Delete pages/predict page.py

pages/predict page.py

@@ -1,65 +0,0 @@
-# app.py (your Streamlit file)
-import streamlit as st
-import numpy as np
-import pickle
-from sklearn.metrics import accuracy_score, f1_score, roc_auc_score
-import pandas as pd
-
-st.title("🧠 Sleep Event Prediction")
-
-# --- Load Pickles ---
-@st.cache_resource
-def load_all():
-    with open("model.pkl", "rb") as f: model = pickle.load(f)
-    with open("scaler.pkl", "rb") as f: scaler = pickle.load(f)
-    with open("label_encoder.pkl", "rb") as f: le = pickle.load(f)
-    with open("X_test.pkl", "rb") as f: X_test = pickle.load(f)
-    with open("y_test.pkl", "rb") as f: y_test = pickle.load(f)
-    return model, scaler, le, X_test, y_test
-
-model, scaler, le, X_test, y_test = load_all()
-
-# --- Predict and Evaluate ---
-y_pred = model.predict(X_test)
-y_proba = model.predict_proba(X_test)
-
-accuracy = accuracy_score(y_test, y_pred)
-f1 = f1_score(y_test, y_pred, average='macro')
-
-if y_proba.shape[1] == 2:
-    roc = roc_auc_score(y_test, y_proba[:, 1])
-else:
-    roc = roc_auc_score(y_test, y_proba, multi_class='ovo', average='macro')
-
-# --- Display Metrics ---
-# st.subheader("Model Performance")
-# st.metric("Accuracy", f"{accuracy:.4f}")
-# st.metric("F1 Score", f"{f1:.4f}")
-# st.metric("ROC AUC Score", f"{roc:.4f}")
-
-# Create a DataFrame for metrics
-import pandas as pd
-
-st.subheader("Model Performance")
-
-# Create a DataFrame for metrics
-metrics_df = pd.DataFrame({
-    "Metric": ["Accuracy", "F1 Score", "ROC AUC Score"],
-    "Value": [f"{accuracy:.4f}", f"{f1:.4f}", f"{roc:.4f}"]
-})
-
-# Display as table
-st.table(metrics_df)
-
-
-# --- Predict User Input ---
-st.subheader("Predict Sleep Event")
-anglez = st.number_input("Enter anglez:", value=27.8800)
-enmo = st.number_input("Enter enmo:", value=0.0000)
-
-if st.button("Predict Sleep Event"):
-    input_data = np.array([[anglez, enmo]])
-    input_scaled = scaler.transform(input_data)
-    prediction = model.predict(input_scaled)[0]
-    label = le.inverse_transform([prediction])[0]
-    st.success(f"Predicted Event: {label}")