initial commit

.gitattributes

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.gitignore

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+# Byte-compiled / optimized / DLL files
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+*.py[cod]
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+
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+
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+
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+
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+creds/
+

README.md

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+---
+title: Process Mining
+emoji: 🤗
+colorFrom: yellow
+colorTo: green
+python_version: '3.10'
+sdk: gradio
+sdk_version: 4.37.2
+app_file: sapp.py
+pinned: false
+---

app.py

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+import dash
+from dash import dcc, html
+from dash.dependencies import Input, Output
+import plotly.graph_objects as go
+import networkx as nx
+
+# Create a directed graph
+G = nx.DiGraph()
+
+# Add nodes
+G.add_nodes_from([1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+# Add directed edges
+G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 5), (1, 6), (4,7), (4,8),(5,7), (5,8), (7,9), (8,9), (6,9), (6,6)])
+
+# Initialize the Dash app
+app = dash.Dash(__name__)
+
+app.layout = html.Div([
+    dcc.Dropdown(
+        id='node-dropdown',
+        options=[{'label': f'Node {i}', 'value': i} for i in G.nodes],
+        value=None,
+        placeholder="Select a node to filter"
+    ),
+    dcc.Graph(id='network-graph')
+])
+
+@app.callback(
+    Output('network-graph', 'figure'),
+    Input('node-dropdown', 'value')
+)
+def update_graph(selected_node):
+    if selected_node is not None:
+        nodes_to_filter = [selected_node]
+    else:
+        nodes_to_filter = []
+
+    filtered_graph = filter_nodes(G, nodes_to_filter)
+
+    pos = nx.spring_layout(filtered_graph)
+
+    node_trace = go.Scatter(
+        x=[pos[n][0] for n in filtered_graph.nodes],
+        y=[pos[n][1] for n in filtered_graph.nodes],
+        text=list(filtered_graph.nodes),
+        mode='markers+text',
+        textposition='top center',
+        marker=dict(size=20, color='LightSkyBlue', line=dict(width=2))
+    )
+
+    edge_trace = go.Scatter(
+        x=(),
+        y=(),
+        line=dict(width=1.5, color='Gray'),
+        hoverinfo='none',
+        mode='lines'
+    )
+
+    annotations = []
+    for edge in filtered_graph.edges:
+        x0, y0 = pos[edge[0]]
+        x1, y1 = pos[edge[1]]
+        edge_trace['x'] += (x0, x1, None)
+        edge_trace['y'] += (y0, y1, None)
+
+        annotations.append(
+            dict(
+                ax=x0,
+                ay=y0,
+                axref='x',
+                ayref='y',
+                x=x1,
+                y=y1,
+                xref='x',
+                yref='y',
+                showarrow=True,
+                arrowhead=2,
+                arrowsize=1,
+                arrowwidth=2,
+                arrowcolor='Gray'
+            )
+        )
+
+    fig = go.Figure(data=[edge_trace, node_trace],
+                    layout=go.Layout(
+                        showlegend=False,
+                        hovermode='closest',
+                        margin=dict(b=0, l=0, r=0, t=0),
+                        annotations=annotations,
+                        xaxis=dict(showgrid=False, zeroline=False),
+                        yaxis=dict(showgrid=False, zeroline=False)
+                    ))
+
+    return fig
+
+def filter_nodes(graph, nodes_to_remove):
+    filtered_graph = graph.copy()
+    for node in nodes_to_remove:
+        if node in filtered_graph:
+            filtered_graph.remove_node(node)
+    return filtered_graph
+
+if __name__ == '__main__':
+    app.run_server(debug=True, port=8050, host='0.0.0.0')
+

gapp.py

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+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, 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=';', dtype={'case_id': str}, parse_dates = ['timestamp'])
+        df.loc[:, 'timestamp'] = pd.to_datetime(df['timestamp']) # format='%Y-%m-%d %H:%M:%S'
+    else:
+        # df = pd.read_csv(temp_file.name, ) # parse_dates=[ "Start", "Finish"]
+        df = pd.read_csv(temp_file.name, sep=';', dtype={'case_id': str}, parse_dates = ['timestamp'])
+        df.loc[:, 'timestamp'] = pd.to_datetime(df['timestamp'])
+    # logger.debug(df.head())
+    # logger.debug(df.dtypes)
+    state['df'] = df
+    return df, 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
+
+
+## --- block --- ##
+css = """
+h1 {
+    text-align: center;
+    display:block;
+}
+"""
+demo = gr.Blocks(css = css)
+with demo:
+    gr.Markdown("# 🌟 Process Dicovery 🌟")
+    state = gr.State(value={})
+    with gr.Row():
+        upl_btn = gr.UploadButton(label="Upload", file_types = ['.csv'], file_count = "single")
+    # with gr.Row('Data Preview'):
+    with gr.Accordion('Data Preview'): 
+        df = gr.Dataframe()
+    upl_btn.upload( fn=get_data, inputs = [upl_btn, 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"))
+    )
+

