2
import requests
import os
from ultralytics import YOLO
path = ['./data/0068.jpg', './data/0210.jpg', './data/IMG_7078.jpg', './data/IMG_7103.jpg', './data/IMG_7705.jpg']
model_path = './best.pt'
model = YOLO(model_path)
def detect_cheerios(image_path):
# Run inference on the input image
results = model(image_path)
image = results[0].plot() [:,:,::-1]
return image
iface = gr.Interface(
fn=detect_cheerios,
inputs=gr.components.Image(type="filepath", label="Input Image"),
outputs=gr.Image(),
title="Cheerios detector",
description='This model is trained to detect one Cheerios box in an indoor setting, and it is trained using synthetic data from the Duality.ai simulation software: FalconEditor. In a world where data regulations are starting to limit AI useage, FalconEditor offers a way to obtain large, regulation-passing datasets easily and quickly. Dive into synthetic data by creating a FREE learner account at falcon.duality.ai. See if you can train a more robust model that functions in a larger variety of domains! Follow along with tutorials as we walk you through how to assemble a scenario and collect data for AI training. Used by companies like P&G, KEF Robotics, and AWS, this powerful software is now available for non-commercial use. Go to this link to sign up and start simulating!',
examples= path,
# gradio.HTML(https://falcon.duality.ai/secure/documentation?learnWelcome=true&sidebarMode=learn),
)
# Launch the interface
iface.launch()