gapp.py

import os
import logging
from io import StringIO
from pathlib import Path
from collections import OrderedDict

import plotly.express as px
import gradio as gr
import pandas as pd
from dotenv import load_dotenv
# from PIL import Image
import matplotlib.pyplot as plt
# import cv2
# import numpy as np
import plotly.graph_objects as go
import networkx as nx

from model import dfg2networkx, discover_process_map, discover_process_map_activities_connections, discover_process_map_variants, view_process_map

load_dotenv()

logger = logging.getLogger(__name__) 
logger.setLevel(logging.DEBUG)


def get_data(temp_file, case_col, activity_col, timestamp_col, state: dict):
    # print(f"temp_file: {temp_file}")
    if isinstance(temp_file, str):
    #   df = pd.read_csv(StringIO(temp_file), parse_dates=[ "Start", "Finish"])
        df = pd.read_csv(temp_file, sep=';|,')
    else:
        # df = pd.read_csv(temp_file.name, ) # parse_dates=[ "Start", "Finish"]
        df = pd.read_csv(temp_file.name, sep=';|,')
    # logger.debug(df.head())
    # logger.debug(df.dtypes)
    state['df'] = df
    return df, \
        gr.Dropdown( choices=list(df.columns), multiselect=False, label="Case", info="選擇 Case ID"),  \
        gr.Dropdown( choices=list(df.columns), multiselect=False, label="Activity", info="選擇 Activity ID"),  \
        gr.Dropdown( choices=list(df.columns), multiselect=False, label="Timestamp", info="選擇 Timestamp"), \
        state


def get_stats(state: dict):
    df = state.get('df', pd.DataFrame()).copy()
    summary = pd.DataFrame({
        "metric": ["資料筆數", "Case 數量", "Activity 數量", "起始時間", "結束時間"],
        "value": [ df.shape[0], df['case_id'].nunique(), df['activity'].nunique(), df['timestamp'].min(), df['timestamp'].max() ]
    })
    case_stats = df.groupby(
        by = ['case_id'], as_index=False
    ).agg(count = ('activity', len)).reset_index()
    logger.debug(f"case stats: {case_stats}")

    case_lead_time = df.groupby(
        by = ['case_id'], as_index=False
    ).agg( duration = ('timestamp', lambda x: (x.max() - x.min()).total_seconds()//3600 )).reset_index()

    def avg_duration(x):
        return pd.Series({ "avg_duration": (x.timestamp.max() - x.timestamp.min()).total_seconds()//3600})
    case_avg_duration = df.groupby(
        by = ['case_id'], as_index=False
    ).apply( 
        avg_duration
    )

    logger.debug(f"case lead time: {case_lead_time}")
    return (
        summary,
        gr.BarPlot( case_stats, x="case_id", y="count", title="Case Stats", tooltip = ["case_id", "count"], width=None),
        gr.BarPlot( case_lead_time, x="case_id", y="duration", title="Case Lead Time", tooltip = ["case_id", "duration"], width=None),
        gr.BarPlot( case_avg_duration, x="case_id", y="avg_duration", title="Case Average Duration", tooltip = ["case_id", "avg_duration"], width=None),
        state
    )


def get_process_map( state: dict = {}):
    df = state.get('df', pd.DataFrame()).copy()
    net, img  = discover_process_map( df, type='petrinet')
    return img, state

def get_process_map_variants( top_k: int = 1, state: dict = {}):
    """
    """
    df = state.get('df', pd.DataFrame()).copy()
    dfg, start_activities, end_activities = discover_process_map_variants( df, top_k, type='dfg')
    top_variant_connections = OrderedDict(sorted(dfg.items(), key=lambda item: item[1], reverse=True))
    state['top_variant_connections'] = top_variant_connections
    if 'top_variant' not in state and top_k == 1:
        state['top_variant'] = {'dfg': dfg, 'start_activities': start_activities, 'end_activities': end_activities}
    nx_graph = dfg2networkx( dfg, start_activities, end_activities)
    chart = view_process_map( nx_graph, process_type='dfg', layout_type='sfdp')
    return chart, state


