src/agri_predict/plotting.py

import streamlit as st
import pandas as pd
import plotly.graph_objects as go
import io
import calendar
import numpy as np


def plot_data(original_df, future_df, last_date, model, days):
    actual_last_df = original_df[original_df['Reported Date'] > (last_date - pd.Timedelta(days=days))]
    predicted_plot_df = actual_last_df[['Reported Date']].copy()
    predicted_plot_df['Modal Price (Rs./Quintal)'] = model.predict(
        actual_last_df.drop(columns=['Modal Price (Rs./Quintal)', 'Reported Date'], errors='ignore'))
    predicted_plot_df['Type'] = 'Actual'

    future_plot_df = future_df[['Reported Date', 'Modal Price (Rs./Quintal)']].copy()
    future_plot_df['Type'] = 'Forecasted'
    last_actual_point = predicted_plot_df.iloc[[-1]].copy()
    last_actual_point['Type'] = 'Forecasted'
    future_plot_df = pd.concat([last_actual_point, future_plot_df])
    plot_df = pd.concat([predicted_plot_df, future_plot_df])

    fig = go.Figure()
    for plot_type, color, dash in [('Actual', 'blue', 'solid'), ('Forecasted', 'red', 'dash')]:
        data = plot_df[plot_df['Type'] == plot_type]
        fig.add_trace(go.Scatter(x=data['Reported Date'], y=data['Modal Price (Rs./Quintal)'], mode='lines', name=f"{plot_type} Data", line=dict(color=color, dash=dash)))
    fig.update_layout(title="Actual vs Forecasted Modal Price (Rs./Quintal)", xaxis_title="Date", yaxis_title="Modal Price (Rs./Quintal)", template="plotly_white")
    st.plotly_chart(fig, use_container_width=True)


def download_button(future_df: pd.DataFrame, key: str):
    download_df = future_df[['Reported Date', 'Modal Price (Rs./Quintal)']].copy()
    download_df['Reported Date'] = pd.to_datetime(download_df['Reported Date']).dt.strftime('%Y-%m-%d')
    towrite = io.BytesIO()
    with pd.ExcelWriter(towrite, engine='xlsxwriter') as writer:
        download_df.to_excel(writer, index=False, sheet_name='Forecast')
    towrite.seek(0)
    st.download_button(label="Download Forecast as Excel", data=towrite, file_name=f"forecast_{key}.xlsx")


def display_statistics(df):
    st.title("📊 National Market Statistics Dashboard")
    st.markdown("""
        <style>
            h1 {
                color: #2e7d32;
                font-size: 36px;
                font-weight: bold;
            }
            h3 {
                color: #388e3c;
                font-size: 28px;
                font-weight: 600;
            }
            p {
                font-size: 16px;
                line-height: 1.6;
            }
            .highlight {
                background-color: #f1f8e9;
                padding: 10px;
                border-radius: 8px;
                font-size: 16px;
                color: #2e7d32;
                font-weight: 500;
            }
        </style>
    """, unsafe_allow_html=True)

    # Ensure 'Reported Date' is in datetime format
    df['Reported Date'] = pd.to_datetime(df['Reported Date'])
    national_data = df.groupby('Reported Date').agg({
        'Modal Price (Rs./Quintal)': 'mean',
        'Arrivals (Tonnes)': 'sum'
    }).reset_index()

    st.subheader("🗓️ Key Statistics")
    latest_date = national_data['Reported Date'].max()
    latest_price = national_data[national_data['Reported Date'] == latest_date]['Modal Price (Rs./Quintal)'].mean()
    latest_arrivals = national_data[national_data['Reported Date'] == latest_date]['Arrivals (Tonnes)'].sum()

