analytics.py

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
from typing import Dict, Optional, List

from config import get_chart_theme, DESIGN_SYSTEM, get_translation

@st.cache_data
def get_material_stats(df: pd.DataFrame) -> Dict:
    stats = {}
    total = df['weight_kg'].sum()
    total_work_days = df['date'].nunique()
    
    for material in df['material_type'].unique():
        data = df[df['material_type'] == material]
        work_days = data['date'].nunique()
        daily_avg = data.groupby('date')['weight_kg'].sum().mean()
        
        stats[material] = {
            'total': data['weight_kg'].sum(),
            'percentage': (data['weight_kg'].sum() / total) * 100,
            'daily_avg': daily_avg,
            'work_days': work_days,
            'records': len(data)
        }
    
    stats['_total_'] = {
        'total': total,
        'percentage': 100.0,
        'daily_avg': df.groupby('date')['weight_kg'].sum().mean(),
        'work_days': total_work_days,
        'records': len(df)
    }
    
    return stats

@st.cache_data
def detect_outliers(df: pd.DataFrame) -> Dict:
    outliers = {}
    
    for material in df['material_type'].unique():
        material_data = df[df['material_type'] == material]
        data = material_data['weight_kg']
        
        Q1, Q3 = data.quantile(0.25), data.quantile(0.75)
        IQR = Q3 - Q1
        lower, upper = Q1 - 1.5 * IQR, Q3 + 1.5 * IQR
        
        outlier_mask = (data < lower) | (data > upper)
        outlier_dates = material_data[outlier_mask]['date'].dt.strftime('%Y-%m-%d').tolist()
        
        outliers[material] = {
            'count': len(outlier_dates),
            'range': f"{lower:.0f} - {upper:.0f} kg",
            'dates': outlier_dates
        }
    
    return outliers

def create_total_production_chart(df: pd.DataFrame, time_period: str = 'daily', lang: str = 'English'):
    t = get_translation(lang)
    
    if time_period == 'daily':
        grouped = df.groupby('date')['weight_kg'].sum().reset_index()
        fig = px.line(grouped, x='date', y='weight_kg', 
                     title=t.get('chart_total_production', 'Total Production Trend'),
                     labels={'weight_kg': t.get('label_weight', 'Weight (kg)'), 'date': t.get('label_date', 'Date')})
    elif time_period == 'weekly':
        df_copy = df.copy()
        df_copy['week'] = df_copy['date'].dt.isocalendar().week
        df_copy['year'] = df_copy['date'].dt.year
        grouped = df_copy.groupby(['year', 'week'])['weight_kg'].sum().reset_index()
        grouped['week_label'] = grouped['year'].astype(str) + '-W' + grouped['week'].astype(str)
        fig = px.bar(grouped, x='week_label', y='weight_kg',
                    title=t.get('chart_total_production_weekly', 'Total Production Trend (Weekly)'),
                    labels={'weight_kg': t.get('label_weight', 'Weight (kg)'), 'week_label': t.get('label_week', 'Week')})
    else:
        df_copy = df.copy()
        df_copy['month'] = df_copy['date'].dt.to_period('M')
        grouped = df_copy.groupby('month')['weight_kg'].sum().reset_index()
        grouped['month'] = grouped['month'].astype(str)
        fig = px.bar(grouped, x='month', y='weight_kg',
                    title=t.get('chart_total_production_monthly', 'Total Production Trend (Monthly)'),
                    labels={'weight_kg': t.get('label_weight', 'Weight (kg)'), 'month': t.get('label_month', 'Month')})
    
    fig.update_layout(**get_chart_theme()['layout'], height=400, showlegend=False)
    return fig

def create_materials_trend_chart(df: pd.DataFrame, time_period: str = 'daily', 
                                selected_materials: Optional[List[str]] = None, lang: str = 'English'):
    df_copy = df.copy()
    t = get_translation(lang)
    
    if selected_materials:
        df_copy = df_copy[df_copy['material_type'].isin(selected_materials)]
    
