import streamlit as st import keras import numpy as np from PIL import Image import io import urllib.request st.set_page_config(layout="wide") st.markdown(""" """, unsafe_allow_html=True) #title col1, col2 = st.columns(2) with col1: buffer, col = st.columns(2) with col: st.header('Overpass Identifier') with col2: buffer1, col, buffer2 = st.columns(3) with col: st.image('overpass.jpg', width = 190) st.write("---") #load model and initialize image size required by model. uploaded images are resized to indicated size img_height = 640 img_width = 640 state = st.session_state if "loaded_model" not in state: state.loaded_model = keras.models.load_model("0.0008-0.92.keras") if "lat" not in state: state.lat = 39.11 if "lng" not in state: state.lng = -86.56 if "coords_submitted" not in state: state.coords_submitted = False #if "img_submitted" not in state: #state.img_submitted = False if "img" not in state: state.img = None col1, col2, col3 = st.columns(3) with col3: #header st.subheader('Enter latitude/longitude coordinates:') coll, colr= st.columns(2) with coll: state.lat = st.number_input('Latitude', value=39.11, min_value=-90., max_value=90., step=.01) st.write('The current lat/long are:') with colr: state.lng = st.number_input('Longitude', value=-86.56, min_value=-180., max_value=180., step=.01) st.write(str(state.lat)+', '+str(state.lng)) with st.form("my_form"): submit_button = st.form_submit_button( label="Get Image and Prediction", on_click=lambda: state.update(coords_submitted=True)) #header #st.subheader('Upload a satellite image:') #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: #state.img = Image.open(img_buffer).convert("RGB") with col2: if state.coords_submitted: state.coords_submitted = False try: url = "https://maps.googleapis.com/maps/api/staticmap?center="+str(state.lat)+","+str(state.lng)+"&zoom=16&size=640x640&maptype=satellite&key=AIzaSyCzzVb_qf0TQgLw3K2y5EE6geyzE6KzQuA" buffer = io.BytesIO(urllib.request.urlopen(url).read()) state.img = Image.open(buffer).convert("RGB") except Exception as e: st.write("Error! Could not access Google Static API. Error code:",e) if state.img is not None: st.image(state.img, use_column_width = True) with col1: st.subheader("Prediction") if state.img is not None: img_array = np.array(state.img) batch_size = 1 img_array = np.reshape(img_array,[batch_size,img_height,img_width,3]) result = state.loaded_model.predict(img_array) crossing_chance = result[0][1]*100 status = None while status is None: if crossing_chance >= 0: status = "extremely un" if crossing_chance >= 10: status = "highly un" if crossing_chance >= 20: status = "pretty un" if crossing_chance >= 30: status = "slightly un" if crossing_chance >= 40: status = "a tiny bit more unlikely than " if crossing_chance >= 50: status = "a tiny bit more likely than un" if crossing_chance >= 60: status = "slightly " if crossing_chance >= 70: status = "pretty " if crossing_chance >= 80: status = "highly " if crossing_chance >= 90: status = "extremely " st.write(f"It's {status}likely there's at least one overpass here.") st.write("") st.write(f"In fact, the likelihood of at least one overpass is {np.round(crossing_chance,decimals=2)}%.")