Excel/Noonfood_excel_final.py

import requests
import json
import pandas as pd
import numpy as np
from fastapi import FastAPI, Query

app=FastAPI()

latitude=int(input("Enter latitude:"))
longitude=int(input("Enter longitude:"))
url1=input("Enter the restaurant url:")
outlet_code = url1.split('/')[-2]

def extract_items_with_categories(menu):
    items_list = []
    categories_seen = set()  # Set to keep track of categories that have been added
    for category in menu['categories']:
        category_name = category['name']
        category_position = category['position'] if category['position'] != -1 else 0
        if category_name not in categories_seen:
            items_list.append({
                'category': category_name,
                'item': None,  # Placeholder for item
                'itemCode': None,
                'item-position': None,
                'img-url': None,
                'price': None,
                'Description': None,
                'position': category_position
            })
            categories_seen.add(category_name)
        for item_code in category['items']:
            item = next((item for item in menu['items'] if item['itemCode'] == item_code), None)
            if item:
                items_list.append({
                    'category': '',  # Empty string for subsequent items in the same category
                    'item': item['name'],
                    'itemCode': item['itemCode'],
                    'item-position': item['position'],
                    'img-url': "https://f.nooncdn.com/food_production/"+item['image'],
                    'price': item['price'],
                    'Description': item['itemDesc'],
                    'position': category['position']
                })
    return items_list

def extract_options(menu):
    options_dict = {}
    for item in menu['items']:
        if 'modifiers' in item:
            for modifier_code in item['modifiers']:
                modifier = next((modifier for modifier in menu['modifiers'] if modifier['modifierCode'] == modifier_code), None)
                if modifier:
                    if item['itemCode'] not in options_dict:
                        options_dict[item['itemCode']] = {}
                    if modifier['name'] not in options_dict[item['itemCode']]:
                        options_dict[item['itemCode']][modifier['name']] = {
                            'Min': modifier.get('minTotalOptions'),
                            'Max': modifier.get('maxTotalOptions'),
                            'Options': []
                        }
                    for option in modifier['options']:
                        option_item = next((i for i in menu['items'] if i['itemCode'] == option['itemCode']), None)
                        if option_item:
                            options_dict[item['itemCode']][modifier['name']]['Options'].append({
                                'Option name': option_item['name'],
                                'Option price': option['price']
                            })
    return options_dict

# Make the request
url = "https://food.noon.com/_svc/mp-food-api-mpnoon/consumer/restaurant/outlet/details/guest"
payload = {
    "addressLat": latitude,
    "addressLng": longitude,
    "deliveryType": "default",
    "outletCode": outlet_code
}
headers = {
    'Connection': 'keep-alive',
    "Accept": "application/json, text/plain, */*",
    "Accept-Encoding": "gzip, deflate, br, zstd",
    "Accept-Language": "en-GB,en-US;q=0.9,en;q=0.8,gu;q=0.7",
    "Cache-Control": "no-cache, max-age=0, must-revalidate, no-store",
    "Content-Type": "application/json",
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36",
    "Cookie": "bm_mi=791533C8E67CE8E7DA98E80ADED70F69~YAAQRK0cuOep9tGPAQAAUYKw3RcGDAVhD+mtWU8IH76wZL29zl4qqCjMwGv8sKtYlQWZNaFftSOvHFOvQU4+3CY2uHZyjjK6I3GeNdKEn+XHupISeNc0K16GOXLqcPOwu4sADTmxE7PYQvSQE7eimhqsBiJVRd96R8W0D2hl31FlY/4rl+NPZvM3iXjrn2GO50VMv+HhGfCnDMBwApBxgpMWFLfs0u6EYy44mg/FXbom5s5pa3cro8AS35nYHbdUbi61K9fnWRVaF8L/4z0xh7V1AEQETevb5fdGF8aB9m2UG29p2W6KSMb8DyFZLpG3vl5+IRECqZdFxaUMnykO8G/ynRHG~1; Domain=.noon.com; Path=/; Expires=Mon, 03 Jun 2024 12:41:22 GMT; Max-Age=7199; Secure"
}
response = requests.post(url, headers=headers, json=payload)
json_data = response.json()


# Extract items and options
items = extract_items_with_categories(json_data['data']['menu'])
options = extract_options(json_data['data']['menu'])

# Create a DataFrame for items
items_df = pd.DataFrame(items)

options_list = []
for item_code, option_groups in options.items():
    for group_name, group_data in option_groups.items():
        row = {
            'itemCode': item_code,
            'Option Group Name': group_name,
            'Min': group_data.get('Min'),
            'Max': group_data.get('Max')
        }
        for i, option in enumerate(group_data['Options']):
            row[f'Option name {i+1}'] = option['Option name']
            row[f'Option price {i+1}'] = option['Option price']
        options_list.append(row)

# Create DataFrame for options
options_df = pd.DataFrame(options_list)

# Merge DataFrames on 'itemCode'
merged_df = items_df.merge(options_df, on='itemCode', how='left')

merged_df['category'] = merged_df['category'].replace('', np.nan).ffill()
merged_df['item'] = merged_df['item'].replace('', np.nan)
#merged_df.iloc[:, :7] = merged_df.groupby('category').apply(lambda x: x.ffill().mask(x.duplicated(), '')).reset_index(level=0, drop=True)

#merged_df['category'] = merged_df['category'].replace('', pd.NA).ffill()
merged_df.iloc[:, :7] = merged_df.iloc[:, :7].mask(merged_df.iloc[:, :7].duplicated(), '')
merged_df = merged_df.dropna(subset=['item', 'itemCode', 'item-position', 'img-url', 'price', 'Description'], how='all')


merged_df.to_excel("output3.xlsx", index=False)