Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,303 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import json
+from datetime import datetime
+import io
+import os
+
+# --- Inizializzazione Session State ---
+if 'site_code_input' not in st.session_state:
+    st.session_state.site_code_input = ""
+if 'file_uploader_key' not in st.session_state:
+    st.session_state.file_uploader_key = 0
+
+# --- CSS personalizzato ---
+st.markdown(
+    """
+    <style>
+    .stApp {
+        background-color: #FFFFCC;
+        background-attachment: fixed;
+    }
+    h1, h2 {
+        color: #1E90FF;
+    }
+    p, label {
+        color: #000080;
+    }
+    .stButton>button {
+        color: white;
+        background-color: #1E90FF;
+        border: none;
+        border-radius: 5px;
+        padding: 10px 20px;
+        font-size: 16px;
+    }
+    .stButton>button:hover {
+        background-color: #4169E1;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Funzione di conversione
+def convert_json_to_excel_data(json_file_content: str):
+    try:
+        json_data = json.loads(json_file_content)
+
+        # Campi a livello radice
+        site_code_json = json_data.get('siteCode')
+        company_code_json = json_data.get('companyCode')
+        
+        # Campi sotto 'outbound'
+        outbound_info = json_data.get('outbound', {})
+
+        order_number = outbound_info.get('orderNumber')
+        order_doc_date_str = outbound_info.get('orderDocDate')
+        order_type_json = outbound_info.get('orderType') # Dal JSON è "CL"
+        
+        # Fissi: deliveryType (non nel JSON), service (nel JSON ma fisso PDB), serviceType (nel JSON ma fisso Standard)
+        delivery_type_fixed = "DELIVERY" 
+        service_fixed = "PDB" 
+        service_type_fixed = "Standard"
+
+        order_date_detail = outbound_info.get('orderDateDetail')
+        order_date_str = outbound_info.get('orderDate') # Questo sarà 'dueDate'
+        reason_code = outbound_info.get('reasonCode')
+        payment_info_value = outbound_info.get('paymentInfo', {}).get('value')
+
+        delivery_customer = outbound_info.get('deliveryCustomer', {})
+        customer_code = delivery_customer.get('code')
+        consignee_name = delivery_customer.get('name')
+        consignee_address = delivery_customer.get('address')
+        consignee_city = delivery_customer.get('city')
+        consignee_province = delivery_customer.get('province')
+        consignee_zip_code = delivery_customer.get('zipCode')
+        consignee_country = delivery_customer.get('country')
+        customer_mail = delivery_customer.get('refMail')
+        
+        note_ddt = outbound_info.get('noteDdt')
+        note_warehouse = outbound_info.get('noteWarehouse')
+        note_delivery = outbound_info.get('noteDelivery')
+        note_shipment = outbound_info.get('noteShipment')
+        reason_code_shp = outbound_info.get('reasonCodeShp')
+        info_delivery = outbound_info.get('infoDelivery')
+        booking_info = outbound_info.get('bookingInfo')
+
+        # Formattazione delle date
+        order_doc_date_formatted = None
+        if order_doc_date_str:
+            try:
+                dt_object = datetime.fromisoformat(order_doc_date_str.replace('Z', '+00:00'))
+                order_doc_date_formatted = dt_object.strftime('%d/%m/%Y')
+            except ValueError:
+                order_doc_date_formatted = order_doc_date_str
+                st.warning(f"Attenzione: Impossibile parsare 'orderDocDate' '{order_doc_date_str}'. Verrà usata la stringa originale.")
+
+        due_date_formatted = None
+        if order_date_str:
+            try:
+                dt_object = datetime.fromisoformat(order_date_str.replace('Z', '+00:00'))
+                due_date_formatted = dt_object.strftime('%d/%m/%Y')
+            except ValueError:
+                due_date_formatted = order_date_str
+                st.warning(f"Attenzione: Impossibile parsare 'orderDate' '{order_date_str}'. Verrà usata la stringa originale.")
