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import requests
from ultralytics import YOLO
import cv2
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import gradio as gr
model = YOLO('best (4).pt')
def index(img_url):
response = requests.get(img_url, stream=True)
img_array = np.asarray(bytearray(response.content), dtype=np.uint8)
img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
print(img_url)
classes_ = {0: 'noti', 1: 'pop'}
results = model.predict(source=img, conf = 0.7)
boxes = results[0].boxes.xyxy.tolist()
classes = results[0].boxes.cls.tolist()
names = results[0].names
confidences = results[0].boxes.conf.tolist()
print(boxes)
print(classes)
print(names)
print(confidences)
result_dict = {"boxes": boxes, "classes": classes, "names": names, "confidence": confidences}
return len(boxes)
inputs_image_url = [
gr.Textbox(type="text", label="Image URL"),
]
outputs_result_dict = [
gr.Textbox(type="text", label="Result Dictionary"),
]
interface_image_url = gr.Interface(
fn=index,
inputs=inputs_image_url,
outputs=outputs_result_dict,
title="Popup detection",
cache_examples=False,
)
gr.TabbedInterface(
[interface_image_url],
tab_names=['Image inference']
).queue().launch() |