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README.md

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+
+---
+title: Heavy Metal Predictor
+emoji: 🌎
+colorFrom: indigo
+colorTo: blue
+sdk: streamlit
+sdk_version: "1.30.0"
+app_file: app.py
+pinned: false
+---
+
+# Environmental Contamination Predictor
+
+Click on a map and indicate whether stormwater runoff is present. The app will predict concentrations (ppm) of heavy metals — Fe, Cr, Mn, Mo, In, Ta — and assess risk levels based on EPA thresholds.

app.py

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+
+import streamlit as st
+from streamlit_folium import st_folium
+import folium
+from model import predict_metals
+from utils import get_epa_warnings, calculate_risk_score
+
+st.set_page_config(page_title="Heavy Metal Predictor", layout="wide")
+st.title("🌍 Environmental Heavy Metal Risk Predictor")
+
+st.markdown("Click a location on the map and indicate whether stormwater runoff is present.")
+
+m = folium.Map(location=[33.4484, -112.0740], zoom_start=10)  # Phoenix default
+click_info = st_folium(m, height=500, width=700)
+
+lat = click_info.get("last_clicked", {}).get("lat", None)
+lon = click_info.get("last_clicked", {}).get("lng", None)
+
+storm = st.radio("Stormwater Runoff Present?", ("Yes", "No"))
+stormwater_val = 1 if storm == "Yes" else 0
+
+if lat and lon:
+    st.success(f"Selected Location: ({lat:.4f}, {lon:.4f})")
+
+    if st.button("Predict Contamination"):
+        pred = predict_metals(lat, lon, stormwater_val)
+        epa_flags = get_epa_warnings(pred)
+        risk = calculate_risk_score(pred)
+
+        st.subheader("Predicted Heavy Metal Concentrations (ppm):")
+        for metal, val in pred.items():
+            warn = "⚠️" if epa_flags[metal] else "✅"
+            st.write(f"- **{metal}**: {val:.3f} ppm {warn}")
+
+        st.markdown(f"### 🧪 Risk Score: `{risk}` (higher = more hazardous)")
+else:
+    st.info("Please click on the map to choose a location.")

final_gbm_model_all_metals.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b814725d28bce1d3f4e90e11397a6203a1ec4ffd8b1a224c398b0a2ee84c757d
+size 611611

model.py

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+
+import joblib
+import numpy as np
+
+MODEL_PATH = "final_gbm_model_all_metals.pkl"
+model = joblib.load(MODEL_PATH)
+
+def predict_metals(latitude, longitude, stormwater):
+    X_input = np.array([[latitude, longitude, stormwater]])
+    predictions = model.predict(X_input)[0]
+    metals = ["Fe", "Cr", "Mn", "Mo", "In", "Ta"]
+    return dict(zip(metals, predictions))

requirements.txt

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+streamlit==1.30.0
+folium==0.15.1
+streamlit-folium==0.12.0
+scikit-learn
+joblib
+p
+pandas

utils.py

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+
+EPA_THRESHOLDS_PPM = {
+    "Fe": 300,
+    "Cr": 0.1,
+    "Mn": 0.05,
+    "Mo": 0.1,
+    "In": 0.05,
+    "Ta": 0.01
+}
+
+def get_epa_warnings(predicted):
+    warnings = {}
+    for metal, value in predicted.items():
+        threshold = EPA_THRESHOLDS_PPM.get(metal, float("inf"))
+        warnings[metal] = value > threshold
+    return warnings
+
+def calculate_risk_score(predicted):
+    weights = {"Fe": 1.2, "Cr": 2.5, "Mn": 1.0, "Mo": 1.8, "In": 3.0, "Ta": 2.2}
+    score = sum(predicted[m] * weights.get(m, 1) for m in predicted)
+    return round(score / len(predicted), 2)