Upload app_utils.py

app_utils.py

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+import streamlit as st
+
+# Strings
+replicate = ":bulb: Choose **ResNet50V2** model and **conv3_block4_out** to get the results as in the example."
+credits = ":memo: Keras example by [@fchollet](https://twitter.com/fchollet)."
+vit_info = ":star: For Vision Transformers, check the excellent [probing-vits](https://huggingface.co/probing-vits) space."
+
+title = "Visualizing What Convnets Learn"
+info_text = """
+                Models in this demo are pre-trained on the ImageNet dataset.
+                The simple visualization process involves creation of input images that maximize the activation of specific filters in a target layer.
+                Such images represent a visualization of the pattern that the filter responds to. 
+            """
+
+
+# Constants and globals
+IMG_WIDTH = 180
+IMG_HEIGHT = 180
+VIS_OPTION = {"only the first filter": 0, "the first 64 filters": 64}
+ITERATIONS = 30
+LEARNING_RATE = 10.0
+
+
+# Streamlit state variables
+if "model_name" not in st.session_state:
+    st.session_state.model_name = None
+
+if "layer_name" not in st.session_state:
+    st.session_state.layer_name = None
+
+if "layer_list" not in st.session_state:
+    st.session_state.layer_list = None
+
+if "model" not in st.session_state:
+    st.session_state.model = None
+
+if "feat_extract" not in st.session_state:
+    st.session_state.feat_extract = None