+
+
+        details = outbound_info.get('details', [])
+
+        processed_data_for_this_file = []
+        for item in details:
+            row = {
+                'siteCode': site_code_json,
+                'companyCode': company_code_json,
+                'orderNumber': order_number,
+                'orderType': order_type_json, # Mappa da 'outbound.orderType' (CL nel JSON, non fisso DELIVERY)
+                'deliveryType': delivery_type_fixed, # Fisso a 'DELIVERY' come da richiesta immagine
+                'orderDocDate': order_doc_date_formatted,
+                'orderDateDetail': order_date_detail,
+                'dueDate': due_date_formatted,
+                'reasonCode': reason_code,
+                'service': service_fixed, # Fisso a 'PDB' come da richiesta immagine
+                'serviceType': service_type_fixed, # Fisso a 'Standard' come da richiesta immagine
+                'paymentInfo.value': payment_info_value,
+                'customer.code': customer_code,
+                'consignee.name': consignee_name,
+                'consignee.address': consignee_address,
+                'consignee.city': consignee_city,
+                'consignee.province': consignee_province,
+                'consignee.zipCode': consignee_zip_code,
+                'consignee.country': consignee_country,
+                'customer.mail': customer_mail,
+                'details.positionId': item.get('positionId'),
+                'details.itemNumber': item.get('itemCode'),
+                'details.requestedQt': item.get('quantity'),
+                'details.lotId': item.get('lotId'),
+                'details.stockOwner': item.get('stockOwner'),
+                'details.magCli': item.get('warehouse'),
+                'details.availability': item.get('availability'),
+                'details.accountingStatus': item.get('accountingStatus'),
+                'details.refOrder': item.get('refOrder'),
+                'noteDdt': note_ddt,
+                'noteWarehouse': note_warehouse,
+                'noteDelivery': note_delivery,
+                'noteShipment': note_shipment,
+                'reasonCodeShp': reason_code_shp,
+                'infoDelivery': info_delivery,
+                'bookingInfo': booking_info
+            }
+            processed_data_for_this_file.append(row)
+
+        if not processed_data_for_this_file:
+            return pd.DataFrame() 
+
+        return pd.DataFrame(processed_data_for_this_file)
+
+    except json.JSONDecodeError as e:
+        st.error(f"Errore nel file JSON: {e}. Assicurati che il contenuto sia un JSON valido.")
+        return None
+    except KeyError as ke:
+        st.error(f"Errore: Chiave JSON mancante o struttura inattesa: {ke}. Assicurati che il JSON abbia la struttura attesa.")
+        return None
+    except Exception as e:
+        st.error(f"Si è verificato un errore generico durante la conversione di un file JSON: {e}")
+        return None
+
+# --- Interfaccia Streamlit ---
+st.title("Convertitore JSON Outbound a Template Excel Outbound")
+st.write("Carica i tuoi file JSON degli ordini Outbound e scarica il foglio Excel formattato.")
+
+# Non chiediamo siteCode all'utente se è già nel JSON, a meno che non vogliamo sovrascriverlo
+# st.header("1. Qual è il Site Code? Inseriscilo qui:")
+# user_site_code = st.text_input("Site Code", key="site_code_input")
+st.markdown("**(Il Site Code verrà letto direttamente dal JSON)**")
+
+
+st.header("1. Carica i tuoi file JSON")
+# Passiamo la chiave dinamica al file_uploader
+uploaded_files = st.file_uploader(
+    "Scegli uno o più file JSON di tipo 'Ordine_Outbound'",
+    type=["json"],
+    accept_multiple_files=True,
+    key=st.session_state.file_uploader_key # Usa la chiave dinamica qui
+)
+
+all_processed_data = []
+
+if uploaded_files:
+    st.info(f"Caricati {len(uploaded_files)} file. Elaborazione in corso...")
+    
+    conversion_successful = True
+    
+    for uploaded_file in uploaded_files:
+        file_name = uploaded_file.name
+        
+        # Verifica che il nome del file inizi con "Ordine_Outbound"
+        if not file_name.lower().startswith("ordine_outbound"):
+            st.error(f"Errore: Il file '{file_name}' non inizia con 'Ordine_Outbound'. Verrà ignorato.")
+            conversion_successful = False
+            continue
+        
+        try:
+            json_content_bytes = uploaded_file.read()
+            json_content_string = json_content_bytes.decode('utf-8')
+
+            df_this_file = convert_json_to_excel_data(json_content_string)
+            
+            if df_this_file is not None and not df_this_file.empty: # Assicurati che il DataFrame non sia vuoto
+                all_processed_data.append(df_this_file)
+            elif df_this_file is None: # C'è stato un errore e la funzione ha restituito None
+                conversion_successful = False
+                st.error(f"Impossibile convertire il file '{file_name}'. Controlla i log per i dettagli.")
