streamlit_app.py

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")