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README.md

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+---
+title: GeoMetals Predictor
+emoji: 🧪
+colorFrom: indigo
+colorTo: red
+sdk: streamlit
+sdk_version: "1.30.0"
+app_file: app.py
+pinned: false
+---
+
+# GeoMetals Predictor
+
+This app predicts concentrations of six heavy metals (Fe, Cr, Mn, Mo, In, Ta) based on user-selected locations in Metro Phoenix and stormwater runoff status using a Gradient Boosting Regressor model.

app.py

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+# app.py
+import streamlit as st
+import folium
+from streamlit_folium import st_folium
+from model import predict_metals
+
+st.set_page_config(page_title="GeoMetals Predictor", layout="wide")
+
+st.title("🧪 GeoMetals Exposure Predictor")
+st.write("Click a location on the map and select stormwater runoff status to predict heavy metal concentrations.")
+
+# Map setup
+st.subheader("📍 Select Location")
+m = folium.Map(location=[33.45, -112.07], zoom_start=10)  # Metro Phoenix default center
+
+marker = folium.LatLngPopup()
+m.add_child(marker)
+
+map_data = st_folium(m, width=700, height=500)
+
+# User location input
+lat, lon = None, None
+if map_data["last_clicked"]:
+    lat = map_data["last_clicked"]["lat"]
+    lon = map_data["last_clicked"]["lng"]
+    st.success(f"Selected Coordinates: ({lat:.5f}, {lon:.5f})")
+
+# Stormwater input
+stormwater = st.radio("Stormwater Runoff Present?", ("Yes", "No")) == "Yes"
+
+# Submit and predict
+if st.button("🔍 Predict Heavy Metal Concentrations") and lat and lon:
+    prediction = predict_metals(lat, lon, stormwater)
+
+    st.subheader("🔬 Predicted Heavy Metal Concentrations (ppm)")
+    for metal, value in prediction.items():
+        warning = ""
+        if metal == "Fe_ppm" and value > 300:
+            warning = "⚠️ Above EPA soil screening level for Iron"
+        elif metal == "Cr_ppm" and value > 210:
+            warning = "⚠️ Chromium exceeds safe threshold"
+        elif metal == "Mn_ppm" and value > 500:
+            warning = "⚠️ High Manganese levels"
+        elif metal == "Mo_ppm" and value > 40:
+            warning = "⚠️ Elevated Molybdenum"
+        elif metal == "In_ppm" and value > 0.5:
+            warning = "⚠️ Indium concern"
+        elif metal == "Ta_ppm" and value > 1:
+            warning = "⚠️ Tantalum elevated"
+        st.write(f"**{metal.replace('_ppm', '')}: {value:.2f} ppm** {warning}")
+
+    st.subheader("📉 Legacy Risk Score (Experimental)")
+    score = sum(prediction.values()) / len(prediction)
+    st.metric("Aggregate Risk Score", f"{score:.2f}")
+else:
+    st.info("Click a location on the map and hit 'Predict'.")

geo_metals_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c1e253e9cb2227de42757352b5e2976a85f35c09217c359ab0720963caed5902
+size 513358

model.py

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+# model.py
+import joblib
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.multioutput import MultiOutputRegressor
+from sklearn.model_selection import train_test_split
+
+MODEL_PATH = "geo_metals_model.joblib"
+
+model = joblib.load(MODEL_PATH)
+
+def predict_metals(lat, lon, stormwater):
+    input_data = pd.DataFrame([{
+        'Latitude': lat,
+        'Longitude': lon,
+        'Stormwater': 1 if stormwater else 0
+    }])
+    prediction = model.predict(input_data)[0]
+    metals = ['Fe_ppm', 'Cr_ppm', 'Mn_ppm', 'Mo_ppm', 'In_ppm', 'Ta_ppm']
+    return dict(zip(metals, prediction))

requirements.txt

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+streamlit==1.30.0
+folium==0.15.1
+streamlit-folium==0.11.1
+scikit-learn==1.1.3
+pandas
+joblib