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|
""" |
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SAP Finance Dashboard with RPT-1-OSS Model |
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|
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Main Mesop application with multiple pages: |
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- Dashboard: Overview with metrics and charts |
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- Data Explorer: Browse datasets |
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- Upload: Upload custom datasets |
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- Predictions: AI-powered predictions using SAP-RPT-1-OSS |
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- OData: Connect to SAP OData services |
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""" |
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|
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|
import os |
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import mesop as me |
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import pandas as pd |
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import numpy as np |
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from pathlib import Path |
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import json |
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import base64 |
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from typing import Optional, Dict, Any |
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import plotly.graph_objects as go |
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import plotly.io as pio |
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from utils.data_generator import generate_all_datasets |
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from utils.visualizations import ( |
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create_revenue_expense_chart, |
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create_balance_sheet_chart, |
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create_gl_summary_chart, |
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create_sales_analytics_chart, |
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create_sales_trend_chart, |
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get_summary_metrics |
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) |
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from utils.odata_connector import SAPFinanceConnector |
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from models.rpt_model import RPTModelWrapper, create_model |
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from dataclasses import field |
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|
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@me.stateclass |
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class State: |
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gl_data: pd.DataFrame = field(default_factory=pd.DataFrame) |
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financial_data: pd.DataFrame = field(default_factory=pd.DataFrame) |
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sales_data: pd.DataFrame = field(default_factory=pd.DataFrame) |
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uploaded_data: pd.DataFrame = field(default_factory=pd.DataFrame) |
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current_dataset_type: str = "" |
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odata_connector: Optional[SAPFinanceConnector] = None |
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odata_connected: bool = False |
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odata_data: pd.DataFrame = field(default_factory=pd.DataFrame) |
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model_wrapper: Optional[RPTModelWrapper] = None |
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predictions: Optional[np.ndarray] = None |
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prediction_proba: Optional[np.ndarray] = None |
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connection_message: str = "" |
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fetch_message: str = "" |
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model_initialized: bool = False |
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model_trained: bool = False |
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def load_datasets(state: State): |
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"""Load synthetic datasets if they exist.""" |
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data_dir = Path("data") |
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|
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if (data_dir / "synthetic_gl_accounts.csv").exists(): |
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state.gl_data = pd.read_csv(data_dir / "synthetic_gl_accounts.csv") |
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|
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if (data_dir / "synthetic_financial_statements.csv").exists(): |
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state.financial_data = pd.read_csv(data_dir / "synthetic_financial_statements.csv") |
