initial

app.py

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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(share=True, auth=("BrianA", "Brian@Muffintown1")