    st.markdown("<p class='highlight'>This section provides the most recent statistics for the market. It includes the latest available date, the average price of commodities, and the total quantity of goods arriving at the market. These metrics offer an up-to-date snapshot of market conditions.</p>", unsafe_allow_html=True)
    st.write(f"**Latest Date**: {latest_date.strftime('%Y-%m-%d')}")
    st.write(f"**Latest Modal Price**: {latest_price:.2f} Rs./Quintal")
    st.write(f"**Latest Arrivals**: {latest_arrivals:.2f} Tonnes")

    st.subheader("📆 This Day in Previous Years")
    st.markdown("<p class='highlight'>This table shows the modal price and total arrivals for this exact day across previous years. It provides a historical perspective to compare against current market conditions. This section examines historical data for the same day in previous years. By analyzing trends for this specific day, you can identify seasonal patterns, supply-demand changes, or any deviations that might warrant closer attention.</p>", unsafe_allow_html=True)
    today = latest_date
    previous_years_data = national_data[national_data['Reported Date'].dt.dayofyear == today.dayofyear].copy()

    if not previous_years_data.empty:
        previous_years_data['Year'] = previous_years_data['Reported Date'].dt.year.astype(str)
        display_data = (previous_years_data[['Year', 'Modal Price (Rs./Quintal)', 'Arrivals (Tonnes)']]
                        .sort_values(by='Year', ascending=False)
                        .reset_index(drop=True))
        st.table(display_data)
    else:
        st.write("No historical data available for this day in previous years.")

    st.subheader("📅 Monthly Averages Over Years")
    st.markdown("<p class='highlight'>This section displays the average modal prices and arrivals for each month across all years. It helps identify seasonal trends and peak activity months, which can be crucial for inventory planning and market predictions.</p>", unsafe_allow_html=True)
    national_data['Month'] = national_data['Reported Date'].dt.month
    monthly_avg_price = national_data.groupby('Month')['Modal Price (Rs./Quintal)'].mean().reset_index()
    monthly_avg_arrivals = national_data.groupby('Month')['Arrivals (Tonnes)'].mean().reset_index()
    monthly_avg = pd.merge(monthly_avg_price, monthly_avg_arrivals, on='Month')
    monthly_avg['Month'] = monthly_avg['Month'].apply(lambda x: calendar.month_name[x])
    monthly_avg.columns = ['Month', 'Average Modal Price (Rs./Quintal)', 'Average Arrivals (Tonnes)']
    st.write(monthly_avg)
    
    st.subheader("📆 Yearly Averages")
    st.markdown("<p class='highlight'>Yearly averages provide insights into long-term trends in pricing and arrivals. By examining these values, you can detect overall growth, stability, or volatility in the market.</p>", unsafe_allow_html=True)
    national_data['Year'] = national_data['Reported Date'].dt.year
    yearly_avg_price = national_data.groupby('Year')['Modal Price (Rs./Quintal)'].mean().reset_index()
    yearly_sum_arrivals = national_data.groupby('Year')['Arrivals (Tonnes)'].sum().reset_index()
    yearly_avg = pd.merge(yearly_avg_price, yearly_sum_arrivals, on='Year')
    yearly_avg['Year'] = yearly_avg['Year'].apply(lambda x: f"{int(x)}")
    yearly_avg.columns = ['Year', 'Average Modal Price (Rs./Quintal)', 'Average Arrivals (Tonnes)']
    st.write(yearly_avg)

    st.subheader("📈 Largest Daily Price Changes (Past Year)")
    st.markdown("<p class='highlight'>This analysis identifies the most significant daily price changes in the past year. These fluctuations can highlight periods of market volatility, potentially caused by external factors like weather, policy changes, or supply chain disruptions.</p>", unsafe_allow_html=True)
    one_year_ago = latest_date - pd.DateOffset(years=1)
    recent_data = national_data[national_data['Reported Date'] >= one_year_ago].copy()
    recent_data['Daily Change (%)'] = recent_data['Modal Price (Rs./Quintal)'].pct_change() * 100
    largest_changes = recent_data[['Reported Date', 'Modal Price (Rs./Quintal)', 'Daily Change (%)']].nlargest(5, 'Daily Change (%)')
    largest_changes['Reported Date'] = largest_changes['Reported Date'].dt.date
    largest_changes = largest_changes.reset_index(drop=True)
    st.write(largest_changes)