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if (data_dir / "synthetic_sales_orders.csv").exists(): |
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state.sales_data = pd.read_csv(data_dir / "synthetic_sales_orders.csv") |
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|
|
|
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def plotly_to_html(fig_dict: Dict[str, Any]) -> str: |
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"""Convert Plotly figure dict to HTML string.""" |
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if not fig_dict: |
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return "<p>No chart data available</p>" |
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try: |
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fig = go.Figure(fig_dict) |
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html_str = pio.to_html(fig, include_plotlyjs='cdn', div_id="plotly-div") |
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return html_str |
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except Exception as e: |
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return f"<p>Error rendering chart: {str(e)}</p>" |
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@me.page(path="/", title="SAP Finance Dashboard") |
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def dashboard_page(): |
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"""Main dashboard page with overview metrics and charts.""" |
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state = me.state(State) |
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me.text("SAP Finance Dashboard", style=me.Style(font_size=32, font_weight="bold", margin=me.Margin(bottom=16))) |
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if state.gl_data.empty and state.financial_data.empty and state.sales_data.empty: |
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load_datasets(state) |
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if state.gl_data.empty or state.financial_data.empty or state.sales_data.empty: |
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with me.box(style=me.Style(padding=16, background="#fff3cd", border_radius=8, margin=me.Margin(bottom=16))): |
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me.text("Generating synthetic datasets...", style=me.Style(color="#856404")) |
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generate_all_datasets() |
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load_datasets(state) |
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with me.box(style=me.Style(display="grid", grid_template_columns="repeat(4, 1fr)", gap=16, margin=me.Margin(bottom=24))): |
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if not state.gl_data.empty: |
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gl_metrics = get_summary_metrics(state.gl_data, "gl") |
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with me.box(style=me.Style(padding=16, background="#f8f9fa", border_radius=8)): |
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me.text("GL Transactions", style=me.Style(font_weight="bold")) |
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me.text(f"{gl_metrics.get('Total Transactions', 0):,}") |
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if not state.financial_data.empty: |
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fin_metrics = get_summary_metrics(state.financial_data, "financial") |
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with me.box(style=me.Style(padding=16, background="#f8f9fa", border_radius=8)): |
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me.text("Latest Revenue", style=me.Style(font_weight="bold")) |
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me.text(f"${fin_metrics.get('Latest Revenue', 0):,.2f}") |
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if not state.sales_data.empty: |
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sales_metrics = get_summary_metrics(state.sales_data, "sales") |
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with me.box(style=me.Style(padding=16, background="#f8f9fa", border_radius=8)): |
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me.text("Total Sales", style=me.Style(font_weight="bold")) |
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me.text(f"${sales_metrics.get('Total Sales', 0):,.2f}") |
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with me.box(style=me.Style(padding=16, background="#f8f9fa", border_radius=8)): |
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me.text("Datasets", style=me.Style(font_weight="bold")) |
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count = sum([ |
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not state.gl_data.empty, |
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not state.financial_data.empty, |
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not state.sales_data.empty, |
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not state.uploaded_data.empty |
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]) |
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me.text(f"{count} loaded") |
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if not state.financial_data.empty: |
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with me.box(style=me.Style(margin=me.Margin(bottom=24))): |
