Upload streamlit_app.py

streamlit_app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from openpyxl.styles import PatternFill
+from io import BytesIO
+
+# Function to handle file transformation
+def process_files(yoman_file, kasefet_file):
+    # Load the data from the two files (Excel format)
+    yoman_df = pd.read_excel(yoman_file)
+    kasefet_df = pd.read_excel(kasefet_file)
+
+    # First transformation for kasefet
+    kasefet_df['linedirection'] = kasefet_df['OfficeLineId'].astype(str) + "-" + kasefet_df['Direction'].astype(str)
+
+    # Rename columns in yoman DataFrame
+    yoman_df = yoman_df.rename(columns={
+        'מפעיל': 'operator',
+        'אשכול': 'cluster',
+        'מספר אירוע': 'numevent',
+        'תאריך ושעת התחלת אירוע': 'startdate',
+        'תאריך ושעת סיום אירוע': 'enddate',
+        'סטטוס': 'status',
+        'מקטי קו+כיוונים': 'makatdirection',
+        'תיאור האירוע': 'description',
+        'סוג אירוע': 'event_type',
+        'הערת דחייה': 'delay_note'
+    })
+
+    # Convert date columns to datetime
+    yoman_df['startdate'] = pd.to_datetime(yoman_df['startdate'], errors='coerce')
+    yoman_df['enddate'] = pd.to_datetime(yoman_df['enddate'], errors='coerce')
+
+    # Merge the two DataFrames
+    merged_df = pd.merge(
+        yoman_df,
+        kasefet_df,
+        how='left',  # Left join to keep all rows from yoman_df
+        left_on='makatdirection',
+        right_on='linedirection'
+    )
+
+    # Convert the trip_dt column in merged_df to datetime
+    merged_df['trip_dt'] = pd.to_datetime(merged_df['trip_dt'], errors='coerce')
+
+    # Create a mask for the date filtering
+    date_mask = merged_df['trip_dt'].between(merged_df['startdate'], merged_df['enddate'], inclusive='both')
+    filtered_df = merged_df[date_mask].copy()
+
+    # Select relevant columns for the final DataFrame
+    final_columns = [
+        'numevent', 'startdate', 'enddate', 'status', 'event_type', 'Direction', 'TripId', 'delay_note',
+        'trip_dt', 'ClusterId', 'OfficeLineId', 'LineAlternative', 'trip_time', 'rishui_time',
+        'bitzua_history_start_dt', 'bitzua_history_end_dt', 'rishui_bitzua_departure_time_diff',
+        'description', 'status_nesia_luz_nm', 'status_av_nesia_luz_nm', 'status_bakara_luz_nm'
+    ]
+    final_df = filtered_df[final_columns].copy()
+
+    # Add missing numevent rows from yoman
+    missing_numevents = set(yoman_df['numevent']) - set(final_df['numevent'])
+    if missing_numevents:
+        new_rows = []
+        for numevent in missing_numevents:
+            yoman_row = yoman_df[yoman_df['numevent'] == numevent].iloc[0]
+            new_row = {
+                'numevent': numevent,
+                'status': yoman_row['status'],
+                'event_type': yoman_row['event_type'],
+                'Direction': np.nan,
+                'TripId': np.nan,
+                'delay_note': yoman_row['delay_note'],
+                'trip_dt': np.nan,
+                'ClusterId': np.nan,
+                'OfficeLineId': np.nan,
+                'LineAlternative': np.nan,
+                'trip_time': np.nan,
+                'rishui_time': np.nan,
+                'bitzua_history_start_dt': np.nan,
+                'bitzua_history_end_dt': np.nan,
+                'rishui_bitzua_departure_time_diff': np.nan,
+                'description': yoman_row['description'],
+                'status_nesia_luz_nm': np.nan,
+                'status_av_nesia_luz_nm': np.nan,
+                'status_bakara_luz_nm': np.nan,
+                'startdate': yoman_row['startdate'],
+                'enddate': yoman_row['enddate']
+            }
+            new_rows.append(new_row)
+
+        new_rows_df = pd.DataFrame(new_rows)
+        new_rows_df = new_rows_df.dropna(axis=1, how='all')
+        final_df = pd.concat([final_df, new_rows_df], ignore_index=True)
+
+    # Add the electronic control column
+    final_df['בקרה אלקטרונית'] = np.where(
+        final_df['description'].str.startswith(('לא לבקרה', 'לא', 'לא בקרה')),
+        'לא לבקרה אלקטרונית',
+        'כן לבקרה אלקטרונית'
+    )
+
+    # Mark rows that are not detected with "no find results"
+    final_df['not detected'] = np.where(final_df['TripId'].isna(), 'no', '')
+
+    # Highlight 'not detected' rows
+    with pd.ExcelWriter('final_output.xlsx', engine='openpyxl') as writer:
+        final_df.to_excel(writer, index=False, sheet_name='Data')
+        workbook = writer.book
+        worksheet = writer.sheets['Data']
+
+        pink_fill = PatternFill(start_color='FFB2D5', end_color='FFB2D5', fill_type='solid')
+        for row in range(final_df.shape[0]):
+            if final_df.iloc[row]['not detected'] == 'no':
+                for col in range(final_df.shape[1]):
+                    worksheet.cell(row=row + 2, column=col + 1).fill = pink_fill
+
+    return 'final_output.xlsx'
+
+# Streamlit interface
+st.title('Data Transformation Tool')
+
+st.markdown("""
+    Please upload the Yoman and Kasefet files for processing.
+""")
+
+# Upload files
+yoman_file = st.file_uploader("Upload Yoman File", type=["xlsx"])
+kasefet_file = st.file_uploader("Upload Kasefet File", type=["xlsx"])
+
+if yoman_file and kasefet_file:
+    if st.button("Process and Download Output"):
+        output_file = process_files(yoman_file, kasefet_file)
+        st.success("Processing completed successfully!")
+
+        # Provide download link for the final output file
+        with open(output_file, "rb") as f:
+            st.download_button(
+                label="Download Final Output",
+                data=f,
+                file_name="final_output.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+            )