import streamlit as st
import pandas as pd

# Load dummy CSV data
df = pd.read_csv("wind_data.csv")

# Page title
st.title("ReneWind – Wind Turbine Energy Predictor")

st.write("### 📊 Sample Turbine Data")
st.dataframe(df)

st.write("### 🔢 Predict Energy Output for New Input")

# User inputs
wind_speed = st.number_input("Wind Speed (m/s)", min_value=0.0, max_value=50.0, value=12.0)
temperature = st.number_input("Temperature (°C)", min_value=-20.0, max_value=50.0, value=25.0)
turbine_age = st.number_input("Turbine Age (Years)", min_value=0, max_value=30, value=5)
last_maintenance_days = st.number_input("Days Since Last Maintenance", min_value=0, max_value=365, value=90)

# Dummy prediction logic
def predict_energy_output(wind_speed, temperature, turbine_age, last_maintenance_days):
    prediction = (wind_speed * 1.2) - (temperature * 0.5) - (turbine_age * 0.3) - (last_maintenance_days * 0.1)
    return max(0, prediction)

if st.button("Predict Energy Output (kW)"):
    output = predict_energy_output(wind_speed, temperature, turbine_age, last_maintenance_days)
    st.success(f"Estimated Energy Output: {output:.2f} kW")