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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import plotly.express as px
+import plotly.graph_objects as go
+import altair as alt
+import base64
+from sklearn.calibration import calibration_curve
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import accuracy_score, f1_score, log_loss, confusion_matrix
+from sklearn.utils.multiclass import unique_labels
+
+# === Load models ===
+svc_model = joblib.load("svc_pipeline.pkl")
+xgb_model = joblib.load("xgb_pipeline.pkl")
+
+# === App Config ===
+st.set_page_config(page_title="Water Quality Classifier Dashboard", layout="wide")
+st.title("💧 Water Quality Prediction and Model 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("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:
+        input_df = pd.read_csv(uploaded_file)
+else:
+    with st.form("manual_form"):
+        ph = st.number_input("pH", min_value=1.0, 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/100mL)", 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")
+    y_proba = model.predict_proba(input_df)
+    y_pred = model.predict(input_df)
+
+    label_encoder = LabelEncoder()
+    label_encoder.classes_ = np.array(["Biological", "Chemical", "Eutrophication", "Safe"])
+    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)
+
+    # === Download CSV ===
+    st.subheader("📤 Download Prediction")
+    input_df["Predicted Class"] = pred_class
+    input_df[[f"Prob_{c}" for c 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)
+
+# === Footer ===
+st.markdown("---")
+st.markdown("Developed with ❤️ for real-world decision support in water quality monitoring.")
+

label_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:32f95b42cdc2676ef36ff0ff99037431b1ba4d2601e88c04568d99b40e9dca48
+size 582

svc_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:533f0eaa1d3aeb6526056beb04b2e72404d1dbeb06e1e4eeac37e7c2b19fa2bd
+size 238650

xgb_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:66ce23ee36f7901b5f734a652cd43f788efbd2fd52343f3c486e6ce93b332d5b
+size 605211