import streamlit as st
import pandas as pd
from prophet import Prophet
import plotly.graph_objects as go

# Title of the app
st.title("CSV Uploader and Prediction with Prophet")

# Default dataset
default_data = {
    "date": [
        "2022-01-01", "2022-01-02", "2022-01-03", "2022-01-04",
        "2022-01-05", "2022-01-06", "2022-01-07", "2022-01-08",
        "2022-01-09", "2022-01-10", "2022-01-11", "2022-01-12",
        "2022-01-13", "2022-01-14", "2022-01-15"
    ],
    "y": [100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240],
    "temperature": [30, 31, 29, 32, 35, 33, 31, 30, 29, 28, 30, 32, 34, 35, 36],
    "humidity": [70, 65, 60, 75, 80, 68, 72, 74, 69, 71, 66, 72, 70, 73, 75],
    "wind_speed": [5, 7, 6, 4, 8, 5, 6, 4, 5, 6, 7, 8, 6, 5, 7]
}

# Convert the default data to a DataFrame
default_df = pd.DataFrame(default_data)
default_df['date'] = pd.to_datetime(default_df['date'])  # Convert 'date' to datetime

# Upload CSV file
uploaded_file = st.file_uploader("Upload your CSV file", type="csv")

# Use the uploaded file or the default DataFrame
if uploaded_file is not None:
    # Read the CSV file into a DataFrame
    data = pd.read_csv(uploaded_file)
    st.write("Data Preview:")
    st.dataframe(data.head())
else:
    data = default_df
    st.write("Using Default Dataset:")
    st.dataframe(data)

# Select date column
date_column = st.selectbox("Select date column", data.columns)

# Select target variable
target_variable = st.selectbox("Select target variable (y)", data.columns[data.columns != date_column])

# Feature selection
features = data.columns.tolist()
if date_column in features:
    features.remove(date_column)  # Remove date column if it exists

selected_features = st.multiselect("Select features for analysis", features)

# Specify forecast period in months
forecast_period_months = st.number_input("Enter forecast period in months", min_value=1, value=6)

# Check if the necessary columns exist
if target_variable and date_column in data.columns:
    # Prepare data for Prophet
    train_data = data[[date_column, target_variable]].rename(columns={target_variable: 'y', date_column: 'ds'})

    # Fit the Prophet model
    model = Prophet()
    model.fit(train_data)

    # Make future predictions for the specified number of months
    future = model.make_future_dataframe(periods=forecast_period_months, freq='M')  # Predicting specified months into the future
    forecast = model.predict(future)

    # Display the forecast
    st.write("Forecast:")
    st.dataframe(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])

    # Plot using Plotly
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecasted Values'))
    fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', name='Lower Bound', fill='tonexty'))
    fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', name='Upper Bound', fill='tonexty'))

    fig.update_layout(
        title='Forecasted Data',
        xaxis_title='Date',
        yaxis_title='Predicted Values',
        legend_title='Legend',
        template='plotly_white'
    )
    
    st.plotly_chart(fig)

    # Plot selected features if any
    if selected_features:
        st.write("Selected Features Plot:")
        for feature in selected_features:
            if feature in data.columns:
                fig_feature = go.Figure()
                fig_feature.add_trace(go.Scatter(x=data[date_column], y=data[feature], mode='lines', name=feature))
                fig_feature.update_layout(
                    title=f'{feature} over Time',
                    xaxis_title='Date',
                    yaxis_title=feature,
                    template='plotly_white'
                )
                st.plotly_chart(fig_feature)

else:
    st.error("Please ensure the target variable is selected and the uploaded CSV contains the selected date column.")