Upload new_app.py

new_app.py

@@ -0,0 +1,98 @@
+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}")