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.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 best_model.keras filter=lfs diff=lfs merge=lfs -text
+0.0008-0.92.keras filter=lfs diff=lfs merge=lfs -text

0.0008-0.92.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6e3e3765a66ab2d69de87338880087525386c6146138422fc0dcd27405ffcb00
+size 2335883425

streamlit_app.py

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+import streamlit as st
+import keras
+import numpy as np
+from PIL import Image
+
+st.set_page_config(layout="wide")
+
+#title
+st.title('Crossing Identifier')
+
+#header
+st.header('Choose whether you\'d like to enter a latitude/longitude coordinates, or upload a satellite image.') 
+
+
+
+state = st.session_state
+
+if "dict_options" not in state:
+    state.dict_options = {}
+
+if "submitted" not in state:
+    state.submitted = False
+
+options = ["option1", "option2", "option3", "option4"]
+
+col1, col2, col3 = st.columns(3)
+
+with col1.form("my_form"):
+    new_country = st.text_input("New country")
+    submit_button = st.form_submit_button(
+        label="Add new country", on_click=lambda: state.update(submitted=True)
+    )
+
+if state.submitted:
+    state.dict_options[new_country] = col2.multiselect(
+        f"Select the options you want for {new_country}",
+        options,
+        default=options[:2],
+    )
+    col2.write(state.dict_options)
+
+
+    
+#divide app into two columns
+col1, col2 = st.columns(2)
+
+#load model and initialize image size required by model. uploaded images are resized to indicated size
+loaded_model = keras.models.load_model("0.0008-0.92.keras")
+img_height = 640
+img_width = 640
+
+#place to enter
+
+#place to enter coordinates (or upload) and display image
+with col1:
+    enter_coords = st.button("Enter Coordinates")
+    if enter_coords:
+        st.write(":smile:")
+    upload_img = st.button("Upload an Image")
+    if enter_coords:
+        st.write("ok")
+
+    st.header('Please upload a satellite image, or enter a latitude/longitude pair') 
+    img_buffer = st.file_uploader("Upload a satellite image file (format: .png, .jpeg, or .jpg).",type=['png', 'jpeg', 'jpg'])
+    if img_buffer is not None:
+        st.image(img_buffer, use_column_width = True)
+
+#place to display prediction result
+with col2:
+    if img_buffer is not None:
+        st.header('Result')
+        img = Image.open(img_buffer).convert("RGB")
+        img_array = np.array(img)
+        batch_size = 1
+        img_array = np.reshape(img_array,[batch_size,img_height,img_width,3])
+        result = loaded_model.predict(img_array)
+        st.write("Your prediction is:")
+        st.write(f"{np.round(result[0][0]*100,decimals=2)}% chance of no crossing")
+        st.write(f"{np.round(result[0][1]*100,decimals=2)}% chance of at least one crossing")