Update water_quality_index.py

water_quality_index.py

@@ -1,114 +1,161 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import joblib
-import plotly.express as px
-import base64
-from sklearn.preprocessing import LabelEncoder
-
-def run():
-    # === Load Trained Objects ===
-    svc_model = joblib.load("svc_model.pkl")
-    xgb_model = joblib.load("xgb_model.pkl")
-    imputer = joblib.load("imputer.pkl")
-    scaler = joblib.load("scaler.pkl")
-    label_encoder = joblib.load("label_encoder.pkl")  # already fitted
-
-    # === Expected Columns ===
-    feature_cols = [
-        "pH (Potential Hydrogen)",
-        "BOD (Biological Oxygen Demand) (mg/L)",
-        "COD (Chemical Oxygen Demand) (mg/L)",
-        "TSS (Total Suspended Solid) (mg/L)",
-        "DO (Dissolved Oxygen) (mg/L)",
-        "NO3N (Nitrat) (mg/L)",
-        "Total Phosphat (mg/L)",
-        "Fecal Coliform (MPN/100 mL)"
-    ]
-
-    # === Streamlit Setup ===
-    st.set_page_config(page_title="Water Quality Classifier Dashboard", layout="wide")
-    st.title("💧 Water Quality Prediction and Dashboard")
-
-    # === Model Selector ===
-    model_choice = st.selectbox("Select Model", ["SVC + SMOTETomek", "XGBoost + SMOTETomek"])
-    model = svc_model if model_choice == "SVC + SMOTETomek" else xgb_model
-
-    # === Input Section ===
-    st.header("📥 Input Data")
-    data_option = st.radio("Choose Input Method", ["Upload CSV", "Manual Entry"])
-    input_df = None
-
-    if data_option == "Upload CSV":
-        uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
-        if uploaded_file:
-            df = pd.read_csv(uploaded_file)
-            missing_cols = [col for col in feature_cols if col not in df.columns]
-            if missing_cols:
-                st.error(f"Missing required columns: {missing_cols}")
-            else:
-                input_df = df[feature_cols]
-    else:
-        with st.form("manual_form"):
-            ph = st.number_input("pH", min_value=0.0, max_value=14.0, value=7.0)
-            bod = st.number_input("BOD (mg/L)", min_value=0.0, max_value=100.0, value=2.0)
-            cod = st.number_input("COD (mg/L)", min_value=0.0, max_value=500.0, value=10.0)
-            tss = st.number_input("TSS (mg/L)", min_value=0.0, max_value=1000.0, value=20.0)
-            do = st.number_input("DO (mg/L)", min_value=0.0, max_value=20.0, value=5.0)
-            no3 = st.number_input("NO3N (mg/L)", min_value=0.0, max_value=10.0, value=1.0)
-            tp = st.number_input("Total Phosphat (mg/L)", min_value=0.0, max_value=10.0, value=0.1)
-            fecal = st.number_input("Fecal Coliform (MPN/100 mL)", min_value=0.0, max_value=1000000.0, value=500.0)
-            submitted = st.form_submit_button("Predict")
-
-        if submitted:
-            input_df = pd.DataFrame([{
-                "pH (Potential Hydrogen)": ph,
-                "BOD (Biological Oxygen Demand) (mg/L)": bod,
-                "COD (Chemical Oxygen Demand) (mg/L)": cod,
-                "TSS (Total Suspended Solid) (mg/L)": tss,
-                "DO (Dissolved Oxygen) (mg/L)": do,
-                "NO3N (Nitrat) (mg/L)": no3,
-                "Total Phosphat (mg/L)": tp,
-                "Fecal Coliform (MPN/100 mL)": fecal
-            }])
-
-    # === Prediction Section ===
-    if input_df is not None:
-        st.header("🔍 Prediction Results")
-
-        try:
-            # Preprocessing
-            X_imp = imputer.transform(input_df)
-            X_scaled = scaler.transform(X_imp)
-
-            # Prediction
-            y_proba = model.predict_proba(X_scaled)
-            y_pred = model.predict(X_scaled)
-            pred_class = label_encoder.inverse_transform(y_pred)[0]
-
-            st.markdown(f"### 🧪 Predicted Class: `{pred_class}`")
-
-            fig_pie = px.pie(