+            # else: df_this_file era vuoto, nessun errore ma nessun dato da aggiungere
+            
+        except Exception as e:
+            st.error(f"Si è verificato un errore imprevisto durante la lettura o l'elaborazione del file '{file_name}': {e}")
+            conversion_successful = False
+
+    if all_processed_data:
+        final_df = pd.concat(all_processed_data, ignore_index=True)
+        st.success(f"Elaborazione di {len(all_processed_data)} file JSON completata! Pronto per il download.")
+        
+        output = io.BytesIO()
+        writer = pd.ExcelWriter(output, engine='xlsxwriter')
+        
+        sheet_name_excel = 'OutboundOrder' # Nome del foglio Excel
+        final_df.to_excel(writer, sheet_name=sheet_name_excel, index=False)
+
+        workbook = writer.book
+        worksheet = writer.sheets[sheet_name_excel]
+        
+        # Ordine delle colonne e larghezze per l'Excel Outbound
+        # Basato sul mapping fornito, con siteCode/companyCode in testa
+        final_columns_order = [
+            'siteCode', 'companyCode', 'orderNumber', 'orderType', 'deliveryType',
+            'orderDocDate', 'orderDateDetail', 'dueDate', 'reasonCode',
+            'service', 'serviceType', 'paymentInfo.value', 'customer.code',
+            'consignee.name', 'consignee.address', 'consignee.city', 'consignee.province',
+            'consignee.zipCode', 'consignee.country', 'customer.mail',
+            'details.positionId', 'details.itemNumber', 'details.requestedQt',
+            'details.lotId', 'details.stockOwner', 'details.magCli', 'details.availability',
+            'details.accountingStatus', 'details.refOrder',
+            'noteDdt', 'noteWarehouse', 'noteDelivery', 'noteShipment',
+            'reasonCodeShp', 'infoDelivery', 'bookingInfo'
+        ]
+        
+        # Filtra solo le colonne effettivamente presenti nel DataFrame dopo la conversione
+        # e riordinale
+        cols_present_and_ordered = [col for col in final_columns_order if col in final_df.columns]
+        final_df = final_df[cols_present_and_ordered]
+
+        # Larghezze colonne ottimizzate per Outbound
+        col_widths = {
+            'siteCode': 10, 'companyCode': 15, 'orderNumber': 15, 'orderType': 10, 'deliveryType': 12,
+            'orderDocDate': 15, 'orderDateDetail': 15, 'dueDate': 12, 'reasonCode': 12,
+            'service': 10, 'serviceType': 12, 'paymentInfo.value': 15, 'customer.code': 15,
+            'consignee.name': 30, 'consignee.address': 30, 'consignee.city': 15, 'consignee.province': 10,
+            'consignee.zipCode': 10, 'consignee.country': 10, 'customer.mail': 25,
+            'details.positionId': 12, 'details.itemNumber': 20, 'details.requestedQt': 15,
+            'details.lotId': 10, 'details.stockOwner': 15, 'details.magCli': 15, 'details.availability': 15,
+            'details.accountingStatus': 18, 'details.refOrder': 15,
+            'noteDdt': 20, 'noteWarehouse': 20, 'noteDelivery': 20, 'noteShipment': 20,
+            'reasonCodeShp': 15, 'infoDelivery': 20, 'bookingInfo': 20
+        }
+        
+        # Applica le intestazioni e le larghezze delle colonne
+        for col_num, col_name in enumerate(final_df.columns.values):
+            worksheet.write(0, col_num, col_name) 
+            if col_name in col_widths:
+                worksheet.set_column(col_num, col_num, col_widths[col_name])
+            else:
+                worksheet.set_column(col_num, col_num, 15) # Larghezza di default
+
+        writer.close()
+        output.seek(0)
+
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        output_file_name = f"OrdiniOutbound_{timestamp}.xlsx" # Nome del file con timestamp
+
+        st.download_button(
+            label="Scarica file Excel Unificato",
+            data=output.getvalue(),
+            file_name=output_file_name,
+            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        )
+    elif not conversion_successful:
+        st.error("Nessun file valido è stato elaborato con successo. Controlla i messaggi di errore sopra.")
+    else:
+        st.info("Nessun dato valido estratto dai file selezionati.")
+else:
+    st.info("Carica i tuoi file JSON per iniziare.")
+
+
+# --- Funzione per il reset: incrementa la chiave del file_uploader ---
+def clear_inputs():
+    st.session_state.file_uploader_key += 1
+    st.session_state.site_code_input = ""
+    st.rerun()
+
+st.markdown("---")
+st.button("Pulisci Input (RESET)", on_click=clear_inputs)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.markdown("Sviluppato con Streamlit e Python.")