import numpy as np
import pandas as pd
import streamlit as st

USERNAME = "admin"
PASSWORD = "password"

st.title("Admin Panel")

# Login Form
login_success = False
with st.form("login_form"):
    st.write("Please login to access the admin dashboard:")
    username = st.text_input("Username")
    password = st.text_input("Password", type="password")
    login_button = st.form_submit_button("Login")

    if login_button:
        if username == USERNAME and password == PASSWORD:
            login_success = True
            st.success("Login successful!")
        else:
            st.error("Invalid username or password.")

# After successful login
if login_success:
    # Display information about model performance
    st.header("Model Performance Metrics")

    model_r2_score = 0.85  # Mock R^2 Score
    avg_prediction_time = 0.15  # Mock Average Prediction Time in seconds
    num_predictions_made = 2000  # Mock Number of Predictions Made

    st.metric(label="R² Score", value=f"{model_r2_score:.2f}")
    st.metric(
        label="Average Prediction Time", value=f"{avg_prediction_time:.2f} seconds"
    )
    st.metric(label="Total Predictions Made", value=num_predictions_made)

    st.subheader("Detailed Metrics")
    detailed_metrics = pd.DataFrame(
        {
            "Metric": ["MAE", "MSE", "RMSE", "Training Time"],
            "Value": [2.5, 3.4, 1.8, "1.2 hours"],
        }
    )
    st.table(detailed_metrics)

    # Mocking prediction latency over time (example chart)
    st.subheader("Prediction Latency Over Time")
    latency_data = pd.DataFrame(
        {
            "Date": pd.date_range(end=pd.Timestamp.today(), periods=7).to_list(),
            "Prediction Time (s)": np.random.uniform(0.1, 0.5, 7),
        }
    )
    st.line_chart(latency_data.set_index("Date"))

    # Button to simulate refreshing metrics
    if st.button("Refresh Metrics"):
        st.experimental_rerun()
else:
    st.warning("Please login to access the admin panel.")