everything accomplished hopefully

app.py

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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 (3).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 result_dict
+
+
+
+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,
+)

best (3).pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea96187ab41db4d90757f82a2f4435f4a233c207a316fe9a0a73fcda3501306b
+size 22550041

requirements.txt

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+flask-mongoengine @ git+https://github.com/idoshr/flask-mongoengine.git@e244408acf440c4208f7ddcd6e5d819cb472e4da
+flask
+requests
+datetime
+pandas
+numpy
+gensim
+requests
+bs4
+tensorflow
+ultralytics
+opencv-python
+matplotlib
+gunicorn
+gevent
+streamlit
+gradio