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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 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.")
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