Update new_app.py

new_app.py

@@ -2,99 +2,133 @@ import streamlit as st
 import keras
 import numpy as np
 from PIL import Image
-import io, os, urllib.request
+import io, urllib.request, os
 
 st.set_page_config(layout="wide")
 
-# ---------- Caching & helpers ----------
-@st.cache_data(show_spinner=False, ttl=3600)
-def fetch_satellite_tile(lat, lng, zoom=16, size=(640, 640), api_key=""):
+
+@st.cache_data(show_spinner=False, ttl=600)
+def fetch_satellite_tile(lat, lng, zoom=16, size="640x640", api_key=""):
     if not api_key:
-        raise RuntimeError("Google Static Maps API key is missing.")
-    size_str = f"{size[0]}x{size[1]}"
+        raise RuntimeError("Missing Google Static Maps API key in env var 'goog_api'.")
     url = (
-        "https://maps.googleapis.com/maps/api/staticmap?"
-        f"center={lat},{lng}&zoom={zoom}&size={size_str}&maptype=satellite&scale=2&key={api_key}"
+       f"https://maps.googleapis.com/maps/api/staticmap?"
+       f"center={lat},{lng}&zoom={zoom}&size={size}&maptype=satellite&key={api_key}"
     )
-    req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
-    with urllib.request.urlopen(req, timeout=10) as resp:
-        buffer = io.BytesIO(resp.read())
+    buffer = io.BytesIO(urllib.request.urlopen(url, timeout=10).read())
     return Image.open(buffer).convert("RGB")
 
+
 @st.cache_resource
 def get_model():
+    # compile=False skips optimizer/state rebuild, which saves time
     return keras.models.load_model("0.0008-0.92.keras", compile=False)
 
-def preprocess(img: Image.Image, size=(640, 640)) -> np.ndarray:
-    arr = np.asarray(img, dtype=np.float32)  # no /255.0, no resize
-    return np.expand_dims(arr, axis=0)
 
-def status_text(pct: float) -> str:
-    if pct >= 90:  return "extremely likely"
-    if pct >= 60:  return "likely"
-    if pct >= 40:  return "a coin toss whether"
-    if pct >= 10:  return "unlikely"
-    return "extremely unlikely"
-
-# ---------- Styling ----------
 st.markdown("""
-<style>
-.block-container { padding-top: 1rem; padding-bottom: 0rem; padding-left: 5rem; padding-right: 5rem; }
-</style>
-""", unsafe_allow_html=True)
-
-# ---------- Header row (two columns) ----------
-h1, h2 = st.columns([1,1], gap="large")
-with h1:
-    st.header("Overpass Identifier")
-with h2:
-    st.image("overpass.png")
+        <style>
+               .block-container {
+                    padding-top: 1rem;
+                    padding-bottom: 0rem;
+                    padding-left: 5rem;
+                    padding-right: 5rem;
+                }
+        </style>
+        """, unsafe_allow_html=True)
+
+#title
+col1, col2 = st.columns(2)
+with col1:
+    _, col = st.columns(2)
+    with col:
+        st.header('Overpass Identifier')
+with col2:
+    _, col, _ = st.columns(3)
+    with col:
+        st.image('overpass.png')
 
 st.write("---")
 
-# Defaults
-img_size = (640, 640)
-model = get_model()
-lat = st.session_state.get("lat", 39.11)
-lng = st.session_state.get("lng", -86.56)
-
-# ---------- Main content row (three columns: Pred | Image | Inputs) ----------
-pred_col, img_col, form_col = st.columns([1,1,1], gap="large")
-
-# Right: inputs (form to avoid mid-typing reruns)
-with form_col:
-    st.subheader("Enter latitude/longitude coordinates:")
-    with st.form("coords"):
-        c1, c2 = st.columns(2)
-        with c1:
-            lat = st.number_input("Latitude", value=float(lat), min_value=-90.0, max_value=90.0, step=0.01, format="%.4f")
-        with c2:
-            lng = st.number_input("Longitude", value=float(lng), min_value=-180.0, max_value=180.0, step=0.01, format="%.4f")
-        submitted = st.form_submit_button("Get Image and Prediction")
-
-# Center: image
-img = None
-if submitted:
-    st.session_state.lat, st.session_state.lng = lat, lng
+#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
+state.loaded_model = get_model()
+
+#if "loaded_model" not in state:
+#    with st.spinner('Loading model. This may take a few seconds...'):
+#        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" not in state:
+    state.img = None
+
+# Preload default image once
+if state.img is None:
     try:
-        with st.spinner("Fetching satellite tile..."):
-            img = fetch_satellite_tile(lat, lng, size=img_size, api_key=os.getenv("goog_api", ""))
+        api_key = os.getenv("goog_api", "")
+        state.img = fetch_satellite_tile(state.lat, state.lng, api_key=api_key)
     except Exception as e:
-        st.error(f"Error fetching image: {e}")
+        st.info(f"Couldn’t fetch default tile: {e}")
 
-with img_col:
-    if img is not None:
-        st.image(img, caption=f"{lat:.4f}, {lng:.4f}", use_column_width=True)
+col1, col2, col3 = st.columns(3)
 
-# Left: prediction
-with pred_col:
+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))
+    
+with col2:
+    if state.coords_submitted:
+        state.coords_submitted = False
+        try:
+            api_key = os.getenv("goog_api", "")
+            state.img = fetch_satellite_tile(state.lat, state.lng, api_key=api_key)
+        except Exception as e:
+            st.error(f"Error fetching image: {e}")
+
+    if state.img is not None:
+        st.image(state.img, use_container_width = True)
+
+with col1:
     st.subheader("Prediction")
-    if img is not None:
+    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])
         with st.spinner("Running inference..."):
-            x = preprocess(img, size=img_size)
-            y = model(x, training=False).numpy()     # adjust index if your model differs
-            crossing_chance = float(y[0][1] * 100.0)
-        st.markdown(f"**It’s {status_text(crossing_chance)} that there’s an overpass here.**")
-        st.write(f"Estimated probability: **{crossing_chance:.2f}%**.")
-    else:
-        st.caption("Submit coordinates to see the satellite image and prediction.")
+            result = state.loaded_model.predict(img_array)
+        crossing_chance = result[0][1]*100
+        status = None
+        if crossing_chance >= 90:
+            status = "extremely likely"
+        elif crossing_chance >= 60:
+            status = "likely"
+        elif crossing_chance >= 40:
+            status = "a coin toss whether"
+        elif crossing_chance >= 10:
+            status = "unlikely"
+        elif crossing_chance >= 0:
+            status = "extremely unlikely"
+
+        st.write(f"It's {status} there's an overpass here.")
+        st.write("")
+        st.write(f"In fact, the likelihood of at least one overpass is {np.round(crossing_chance,decimals=2)}%.")