randomly direct sahi related urls during spinner

app.py

@@ -4,6 +4,7 @@ import sahi.model
 import sahi.predict
 from PIL import Image
 import numpy
+import random
 
 
 MMDET_YOLACT_MODEL_URL = "https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco/yolact_r50_1x8_coco_20200908-f38d58df.pth"
@@ -51,8 +52,8 @@ def get_mmdet_model(model_name: str):
         config_path = sahi.utils.mmdet.download_mmdet_config(
             model_name="yolox", config_file_name="yolox_tiny_8x8_300e_coco.py"
         )
-    elif model_name == "fasterrcnn":
-        model_path = "fasterrcnn.pt"
+    elif model_name == "faster_rcnn":
+        model_path = "faster_rcnn.pt"
         sahi.utils.file.download_from_url(
             MMDET_FASTERRCNN_MODEL_URL,
             model_path,
@@ -130,7 +131,7 @@ st.markdown(
     unsafe_allow_html=True,
 )
 st.markdown(
-    "<p style='text-align: center'>SAHI is a lightweight vision library for performing large scale object detection/ instance segmentation.. <a href='https://github.com/obss/sahi'>SAHI Github</a> | <a href='https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80'>SAHI Blog</a> | <a href='https://github.com/fcakyon/yolov5-pip'>YOLOv5 Github</a> </p>",
+    "<p style='text-align: center'>SAHI is a lightweight vision library for performing large scale object detection/ instance segmentation.. <a href='https://github.com/obss/sahi'>SAHI Github</a> | <a href='https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80'>SAHI Blog</a> | <a href='https://huggingface.co/spaces/fcakyon/sahi-yolov5'>SAHI+YOLOv5 Demo</a> </p>",
     unsafe_allow_html=True,
 )
 
@@ -162,7 +163,7 @@ with col1:
     st.image(image, caption=slider, width=300)
 with col3:
     model_name = st.selectbox(
-        "Select MMDetection model:", ("fasterrcnn", "yolact", "yolox")
+        "Select MMDetection model:", ("faster_rcnn", "yolact", "yolox"), index=2
     )
     slice_size = st.number_input("slice_size", 256, value=512, step=256)
     overlap_ratio = st.number_input("overlap_ratio", 0.0, 0.6, value=0.2, step=0.2)
@@ -186,21 +187,58 @@ if image_file is not None:
 else:
     image = Image.open(slider)
 
+
+class SpinnerTexts:
+    def __init__(self):
+        self.ind_history_list = []
+        self.text_list = [
+            "Meanwhile check out [MMDetection Colab notebook of SAHI](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb)!",
+            "Meanwhile check out [aerial object detection with SAHI](https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98?gi=b434299595d4)!",
+            "Meanwhile check out [COCO Utilities of SAHI](https://github.com/obss/sahi/blob/main/docs/COCO.md)!",
+            "Meanwhile check out [FiftyOne utilities of SAHI](https://github.com/obss/sahi#fiftyone-utilities)!",
+            "Meanwhile check out [easy installation of SAHI](https://github.com/obss/sahi#getting-started)!",
+            "Meanwhile check out [give a Github star to SAHI](https://github.com/obss/sahi/stargazers)!",
+            "Meanwhile check out [easy installation of SAHI](https://github.com/obss/sahi#getting-started)!",
+            "Meanwhile check out [Medium blogpost of SAHI](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80)!",
+            "Meanwhile check out [YOLOv5 HF Spaces demo of SAHI](https://huggingface.co/spaces/fcakyon/sahi-yolov5)!",
+        ]
+
+    def _store(self, ind):
+        if len(self.ind_history_list) == 6:
+            self.ind_history_list.pop(0)
+        self.ind_history_list.append(ind)
+
+    def get(self):
+        ind = 0
+        while ind in self.ind_history_list:
+            ind = random.randint(0, len(self.text_list) - 1)
+        self._store(ind)
+        return self.text_list[ind]
+
+
+if "last_spinner_texts" not in st.session_state:
+    st.session_state["last_spinner_texts"] = SpinnerTexts()
+
 if submit:
     # perform prediction
     st.markdown(
         "<h3 style='text-align: center'> Results: </h1>",
         unsafe_allow_html=True,
     )
-    with st.spinner(text="Downloading model weight.."):
+
+    with st.spinner(
+        text="Downloading model weight.. "
+        + st.session_state["last_spinner_texts"].get()
+    ):
         detection_model = get_mmdet_model(model_name)
+
     if model_name == "yolox":
         image_size = 416
     else:
         image_size = 640
 
     with st.spinner(
-        text="Performing prediction.. Meanwhile check out [other features of SAHI](https://github.com/obss/sahi/blob/main/README.md)!"
+        text="Performing prediction.. " + st.session_state["last_spinner_texts"].get()
     ):
         output_1, output_2 = sahi_mmdet_inference(
             image,