    if time_period == 'daily':
        grouped = df_copy.groupby(['date', 'material_type'])['weight_kg'].sum().reset_index()
        fig = px.line(grouped, x='date', y='weight_kg', color='material_type',
                     title=t.get('chart_materials_trends', 'Materials Production Trends'),
                     labels={'weight_kg': t.get('label_weight', 'Weight (kg)'), 
                             'date': t.get('label_date', 'Date'), 
                             'material_type': t.get('label_material', 'Material')})
    elif time_period == 'weekly':
        df_copy['week'] = df_copy['date'].dt.isocalendar().week
        df_copy['year'] = df_copy['date'].dt.year
        grouped = df_copy.groupby(['year', 'week', 'material_type'])['weight_kg'].sum().reset_index()
        grouped['week_label'] = grouped['year'].astype(str) + '-W' + grouped['week'].astype(str)
        fig = px.bar(grouped, x='week_label', y='weight_kg', color='material_type',
                    title=t.get('chart_materials_trends_weekly', 'Materials Production Trends (Weekly)'),
                    labels={'weight_kg': t.get('label_weight', 'Weight (kg)'), 
                            'week_label': t.get('label_week', 'Week'), 
                            'material_type': t.get('label_material', 'Material')})
    else:
        df_copy['month'] = df_copy['date'].dt.to_period('M')
        grouped = df_copy.groupby(['month', 'material_type'])['weight_kg'].sum().reset_index()
        grouped['month'] = grouped['month'].astype(str)
        fig = px.bar(grouped, x='month', y='weight_kg', color='material_type',
                    title=t.get('chart_materials_trends_monthly', 'Materials Production Trends (Monthly)'),
                    labels={'weight_kg': t.get('label_weight', 'Weight (kg)'), 
                            'month': t.get('label_month', 'Month'), 
                            'material_type': t.get('label_material', 'Material')})
    
    fig.update_layout(**get_chart_theme()['layout'], height=400)
    return fig

def create_shift_trend_chart(df: pd.DataFrame, time_period: str = 'daily', lang: str = 'English'):
    theme = get_chart_theme()
    t = get_translation(lang)
    
    if time_period == 'daily':
        grouped = df.groupby(['date', 'shift'])['weight_kg'].sum().reset_index()
        pivot_data = grouped.pivot(index='date', columns='shift', values='weight_kg').fillna(0)
        
        fig = go.Figure()
        
        if 'day' in pivot_data.columns:
            fig.add_trace(go.Bar(
                x=pivot_data.index, 
                y=pivot_data['day'], 
                name=t.get('label_day_shift', 'Day Shift'),
                marker_color=DESIGN_SYSTEM['colors']['warning'],
                text=pivot_data['day'].round(0), 
                textposition='inside'
            ))
        
        if 'night' in pivot_data.columns:
            fig.add_trace(go.Bar(
                x=pivot_data.index, 
                y=pivot_data['night'], 
                name=t.get('label_night_shift', 'Night Shift'),
                marker_color=DESIGN_SYSTEM['colors']['primary'],
                base=pivot_data['day'] if 'day' in pivot_data.columns else 0,
                text=pivot_data['night'].round(0), 
                textposition='inside'
            ))
        
        fig.update_layout(
            **theme['layout'],
            title=t.get('chart_shift_trends', 'Daily Shift Production Trends (Stacked)'),
            xaxis_title=t.get('label_date', 'Date'), 
            yaxis_title=t.get('label_weight', 'Weight (kg)'),
            barmode='stack', 
            height=400, 
            showlegend=True
        )
    else:
        grouped = df.groupby(['date', 'shift'])['weight_kg'].sum().reset_index()
        fig = px.bar(grouped, x='date', y='weight_kg', color='shift',
                    title=t.get('chart_shift_trends_period', f'{time_period.title()} Shift Production Trends'),
                    barmode='stack')
        fig.update_layout(**theme['layout'], height=400)
    
    return fig