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me.text("Financial Trends", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(bottom=8))) |
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chart_data = create_revenue_expense_chart(state.financial_data) |
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if chart_data: |
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html_chart = plotly_to_html(chart_data) |
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me.html(html_chart) |
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if not state.financial_data.empty: |
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with me.box(style=me.Style(margin=me.Margin(bottom=24))): |
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me.text("Balance Sheet", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(bottom=8))) |
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chart_data = create_balance_sheet_chart(state.financial_data) |
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if chart_data: |
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html_chart = plotly_to_html(chart_data) |
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me.html(html_chart) |
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if not state.sales_data.empty: |
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with me.box(style=me.Style(margin=me.Margin(bottom=24))): |
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me.text("Sales Analytics", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(bottom=8))) |
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chart_data = create_sales_analytics_chart(state.sales_data) |
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if chart_data: |
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html_chart = plotly_to_html(chart_data) |
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me.html(html_chart) |
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|
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@me.page(path="/explorer", title="Data Explorer") |
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def explorer_page(): |
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"""Data explorer page to browse and filter datasets.""" |
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state = me.state(State) |
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me.text("Data Explorer", style=me.Style(font_size=32, font_weight="bold", margin=me.Margin(bottom=16))) |
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with me.box(style=me.Style(margin=me.Margin(bottom=16))): |
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me.text("Select Dataset:", style=me.Style(font_weight="bold", margin=me.Margin(bottom=8))) |
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dataset_options = [ |
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("GL Accounts", "gl"), |
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("Financial Statements", "financial"), |
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("Sales Orders", "sales"), |
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("Uploaded Data", "uploaded") |
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] |
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me.select( |
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label="Dataset", |
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options=[me.SelectOption(label=label, value=value) for label, value in dataset_options], |
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on_selection_change=on_dataset_selection_change, |
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value=state.current_dataset_type |
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) |
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if state.current_dataset_type: |
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display_dataset(state, state.current_dataset_type) |
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def on_dataset_selection_change(e: me.SelectSelectionChangeEvent): |
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"""Handle dataset selection change.""" |
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|
state = me.state(State) |
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state.current_dataset_type = e.value |
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def display_dataset(state: State, dataset_type: str): |
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"""Display the selected dataset.""" |
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if dataset_type == "gl" and not state.gl_data.empty: |
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df = state.gl_data |
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|
me.text(f"GL Accounts ({len(df)} records)", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(bottom=8))) |
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|
chart_data = create_gl_summary_chart(df) |
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|
if chart_data: |
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html_chart = plotly_to_html(chart_data) |
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me.html(html_chart) |
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me.table(data=df.head(100).to_dict("records"), style=me.Style(margin=me.Margin(top=16))) |