model.py

@@ -0,0 +1,216 @@
+import os
+from typing import List, Optional, Tuple, Any
+from collections import OrderedDict
+
+import pandas as pd
+from loguru import logger
+import pm4py
+import plotly.graph_objects as go
+import networkx as nx
+import matplotlib.pyplot as plt
+from PIL import Image
+from pydantic import BaseModel
+
+
+class ProcessMap(BaseModel):
+    net: Any
+    start_activities: List | None
+    end_activities: List | None
+    img: Any | None
+
+
+def dfg2networkx( dfg, start, end):
+    """Dfg to networkx 
+    Argument
+        dfg: a list of dict of edges from directly-follow-graph
+        start: a dict of start activities
+        end: a dict of end activities
+    Return
+        nx: networkx graph object
+    """
+    PROCESS_START = '#Start#'
+    PROCESS_END = '#End#'
+    nodes = { PROCESS_START: 0, PROCESS_END: 1}
+    node_idx = 2
+    for activity in start:
+        assert activity not in nodes, f"#ERROR: {activity} exists"
+        nodes[activity] = node_idx
+        node_idx += 1
+    for activity in end:
+        assert activity not in nodes, f"#ERROR: {activity} exists"
+        nodes[activity] = node_idx
+        node_idx += 1
+    for node in dfg:
+        left_activity = node[0]
+        if left_activity not in nodes:
+            nodes[left_activity] = node_idx
+            node_idx +=1 
+        right_activity = node[1]
+        if right_activity not in nodes:
+            nodes[right_activity] = node_idx
+            node_idx +=1 
+    nodes = list(nodes.keys())
+    
+    edges = []
+    for activity in start:
+        from_id = str(PROCESS_START)
+        to_id = str(activity)
+        edges.append( ( PROCESS_START, activity) ) 
+    for activity in end:
+        from_id = str(activity)
+        to_id = str(PROCESS_END)
+        edges.append( ( activity, PROCESS_END) ) 
+    for transition in dfg:
+        edges.append( ( transition[0], transition[1]) ) 
+    nx_graph = nx.DiGraph()
+    nx_graph.add_nodes_from( nodes)
+    nx_graph.add_edges_from(edges)
+    return nx_graph
+
+
+def discover_process_map_variants( df, top_k: int = 0, type: str = 'dfg'):
+    """Discover process map from data frame (raw event log)
+    Argument
+        df: a pandas dataframe
+        top_k: top k variants
+        type: dfg or petri
+    Return
+        dfg, start_activities, end_activities
+    """
+    event_log = pm4py.format_dataframe( df, case_id='case_id', activity_key='activity', timestamp_key='timestamp')
+    if top_k > 0:
+        event_log = pm4py.filter_variants_top_k( event_log, k = top_k)
+    dfg, start_activities, end_activities = pm4py.discover_dfg(event_log)
+    pm4py.view_dfg(dfg, start_activities=start_activities, end_activities=end_activities)
+    return dfg, start_activities, end_activities
+
+
+def discover_process_map_activities_connections( df, activity_rank: int = 0, connection_rank: int = 0, state: dict = {}, type: str = 'dfg'):
+    """Discover process map from data frame (raw event log)
+    Argument
+        df: a pandas dataframe
+        top_k: top k variants
+        type: dfg or petri
+    Return
+        dfg, start_activities, end_activities
+    """
+    event_log = pm4py.format_dataframe( df, case_id='case_id', activity_key='activity', timestamp_key='timestamp')
+    full_dfg, _, __ = pm4py.discover_dfg(event_log)
+    ranked_connections = OrderedDict(sorted(full_dfg.items(), key=lambda item: item[1], reverse=True))
+
+    if activity_rank > 0:
+        pass
+    if connection_rank > 0:
+        top_variant_connections = state.get('top_variant_connections', [])
+        filtered_connections = list(ranked_connections.keys())[ : (connection_rank+ len(ranked_connections))]
+    else:
+        filtered_connections = list(ranked_connections.keys())
+    event_log = pm4py.filter_directly_follows_relation( event_log, relations = filtered_connections)
+    dfg, start_activities, end_activities = pm4py.discover_dfg(event_log)
+    pm4py.view_dfg(dfg, start_activities=start_activities, end_activities=end_activities)
+    return dfg, start_activities, end_activities
+
+
+def discover_process_map( df: pd.DataFrame, type: str = 'dfg'):
+    """
+    """
+    event_log = pm4py.format_dataframe( df, case_id='case_id', activity_key='activity', timestamp_key='timestamp')
+    if type=='dfg':
+        dfg, start_activities, end_activities = pm4py.discover_dfg(event_log)
+        pm4py.view_dfg(dfg, start_activities=start_activities, end_activities=end_activities)
+        return dfg, start_activities, end_activities
+    elif type=='petrinet':
+        net, im, fm = pm4py.discover_petri_net_inductive(event_log)
+        pm4py.view_petri_net( petri_net=net, initial_marking=im, final_marking=fm)
+        file_path = 'output/petri_net.png'
+        pm4py.save_vis_petri_net( net, im, fm, file_path)
+        img = Image.open(file_path)
+        return net, img
+    elif type=='bpmn':
+        net = pm4py.discover_bpmn_inductive(event_log)
+        pm4py.view_bpmn(net, format='png')
+        file_path = 'output/bpmn.png'
+        pm4py.save_vis_bpmn( net, file_path)
+        img = Image.open(file_path)
+        return net, img
+    else:
+        raise Exception(f"Invalid type: {type}")
+
+
+def view_networkx( nx_graph, layout):
+    """
+    Argument
+        nx_graph
+    Return 
+        graph object
+    fig.update_xaxes(showticklabels=False)
+    fig.update_yaxes(showticklabels=False)
+    """
+    # Create node scatter plot
+    node_trace = go.Scatter(
+        x=[layout[n][0] for n in nx_graph.nodes],
+        y=[layout[n][1] for n in nx_graph.nodes],
+        text=list(nx_graph.nodes),
+        mode='markers+text',
+        hovertext = [n for n in nx_graph.nodes],
+        textposition='top center',
+        marker=dict(size=5, color='LightSkyBlue', line=dict(width=2))
+    )
+    
+    # Create edge lines
+    edge_trace = go.Scatter(
+        x=(),
+        y=(),
+        line=dict(width=1.5, color='#888'),
+        hoverinfo='none',
+        mode='lines'
+    )
+    
+    # Add arrows for directed edges
+    annotations = []
+    for edge in nx_graph.edges:
+        x0, y0 = layout[edge[0]]
+        x1, y1 = layout[edge[1]]
+        edge_trace['x'] += (x0, x1, None)
+        edge_trace['y'] += (y0, y1, None)
+    
+        # Calculate direction of the arrow
+        annotations.append(
+            dict(
+                ax=x0,
+                ay=y0,
+                axref='x',
+                ayref='y',
+                x=x1,
+                y=y1,
+                xref='x',
+                yref='y',
+                showarrow=True,
+                arrowhead=2,
+                arrowsize=1,
+                arrowwidth=2,
+                arrowcolor='Gray'
+            )
+        )
+    
+    # Draw the figure
+    fig = go.Figure(data=[edge_trace, node_trace],
+        layout=go.Layout( 
+        showlegend=False,
+        hovermode='closest',
+        margin=dict(b=0, l=0, r=0, t=0),
+        annotations=annotations,
+        xaxis=dict(showgrid=False, zeroline=False),
+        yaxis=dict(showgrid=False, zeroline=False)
+    ))
+    fig = fig.update_xaxes(showticklabels=False)
+    fig = fig.update_yaxes(showticklabels=False)
+    return fig
+
+
+def view_process_map( nx_graph, process_type: str = 'dfg', layout_type: str = 'sfdp'):
+    """
+    """
+    layout = nx.nx_agraph.graphviz_layout( nx_graph, prog=layout_type)
+    fig = view_networkx(nx_graph, layout)
+    return fig