def get_process_map_activities_connections( activity_rank: int = 0, connection_rank: int = 0, state: dict = {}):
    """
    """
    df = state.get('df', pd.DataFrame()).copy()
    dfg, start_activities, end_activities = discover_process_map_activities_connections( df, activity_rank = activity_rank, connection_rank = connection_rank, state = state)
    nx_graph = dfg2networkx( dfg, start_activities, end_activities)
    chart = view_process_map( nx_graph, process_type='dfg', layout_type='sfdp')
    return chart, state


def etl( case_col, activity_col, timestamp_col, state: dict):
    """
    Argument
    Return
    """
    df = state['df'].copy()
    df.loc[:, case_col] = df[case_col].astype(str)
    df.loc[:, activity_col] = df[activity_col].astype(str)
    df.loc[:, timestamp_col] = pd.to_datetime(df[timestamp_col]) # format='%Y-%m-%d %H:%M:%S'
    df.rename(columns={case_col: 'case_id', activity_col: 'activity', timestamp_col: 'timestamp'}, inplace=True)
    state['df'] = df
    return df, state


## --- block --- ##
css = """
h1 {
    text-align: center;
    display:block;
}
"""
demo = gr.Blocks(css = css)
with demo:
    gr.Markdown("# 🌟 Process Discovery 🌟")
    state = gr.State(value={})
    with gr.Row():
        upl_btn = gr.UploadButton(label="Upload", file_types = ['.csv'], file_count = "single")
    with gr.Accordion('Data Preview'): 
        df = gr.Dataframe()

    with gr.Row():
        case_col = gr.Dropdown( multiselect=False, label="Case", info="選擇 Case ID")
        activity_col = gr.Dropdown( multiselect=False, label="Activity", info="選擇 Activity ID")
        timestamp_col = gr.Dropdown( multiselect=False, label="Timestamp", info="選擇 Timestamp")

    upl_btn.upload( fn=get_data, inputs = [upl_btn, case_col, activity_col, timestamp_col, state], outputs=[df, case_col, activity_col, timestamp_col, state])

    column_btn = gr.Button("Select Columns")
    column_btn.click( fn=etl, inputs = [ case_col, activity_col, timestamp_col, state], outputs=[df, state])

    with gr.Row():
        with gr.Tab('Data Explorer'):
            # outputs.append(gr.Dataframe( label="Event logs"))
            de_btn = gr.Button("Get Stats")
            with gr.Row():
                summary = gr.Dataframe( label="Summary", interactive=False, height=300)
                chart1 = gr.BarPlot( label="Case Stats")
                chart2 = gr.BarPlot( label="Case Lead Time Stats")
                chart3 = gr.BarPlot( label="Case Average Activity Time Stats")
            de_btn.click( fn=get_stats, inputs = [state], outputs=[ summary, chart1, chart2, chart3, state])

        with gr.Tab('Variant Explorer'):
            ve_btn = gr.Button("Get Variants")
            top_k_variant_selector = gr.Slider(0, 10, value=1, step=1, label="Top-K", info="選擇 Variant 數量（0: 全選）")
            pmchart = gr.Plot( label="Process Map")
            ve_btn.click( fn=get_process_map_variants, inputs = [ top_k_variant_selector, state], outputs=[ pmchart, state])

        # with gr.Tab('Process Explorer'):
        #     pe_btn = gr.Button("Get Activities & Connections") 
        #     with gr.Column():    
        #         top_k_activity_selector = gr.Slider(0, 10, value=1, step=1, label="Activity", info="【pending】增減 Top Activity 數量（0: 全選）")
        #         top_k_connection_selector = gr.Slider(0, 10, value=1, step=1, label="Connection", info="增減 Top Connection 數量（0: 全選）")
        #     pmchart = gr.Plot( label="Process Map")
        #     pe_btn.click( fn=get_process_map_activities_connections, inputs = [ top_k_activity_selector, top_k_connection_selector, state], outputs=[ pmchart, state])

        # with gr.Tab('Process Model'):
        #     cc_btn = gr.Button("Get Process Model")
        #     img = gr.Image( label="Process Model")
        #     cc_btn.click( fn=get_process_map, inputs = [state], outputs=[ img, state])


if __name__ == "__main__":
    
    demo.launch( 
        # share=True, 
        server_name="0.0.0.0",
        server_port=int(os.environ.get("PORT")),
        auth=( os.environ.get("USER_NAME"), os.environ.get("PASSWORD"))
    )