    st.subheader("🏆 Top 5 Highest and Lowest Prices (Past Year)")
    st.markdown("<p class='highlight'>This section highlights the highest and lowest prices over the past year. These values reflect the extremes of market dynamics, helping to understand price ceilings and floors in the recent period.</p>", unsafe_allow_html=True)
    highest_prices = recent_data.nlargest(5, 'Modal Price (Rs./Quintal)')[['Reported Date', 'Modal Price (Rs./Quintal)']]
    lowest_prices = recent_data.nsmallest(5, 'Modal Price (Rs./Quintal)')[['Reported Date', 'Modal Price (Rs./Quintal)']]
    highest_prices['Reported Date'] = highest_prices['Reported Date'].dt.date
    lowest_prices['Reported Date'] = lowest_prices['Reported Date'].dt.date
    highest_prices = highest_prices.reset_index(drop=True)
    lowest_prices = lowest_prices.reset_index(drop=True)
    st.write("**Top 5 Highest Prices**")
    st.write(highest_prices)
    st.write("**Top 5 Lowest Prices**")
    st.write(lowest_prices)

    st.subheader("🗂️ Data Snapshot")
    st.markdown("<p class='highlight'>This snapshot provides a concise overview of the latest data, including rolling averages and lagged values. These metrics help identify short-term trends and lagged effects in pricing.</p>", unsafe_allow_html=True)
    national_data['Rolling Mean (14 Days)'] = national_data['Modal Price (Rs./Quintal)'].rolling(window=14).mean()
    national_data['Lag (14 Days)'] = national_data['Modal Price (Rs./Quintal)'].shift(14)
    national_data['Reported Date'] = national_data['Reported Date'].dt.date
    national_data = national_data.sort_values(by='Reported Date', ascending=False)
    st.dataframe(national_data.head(14).reset_index(drop=True), use_container_width=True, height=525)

    editable_spreadsheet()


def editable_spreadsheet():
    st.title("Sowing Report Prediction Model")

    # Excel file uploader
    uploaded_file = st.file_uploader("Upload your Excel file", type=['xlsx'])

    # Check if an Excel file is uploaded
    if uploaded_file is not None:
        # Read the Excel file
        df_excel = pd.read_excel(uploaded_file)

        # Display the DataFrame from the Excel file
        st.write("Excel data loaded:", df_excel)

        # Form for inputting filtering options and area for calculation
        with st.form("input_form"):
            input_region = st.text_input("Enter Region to Filter By", placeholder="Region Name")
            input_season = st.text_input("Enter Season to Filter By", placeholder="Season (e.g., Winter)")
            input_area = st.number_input("Enter Area (in hectares) for Production Calculation", min_value=0.0, format="%.2f")
            submit_button = st.form_submit_button("Calculate Production")

        if submit_button:
            if input_region and input_season and input_area > 0:
                # Filter data by the region and season specified
                filtered_df = df_excel[
                    (df_excel['Region'].str.lower() == input_region.lower()) &
                    (df_excel['Season'].str.lower() == input_season.lower())
                    ]

                if not filtered_df.empty:
                    process_dataframe(filtered_df, input_area)
                else:
                    st.error("No data found for the specified region and season.")
            else:
                st.error("Please enter valid region, season, and area to proceed.")


def process_dataframe(df, area):
    if 'Yield' in df.columns:
        average_yield = df['Yield'].mean()
        predicted_production = average_yield * area
        st.success(f"The predicted Production Volume for the specified region and season is: {predicted_production:.2f} units")
    else:
        st.error("The DataFrame does not contain a necessary 'Yield' column for calculation.")