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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from pathlib import Path | |
| def search_data(sku, group_name, date): | |
| if(sku == '' and group_name == '' and date != ''): | |
| filtered_df = initial_df[initial_df['Date'] == date] | |
| elif(sku == '' and group_name != '' and date == ''): | |
| filtered_df = initial_df[initial_df['Group Name'] == group_name] | |
| elif(sku != '' and group_name == '' and date == ''): | |
| filtered_df = initial_df[initial_df['Current Job'] == sku] | |
| elif(sku == '' and group_name != '' and date != ''): | |
| filtered_df = initial_df[(initial_df['Group Name'] == group_name) & (initial_df['Date'] == date)] | |
| elif(sku != '' and group_name == '' and date != ''): | |
| filtered_df = initial_df[(initial_df['Current Job'] == sku) & (initial_df['Date'] == date)] | |
| elif(sku != '' and group_name != '' and date == ''): | |
| filtered_df = initial_df[(initial_df['Current Job'] == sku) & (initial_df['Group Name'] == group_name)] | |
| else: | |
| filtered_df = initial_df[(initial_df['Current Job'] == sku) & (initial_df['Group Name'] == group_name) & (initial_df['Date'] == date)] | |
| records = len(filtered_df) | |
| compliant_records = len(filtered_df[(filtered_df['Units Per Hour'] >= filtered_df['Lower Target']) & (filtered_df['Units Per Hour'] <= filtered_df['Upper Target'])]) | |
| compliance_percentage = round((compliant_records / records),2) * 100 if records != 0 else 0 | |
| avg_units_per_hour = round(filtered_df['Units Per Hour'].mean(),2) | |
| stdev_units_per_hour = round(filtered_df['Units Per Hour'].std(),2) | |
| most_common_value = filtered_df['Description'].value_counts().idxmax() | |
| fig1 = plt.figure() | |
| plt.hist(filtered_df['Units Per Hour'], bins=20, color='skyblue', edgecolor='black') | |
| plt.xlabel('Units Per Hour') | |
| plt.ylabel('Frequency') | |
| plt.grid(True) | |
| return most_common_value, records, compliance_percentage, avg_units_per_hour, stdev_units_per_hour, fig1 | |
| def upload_file(filepath): | |
| global initial_df | |
| name = Path(filepath).name | |
| initial_df = prepare_df(filepath) | |
| initial_analysis_output = initial_analysis(initial_df) | |
| return initial_analysis_output | |
| def initial_analysis(df): | |
| compliant_df = df[(df['Units Per Hour'] >= df['Lower Target']) & (df['Units Per Hour'] <= df['Upper Target'])] | |
| non_compliant_df = df[(df['Units Per Hour'] < df['Lower Target']) | (df['Units Per Hour'] > df['Upper Target'])] | |
| total_observations = len(df) | |
| pct_compliant = 100 * round(len(compliant_df)/total_observations,2) | |
| pct_non_compliant = 100 * round(len(non_compliant_df)/total_observations,2) | |
| return total_observations, pct_compliant, pct_non_compliant | |
| def prepare_df(file_path): | |
| df = pd.read_excel(file_path) | |
| df['Current Job'] = df['Current Job'].astype(str) | |
| df['Start of Batch Date/Time'] = pd.to_datetime(df['Start of Batch Date/Time']) | |
| df['End of Batch Date/Time'] = pd.to_datetime(df['End of Batch Date/Time']) | |
| df['Batch Length (Hours)'] = (df['End of Batch Date/Time'] - df['Start of Batch Date/Time']).dt.total_seconds() / 3600 | |
| df['Units Per Hour'] = df['Batch Count'] / df['Batch Length (Hours)'] | |
| df.dropna(subset=['Optimal Cases Per Hour'], inplace=True) | |
| return df | |
| with gr.Blocks() as tab_search_by_sku: | |
| with gr.Row(): | |
| sku = gr.Textbox(label="SKU") | |
| group_name = gr.Textbox(label="Group Name") | |
| date = gr.Textbox(label="Date(yyyy-mm-dd)") | |
| with gr.Row(): | |
| description = gr.Textbox(label="Description") | |
| with gr.Row(): | |
| records = gr.Textbox(label="Records") | |
| compliance = gr.Textbox(label="Compliance %") | |
| avgunits = gr.Textbox(label="Avg Units/Hr") | |
| stddev = gr.Textbox(label="Std Dev Units/Hr") | |
| with gr.Row(): | |
| graph = gr.Plot(label="Units Per Hour Distribution") | |
| with gr.Row(): | |
| submit_btn = gr.Button('Submit') | |
| submit_btn.click( | |
| fn=search_data, | |
| inputs=[sku, group_name, date], | |
| outputs=[description, records, compliance, avgunits, stddev, graph] | |
| ) | |
| # clear_btn = gr.Button('Clear') | |
| # clear_btn.click( | |
| # fn=lambda: ("", "", "", "", "", plt.figure()), | |
| # inputs=[], | |
| # outputs=[description, records, compliance, avgunits, stddev, graph] | |
| # ) | |
| tab_files = gr.Interface( | |
| fn = upload_file, | |
| inputs = [ | |
| gr.File(label="Upload Template File", file_types=[".xlsx"], file_count="single") | |
| ], | |
| outputs = [ | |
| gr.Textbox(label="Total Observations", interactive=False), | |
| gr.Textbox(label="Compliant Observations %", interactive=False), | |
| gr.Textbox(label="Non-Compliant Observations %", interactive=False) | |
| ], | |
| allow_flagging = "never" | |
| ) | |
| demo = gr.TabbedInterface([tab_files, tab_search_by_sku], ["Upload Sheet", "Search"]) | |
| demo.launch(auth=("BrianA", "Brian@Muffintown1")) |