|
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elif dataset_type == "financial" and not state.financial_data.empty: |
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df = state.financial_data |
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me.text(f"Financial Statements ({len(df)} records)", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(bottom=8))) |
|
|
chart_data = create_revenue_expense_chart(df) |
|
|
if chart_data: |
|
|
html_chart = plotly_to_html(chart_data) |
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|
me.html(html_chart) |
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me.table(data=df.to_dict("records"), style=me.Style(margin=me.Margin(top=16))) |
|
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|
|
|
elif dataset_type == "sales" and not state.sales_data.empty: |
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|
df = state.sales_data |
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me.text(f"Sales Orders ({len(df)} records)", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(bottom=8))) |
|
|
chart_data = create_sales_trend_chart(df) |
|
|
if chart_data: |
|
|
html_chart = plotly_to_html(chart_data) |
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|
me.html(html_chart) |
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|
me.table(data=df.head(100).to_dict("records"), style=me.Style(margin=me.Margin(top=16))) |
|
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|
|
|
elif dataset_type == "uploaded" and not state.uploaded_data.empty: |
|
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df = state.uploaded_data |
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me.text(f"Uploaded Data ({len(df)} records)", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(bottom=8))) |
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me.table(data=df.head(100).to_dict("records"), style=me.Style(margin=me.Margin(top=16))) |
|
|
|
|
|
else: |
|
|
me.text("No data available for this dataset type.", style=me.Style(color="#dc3545")) |
|
|
|
|
|
|
|
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@me.page(path="/upload", title="Upload Data") |
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|
def upload_page(): |
|
|
"""Upload page for custom datasets.""" |
|
|
state = me.state(State) |
|
|
|
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|
me.text("Upload Dataset", style=me.Style(font_size=32, font_weight="bold", margin=me.Margin(bottom=16))) |
|
|
|
|
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with me.box(style=me.Style(margin=me.Margin(bottom=16))): |
|
|
me.text("Upload a CSV file to analyze:", style=me.Style(margin=me.Margin(bottom=8))) |
|
|
me.file_upload( |
|
|
label="Choose CSV File", |
|
|
accept=".csv", |
|
|
on_upload=handle_file_upload |
|
|
) |
|
|
|
|
|
if not state.uploaded_data.empty: |
|
|
me.text("Uploaded Data Preview:", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(top=16, bottom=8))) |
|
|
me.table(data=state.uploaded_data.head(50).to_dict("records")) |
|
|
|
|
|
|
|
|
def handle_file_upload(e: me.UploadEvent): |
|
|
"""Handle file upload.""" |
|
|
state = me.state(State) |
|
|
try: |
|
|
if e.files: |
|
|
file = e.files[0] |
|
|
df = pd.read_csv(file.getvalue()) |
|
|
state.uploaded_data = df |
|
|
except Exception as ex: |
|
|
pass |
|
|
|
|
|
|
|
|
@me.page(path="/predictions", title="Predictions") |
|
|
def predictions_page(): |
|
|
"""Predictions page using SAP-RPT-1-OSS model.""" |
|
|
state = me.state(State) |
|
|
|
|
|
me.text("AI Predictions with SAP-RPT-1-OSS", style=me.Style(font_size=32, font_weight="bold", margin=me.Margin(bottom=16))) |
|
|
|
|
|
with me.box(style=me.Style(margin=me.Margin(bottom=16))): |
|
|
me.text("Model Configuration", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(bottom=8))) |
|
|
|
|
|
me.select( |
|
|
label="Model Type", |
|
|
options=[ |
|
|
me.SelectOption(label="Classifier", value="classifier"), |
|
|
me.SelectOption(label="Regressor", value="regressor") |
|
|
] |
|
|
) |
|
|
|
|
|
me.checkbox(label="Use GPU (requires 80GB memory)", checked=False) |
|
|
|
|
|
me.button("Initialize Model", on_click=on_init_model) |
|
|
|
|
|
if state.model_initialized: |
|
|
me.text("Model initialized successfully!", style=me.Style(color="#28a745", margin=me.Margin(bottom=16))) |
|
|
|
|
|
|
|
|
with me.box(style=me.Style(margin=me.Margin(bottom=16))): |
|
|
me.text("Select Training Data", style=me.Style(font_weight="bold", margin=me.Margin(bottom=8))) |
|
|
dataset_options = [ |
|
|
("GL Accounts", "gl"), |
|
|
("Financial Statements", "financial"), |
|
|
("Sales Orders", "sales"), |
|
|
("Uploaded Data", "uploaded") |
|
|
] |
|
|
|
|
|
me.select( |
|
|
label="Dataset", |
|
|
options=[me.SelectOption(label=label, value=value) for label, value in dataset_options] |
|
|
) |
|
|
|
|
|
me.button("Train Model", on_click=on_train_model) |
|
|
|
|
|
if state.model_trained: |
|
|
me.text("Model trained successfully!", style=me.Style(color="#28a745", margin=me.Margin(top=16))) |
|
|
|
|
|
if state.predictions is not None: |
|
|
me.text("Predictions:", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(top=16, bottom=8))) |
|
|
me.text(str(state.predictions[:10])) |
|
|
|
|
|
else: |
|
|
with me.box(style=me.Style(padding=16, background="#fff3cd", border_radius=8)): |
