Delete water_index_quality.py

water_index_quality.py

@@ -1,92 +0,0 @@
-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")
-
-def run():
-    # === 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.")
-