Delete app.py

app.py

@@ -1,79 +0,0 @@
-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")