|
|
me.text("Please initialize the model first.", style=me.Style(color="#856404")) |
|
|
|
|
|
|
|
|
def on_init_model(e: me.ClickEvent): |
|
|
"""Initialize the model.""" |
|
|
state = me.state(State) |
|
|
try: |
|
|
state.model_wrapper = create_model(model_type="classifier", use_gpu=False) |
|
|
state.model_initialized = True |
|
|
except Exception as ex: |
|
|
state.connection_message = f"Error initializing model: {str(ex)}" |
|
|
|
|
|
|
|
|
def on_train_model(e: me.ClickEvent): |
|
|
"""Train the model.""" |
|
|
state = me.state(State) |
|
|
try: |
|
|
if state.model_wrapper and not state.gl_data.empty: |
|
|
|
|
|
X = state.gl_data.select_dtypes(include=[np.number]).dropna() |
|
|
if len(X) > 0: |
|
|
|
|
|
y = (X.iloc[:, 0] > X.iloc[:, 0].median()).astype(int) |
|
|
state.model_wrapper.fit(X, y) |
|
|
state.model_trained = True |
|
|
except Exception as ex: |
|
|
state.connection_message = f"Error training model: {str(ex)}" |
|
|
|
|
|
|
|
|
@me.page(path="/odata", title="OData Connection") |
|
|
def odata_page(): |
|
|
"""OData connection page for SAP data.""" |
|
|
state = me.state(State) |
|
|
|
|
|
me.text("SAP OData Connection", style=me.Style(font_size=32, font_weight="bold", margin=me.Margin(bottom=16))) |
|
|
|
|
|
|
|
|
with me.box(style=me.Style(margin=me.Margin(bottom=16))): |
|
|
if state.odata_connector is None: |
|
|
state.odata_connector = SAPFinanceConnector() |
|
|
|
|
|
me.button("Test Connection", on_click=on_test_odata_connection) |
|
|
|
|
|
if state.connection_message: |
|
|
color = "#28a745" if state.odata_connected else "#dc3545" |
|
|
me.text(state.connection_message, style=me.Style(color=color, margin=me.Margin(top=8))) |
|
|
elif state.odata_connected: |
|
|
me.text("✓ Connected to SAP OData", style=me.Style(color="#28a745", margin=me.Margin(top=8))) |
|
|
else: |
|
|
me.text("Not connected", style=me.Style(color="#dc3545", margin=me.Margin(top=8))) |
|
|
|
|
|
|
|
|
if state.odata_connected: |
|
|
with me.box(style=me.Style(margin=me.Margin(bottom=16))): |
|
|
me.text("Fetch Data", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(bottom=8))) |
|
|
|
|
|
top_count = me.number_input(label="Number of records", value=100, min_value=1, max_value=1000) |
|
|
|
|
|
with me.box(style=me.Style(display="grid", grid_template_columns="repeat(2, 1fr)", gap=8, margin=me.Margin(top=8))): |
|
|
me.button("Fetch Sales Orders", on_click=lambda e: on_fetch_odata(e, "orders", top_count)) |
|
|
me.button("Fetch Products", on_click=lambda e: on_fetch_odata(e, "products", top_count)) |
|
|
me.button("Fetch Line Items", on_click=lambda e: on_fetch_odata(e, "line_items", top_count)) |
|
|
me.button("Fetch Partners", on_click=lambda e: on_fetch_odata(e, "partners", top_count)) |
|
|
|
|
|
if state.fetch_message: |
|
|
me.text(state.fetch_message, style=me.Style(color="#28a745", margin=me.Margin(top=8))) |
|
|
|
|
|
|
|
|
if not state.odata_data.empty: |
|
|
me.text("Fetched Data:", style=me.Style(font_size=20, font_weight="bold", margin=me.Margin(top=16, bottom=8))) |
|
|
me.table(data=state.odata_data.head(100).to_dict("records")) |
|
|
|
|
|
|
|
|
def on_test_odata_connection(e: me.ClickEvent): |
|
|
"""Test OData connection.""" |
|
|
state = me.state(State) |
|
|
try: |
|
|
if state.odata_connector is None: |
|
|
state.odata_connector = SAPFinanceConnector() |
|
|
|
|
|
connected, message = state.odata_connector.test_connection() |
|
|
state.odata_connected = connected |
|
|
state.connection_message = message |
|
|
except Exception as ex: |
|
|
state.connection_message = f"Error: {str(ex)}" |
|
|
state.odata_connected = False |
|
|
|
|
|
|
|
|
def on_fetch_odata(e: me.ClickEvent, entity_type: str, top: int): |
|
|
"""Fetch data from OData service.""" |
|
|
state = me.state(State) |
|
|
try: |
|
|
if not state.odata_connected: |
|
|
state.fetch_message = "Please connect first!" |
|
|
return |
|
|
|
|
|
if entity_type == "orders": |
|
|
state.odata_data = state.odata_connector.fetch_orders_df(top) |
|
|
elif entity_type == "products": |
|
|
state.odata_data = state.odata_connector.fetch_products_df(top) |
|
|
elif entity_type == "line_items": |
|
|
state.odata_data = state.odata_connector.fetch_line_items_df(top) |
|
|
elif entity_type == "partners": |
|
|
state.odata_data = state.odata_connector.fetch_partners_df(top) |
|
|
|
|
|
state.fetch_message = f"Fetched {len(state.odata_data)} records" |
|
|
except Exception as ex: |
|
|
state.fetch_message = f"Error fetching data: {str(ex)}" |
|
|
|
|
|
|
|
|
|
|
|
@me.page(path="/nav", title="Navigation") |
|
|
def nav_page(): |
|
|
"""Navigation page.""" |
|
|
me.text("Navigation", style=me.Style(font_size=32, font_weight="bold", margin=me.Margin(bottom=16))) |
|
|
|
|
|
nav_links = [ |
|
|
("Dashboard", "/"), |
|
|
("Data Explorer", "/explorer"), |
|
|
("Upload", "/upload"), |
|
|
("Predictions", "/predictions"), |
|
|
("OData", "/odata") |
|
|
] |
|
|
|
|
|
for label, path in nav_links: |
|
|
with me.box(style=me.Style(margin=me.Margin(bottom=8))): |
|
|
me.link(label, path=path, style=me.Style(font_size=18, text_decoration="none")) |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
|
|
|
data_dir = Path("data") |
|
|
if not (data_dir / "synthetic_gl_accounts.csv").exists(): |
|
|
generate_all_datasets() |
|
|
|
|
|
me.run() |
|
|
|
|
|
|