-                names=label_encoder.classes_,
-                values=y_proba[0],
-                title="Prediction Probability per Class",
-                color_discrete_sequence=px.colors.qualitative.Set3
-            )
-            st.plotly_chart(fig_pie, use_container_width=True)
-
-            # Export
-            st.subheader("📤 Download Prediction")
-            input_df["Predicted Class"] = pred_class
-            input_df[[f"Prob_{cls}" for cls in label_encoder.classes_]] = y_proba
-            csv = input_df.to_csv(index=False)
-            b64 = base64.b64encode(csv.encode()).decode()
-            href = f'<a href="data:file/csv;base64,{b64}" download="prediction_result.csv">Download CSV File</a>'
-            st.markdown(href, unsafe_allow_html=True)
-
-        except Exception as e:
-            st.error(f"Prediction failed: {e}")
-
-    # === Footer ===
-    st.markdown("---")
-    st.caption("Developed with ❤️ for real-time water quality analysis")
-
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import plotly.express as px
+import base64
+from sklearn.preprocessing import LabelEncoder
+
+# === Thresholds for Rule-Based Classification ===
+thresholds = {
+    'pH_min': 6.0, 'pH_max': 9.0,
+    'BOD': 3.0,
+    'COD': 25.0,
+    'TSS': 50.0,
+    'DO': 4.0,
+    'Nitrate': 10.0,
+    'Phosphate': 0.2,
+    'FecalColiform': 1000
+}
+
+def categorize_sample(row):
+    pH = row['pH (Potential Hydrogen)']
+    BOD = row['BOD (Biological Oxygen Demand) (mg/L)']
+    COD = row['COD (Chemical Oxygen Demand) (mg/L)']
+    DO = row['DO (Dissolved Oxygen) (mg/L)']
+    nitrate = row['NO3N (Nitrat) (mg/L)']
+    phosphate = row['Total Phosphat (mg/L)']
+    fecal = row['Fecal Coliform (MPN/100 mL)']
+    TSS = row['TSS (Total Suspended Solid) (mg/L)']
+
+    if (
+        thresholds['pH_min'] <= pH <= thresholds['pH_max'] and
+        BOD <= thresholds['BOD'] and
+        COD <= thresholds['COD'] and
+        DO >= thresholds['DO'] and
+        nitrate <= thresholds['Nitrate'] and
+        phosphate <= thresholds['Phosphate'] and
+        fecal <= thresholds['FecalColiform'] and
+        TSS <= thresholds['TSS']
+    ):
+        return "Safe", "Safe"
+
+    categories = []
+    if COD > thresholds['COD'] * 1.5 or pH < thresholds['pH_min'] or pH > thresholds['pH_max'] or TSS > thresholds['TSS']:
+        categories.append("Chemical")
+    if BOD > thresholds['BOD'] or DO < thresholds['DO'] or fecal > thresholds['FecalColiform'] or TSS > thresholds['TSS']:
+        categories.append("Biological")
+    if nitrate > thresholds['Nitrate'] or phosphate > thresholds['Phosphate'] or TSS > thresholds['TSS']:
+        categories.append("Eutrophication")
+
+    priority_order = ["Chemical", "Biological", "Eutrophication"]
+    for cat in priority_order:
+        if cat in categories:
+            return ", ".join(categories), cat
+
+    return "Safe", "Safe"
+
+# === Streamlit App ===
+def run():
+    svc_model = joblib.load("svc_model.pkl")
+    xgb_model = joblib.load("xgb_model.pkl")
+    imputer = joblib.load("imputer.pkl")
+    scaler = joblib.load("scaler.pkl")
+    label_encoder = joblib.load("label_encoder.pkl")
+
+    feature_cols = [
+        "pH (Potential Hydrogen)",
+        "BOD (Biological Oxygen Demand) (mg/L)",
+        "COD (Chemical Oxygen Demand) (mg/L)",
+        "TSS (Total Suspended Solid) (mg/L)",
+        "DO (Dissolved Oxygen) (mg/L)",
+        "NO3N (Nitrat) (mg/L)",
+        "Total Phosphat (mg/L)",
+        "Fecal Coliform (MPN/100 mL)"
+    ]
+
+    st.set_page_config(page_title="Water Quality Classifier Dashboard", layout="wide")
+    st.title("💧 Water Quality Prediction and Rule-Based Evaluation")
+
+    model_choice = st.selectbox("Select Model", ["SVC + SMOTETomek", "XGBoost + SMOTETomek"])
+    model = svc_model if model_choice == "SVC + SMOTETomek" else xgb_model