packages.txt

@@ -0,0 +1,2 @@
+graphviz
+graphviz-dev

requirements.txt

@@ -0,0 +1,176 @@
+aiofiles==23.2.1
+altair==5.3.0
+annotated-types==0.7.0
+anyio==4.4.0
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asttokens==2.4.1
+async-lru==2.0.4
+attrs==23.2.0
+Babel==2.15.0
+beautifulsoup4==4.12.3
+bleach==6.1.0
+blinker==1.8.2
+certifi==2024.6.2
+cffi==1.16.0
+charset-normalizer==3.3.2
+click==8.1.7
+comm==0.2.2
+contourpy==1.2.1
+cvxopt==1.3.2
+cycler==0.12.1
+dash==2.17.1
+dash-core-components==2.0.0
+dash-html-components==2.0.0
+dash-table==5.0.0
+debugpy==1.8.1
+decorator==5.1.1
+defusedxml==0.7.1
+deprecation==2.1.0
+dnspython==2.6.1
+docopt==0.6.2
+email_validator==2.1.1
+exceptiongroup==1.2.1
+executing==2.0.1
+extratools==0.8.2.1
+fastapi==0.111.0
+fastapi-cli==0.0.4
+fastjsonschema==2.20.0
+ffmpy==0.3.2
+filelock==3.15.1
+Flask==3.0.3
+fonttools==4.53.0
+fqdn==1.5.1
+fsspec==2024.6.0
+gradio==4.36.1
+gradio_client==1.0.1
+graphviz==0.20.3
+h11==0.14.0
+httpcore==1.0.5
+httptools==0.6.1
+httpx==0.27.0
+huggingface-hub==0.23.4
+idna==3.7
+importlib_metadata==7.1.0
+importlib_resources==6.4.0
+intervaltree==3.1.0
+ipykernel==6.29.4
+ipython==8.25.0
+isoduration==20.11.0
+itsdangerous==2.2.0
+jedi==0.19.1
+Jinja2==3.1.4
+joblib==1.4.2
+json5==0.9.25
+jsonpointer==3.0.0
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+jupyter-events==0.10.0
+jupyter-lsp==2.2.5
+jupyter_client==8.6.2
+jupyter_core==5.7.2
+jupyter_server==2.14.1
+jupyter_server_terminals==0.5.3
+jupyterlab==4.2.2
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.27.2
+kiwisolver==1.4.5
+loguru==0.7.2
+lxml==5.2.2
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.0
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+mistune==3.0.2
+nbclient==0.10.0
+nbconvert==7.16.4
+nbformat==5.10.4
+nest-asyncio==1.6.0
+networkx==3.3
+notebook==7.2.1
+notebook_shim==0.2.4
+numpy==1.26.4
+orjson==3.10.5
+overrides==7.7.0
+packaging==24.1
+pandas==2.2.2
+pandocfilters==1.5.1
+parso==0.8.4
+pexpect==4.9.0
+pillow==10.3.0
+platformdirs==4.2.2
+plotly==5.22.0
+# pm4py==2.7.11.11
+-e ./pm4py
+prefixspan==0.5.2
+prettytable==3.10.0
+prometheus_client==0.20.0
+prompt_toolkit==3.0.47
+psutil
+ptyprocess
+pure-eval==0.2.2
+pycparser==2.22
+pydantic==2.7.4
+pydantic_core==2.18.4
+pydotplus==2.0.2
+pydub==0.25.1
+pyecharts==2.0.6
+Pygments==2.18.0
+pygraphviz==1.13
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-json-logger==2.0.7
+python-multipart==0.0.9
+pytz==2024.1
+PyYAML==6.0.1
+pyzmq==26.0.3
+referencing==0.35.1
+requests==2.32.3
+retrying==1.3.4
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rich==13.7.1
+rpds-py==0.18.1
+ruff==0.4.9
+scikit-learn==1.5.0
+scipy==1.13.1
+semantic-version==2.10.0
+Send2Trash==1.8.3
+shellingham==1.5.4
+simplejson==3.19.2
+six==1.16.0
+sniffio==1.3.1
+sortedcontainers==2.4.0
+soupsieve==2.5
+stack-data==0.6.3
+starlette==0.37.2
+tenacity==8.3.0
+terminado==0.18.1
+threadpoolctl==3.5.0
+tinycss2==1.3.0
+tomli==2.0.1
+tomlkit==0.12.0
+toolz==0.12.1
+tornado==6.4.1
+tqdm==4.66.4
+traitlets==5.14.3
+typer==0.12.3
+types-python-dateutil==2.9.0.20240316
+typing_extensions==4.12.2
+tzdata==2024.1
+ujson==5.10.0
+uri-template==1.3.0
+urllib3==2.2.1
+uvicorn==0.30.1
+uvloop==0.19.0
+watchfiles==0.22.0
+wcwidth==0.2.13
+webcolors==24.6.0
+webencodings==0.5.1
+websocket-client==1.8.0
+websockets==11.0.3
+Werkzeug==3.0.3
+zipp==3.19.2

sapp.py

@@ -0,0 +1,24 @@
+import os
+import importlib
+
+import gradio as gr
+
+def greet(name):
+    parent_name = get_process_name()
+    return f"Hello {name}!! a greeting from {parent_name}"
+
+def get_process_name():
+    if importlib.util.find_spec("psutil"):
+        import psutil
+    parent_pid = os.getppid()
+    try:
+        parent_name = str(psutil.Process(parent_pid).name())
+        return parent_name
+    except psutil.NoSuchProcess:  # Catch the error caused by the process no longer existing
+        print("NoSuchProcess")
+        return "Uknown Process"
+    
+
+if __name__ == "__main__":
+    iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+    iface.launch()