Update new_app.py

new_app.py

@@ -1,98 +1,101 @@
-import streamlit as st
-import keras
-import numpy as np
-from PIL import Image
-import io, os, urllib.request
-
-st.set_page_config(layout="wide")
-
-@st.cache_data(show_spinner=False, ttl=3600)
-def fetch_satellite_tile(lat, lng, zoom=16, size=(640, 640), api_key=""):
-    if not api_key:
-        raise RuntimeError("Google Static Maps API key is missing.")
-    # Build URL
-    size_str = f"{size[0]}x{size[1]}"
-    url = (
-        "https://maps.googleapis.com/maps/api/staticmap?"
-        f"center={lat},{lng}&zoom={zoom}&size={size_str}&maptype=satellite&scale=2&key={api_key}"
-    )
-    # Fetch with timeout
-    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())
-    return Image.open(buffer).convert("RGB")
-
-@st.cache_resource
-def get_model():
-    # compile=False speeds loading; prefer model(...) over .predict for TF
-    return keras.models.load_model("0.0008-0.92.keras", compile=False)
-
-def preprocess(img: Image.Image, size=(640, 640)) -> np.ndarray:
-    img = img.resize(size, Image.BILINEAR)
-    arr = np.asarray(img, dtype=np.float32)
-    # adjust this normalization to match training
-    arr = arr / 255.0
-    return np.expand_dims(arr, axis=0)
-
-def status_text(pct: float) -> str:
-    if pct >= 90:
-        return "extremely likely"
-    elif pct >= 60:
-        return "likely"
-    elif pct >= 40:
-        return "a coin toss whether"
-    elif pct >= 10:
-        return "unlikely"
-    else:
-        return "extremely unlikely"
-
-st.markdown("""
-<style>
-.block-container { padding-top: 1rem; padding-bottom: 0rem; padding-left: 5rem; padding-right: 5rem; }
-</style>
-""", unsafe_allow_html=True)
-
-img_size = (640, 640)
-model = get_model()
-
-col1, col2 = st.columns(2)
-with col1:
-    st.header("Overpass Identifier")
-with col2:
-    st.image("overpass.png")
-
-st.write("---")
-
-# Defaults
-lat = st.session_state.get("lat", 39.11)
-lng = st.session_state.get("lng", -86.56)
-
-with st.form("coords"):
-    st.subheader("Enter latitude/longitude coordinates:")
-    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")
-
-if submitted:
-    st.session_state.lat, st.session_state.lng = lat, lng
-    api_key = os.getenv("goog_api", "")
-    try:
-        with st.spinner("Fetching satellite tile..."):
-            img = fetch_satellite_tile(lat, lng, size=img_size, api_key=api_key)
-        st.image(img, caption=f"{lat:.4f}, {lng:.4f}", use_column_width=True)
-
-        with st.spinner("Running inference..."):
-            x = preprocess(img, size=img_size)
-            # Prefer model(x, training=False) for speed; adjust index if your model output differs
-            y = model(x, training=False).numpy()
-            crossing_chance = float(y[0][1] * 100.0)
-
-        status = status_text(crossing_chance)
-        st.markdown(f"**It’s {status} that there’s an overpass here.**")
-        st.write(f"In fact, the likelihood of at least one overpass is **{crossing_chance:.2f}%**.")
-
-    except Exception as e:
-        st.error(f"Error fetching image or running prediction: {e}")
+import streamlit as st
+import keras
+import numpy as np
+from PIL import Image
+import io, os, urllib.request
+
+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=""):
+    if not api_key:
+        raise RuntimeError("Google Static Maps API key is missing.")
+    size_str = f"{size[0]}x{size[1]}"
+    url = (
+        "https://maps.googleapis.com/maps/api/staticmap?"
+        f"center={lat},{lng}&zoom={zoom}&size={size_str}&maptype=satellite&scale=2&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())
+    return Image.open(buffer).convert("RGB")
+
+@st.cache_resource
+def get_model():
+    return keras.models.load_model("0.0008-0.92.keras", compile=False)
+
+def preprocess(img: Image.Image, size=(640, 640)) -> np.ndarray:
+    img = img.resize(size, Image.BILINEAR)
+    arr = np.asarray(img, dtype=np.float32) / 255.0
+    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")
+
+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
+    try:
+        with st.spinner("Fetching satellite tile..."):
+            img = fetch_satellite_tile(lat, lng, size=img_size, api_key=os.getenv("goog_api", ""))
+    except Exception as e:
+        st.error(f"Error fetching image: {e}")
+
+with img_col:
+    if img is not None:
+        st.image(img, caption=f"{lat:.4f}, {lng:.4f}", use_column_width=True)
+
+# Left: prediction
+with pred_col:
+    st.subheader("Prediction")
+    if img is not None:
+        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.")