+
+    st.header("📥 Input Data")
+    data_option = st.radio("Choose Input Method", ["Upload CSV", "Manual Entry"])
+    input_df = None
+
+    if data_option == "Upload CSV":
+        uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
+        if uploaded_file:
+            df = pd.read_csv(uploaded_file)
+            missing_cols = [col for col in feature_cols if col not in df.columns]
+            if missing_cols:
+                st.error(f"Missing required columns: {missing_cols}")
+            else:
+                input_df = df[feature_cols]
+    else:
+        with st.form("manual_form"):
+            ph = st.number_input("pH", min_value=0.0, max_value=14.0, value=7.0)
+            bod = st.number_input("BOD (mg/L)", min_value=0.0, max_value=100.0, value=2.0)
+            cod = st.number_input("COD (mg/L)", min_value=0.0, max_value=500.0, value=10.0)
+            tss = st.number_input("TSS (mg/L)", min_value=0.0, max_value=1000.0, value=20.0)
+            do = st.number_input("DO (mg/L)", min_value=0.0, max_value=20.0, value=5.0)
+            no3 = st.number_input("NO3N (mg/L)", min_value=0.0, max_value=10.0, value=1.0)
+            tp = st.number_input("Total Phosphat (mg/L)", min_value=0.0, max_value=10.0, value=0.1)
+            fecal = st.number_input("Fecal Coliform (MPN/100 mL)", min_value=0.0, max_value=1000000.0, value=500.0)
+            submitted = st.form_submit_button("Predict")
+
+        if submitted:
+            input_df = pd.DataFrame([{
+                "pH (Potential Hydrogen)": ph,
+                "BOD (Biological Oxygen Demand) (mg/L)": bod,
+                "COD (Chemical Oxygen Demand) (mg/L)": cod,
+                "TSS (Total Suspended Solid) (mg/L)": tss,
+                "DO (Dissolved Oxygen) (mg/L)": do,
+                "NO3N (Nitrat) (mg/L)": no3,
+                "Total Phosphat (mg/L)": tp,
+                "Fecal Coliform (MPN/100 mL)": fecal
+            }])
+
+    if input_df is not None:
+        st.header("🔍 Prediction Results")
+
+        try:
+            X_imp = imputer.transform(input_df)
+            X_scaled = scaler.transform(X_imp)
+            y_proba = model.predict_proba(X_scaled)
+            y_pred = model.predict(X_scaled)
+            pred_class = label_encoder.inverse_transform(y_pred)[0]
+
+            # Rule-Based Evaluation
+            rule_violations, rule_label = categorize_sample(input_df.iloc[0])
+
+            # Display results
+            st.markdown(f"### 🧪 ML Predicted Class: `{pred_class}`")
+            st.markdown(f"### 📏 Rule-Based Class: `{rule_label}`")
+            st.markdown(f"**Violations Detected:** {rule_violations}")
+
+            fig_pie = px.pie(
+                names=label_encoder.classes_,
+                values=y_proba[0],
+                title="Prediction Probability per Class",
+                color_discrete_sequence=px.colors.qualitative.Set3
+            )
+            st.plotly_chart(fig_pie, use_container_width=True)
+
+            # Export Results
+            input_df["Predicted Class (ML)"] = pred_class
+            input_df["Rule-Based Class"] = rule_label
+            input_df["Rule-Based Violations"] = rule_violations
+            input_df[[f"Prob_{cls}" for cls in label_encoder.classes_]] = y_proba
+            csv = input_df.to_csv(index=False)
+            b64 = base64.b64encode(csv.encode()).decode()
+            href = f'<a href="data:file/csv;base64,{b64}" download="prediction_result.csv">Download CSV File</a>'
+            st.subheader("📤 Download Result")
+            st.markdown(href, unsafe_allow_html=True)
+
+        except Exception as e:
+            st.error(f"Prediction failed: {e}")
+
+    st.markdown("---")
+    st.caption("Developed with ❤️ for integrated ML + expert rule water quality system")