import json
import requests
from bs4 import BeautifulSoup
import pandas as pd

def fetch_restaurant_links(city, location):
    base_url = "https://deliveroo.ae"
    url = f"{base_url}/restaurants/{city}/{location}/?collection=restaurants"

    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
        'Cookie': '__cf_bm=oakl46sJ3V9vwmnIIbfXWfkHbGmmC2pH56GyTI33b4U-1715931048-1.0.1.1-4XOcSGSThZV_INfpn3aptlo8jpZtLFbYoLsZxP9BpQ8LIjq3wBIe8CPlSf0AomuniXy4TZWyVlBQBTlrm.CPiSfI1jzx18y9zxwc9GX0fmo; roo_guid=c40617a7-76f7-432c-b780-f2653cd2edfe; roo_session_guid=2e989653-2776-4ede-a52e-b610f1ad64a2'
    }

    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        soup = BeautifulSoup(response.content, 'html.parser')

        if "We couldn't find" in soup.text or "No restaurants" in soup.text:
            print("No restaurants found for the specified location.")
            return []

        divs = soup.find_all('div', class_=["HomeFeedScrollTracker-bd9a6ffea8a4b4b7", "HomeFeedUICard-157f7be5d7b2fa7b"])

        hrefs = [a_tag['href'] for div in divs for a_tag in div.find_all('a', href=True)]
        hrefs = hrefs[:20]
        return [f"{base_url}{href}" for href in hrefs]
    else:
        print("Response timed out.")
        return []

def Excel_final(urls):
    def fetch_restaurant_data(url):
        headers = {
            'Cookie': '__cf_bm=_AOZtAiObnqBHPy4zhGRgBLW9xg9WiaDCRzg5E0sbMk-1715757967-1.0.1.1-xZNMBsnAqy_tfjUveujgfzT4Usw5ur4u7L0JlCcNXAQIC6Cq6wj46vPH7RLTh0Gq90JENxl7kbzjyOUFaBr8yCkmRGmt7APITEk0kkXzLTs; roo_guid=c40617a7-76f7-432c-b780-f2653cd2edfe; roo_session_guid=5846d6f0-5b7f-4598-8c6d-82b8023fd4fc'
        }
        response = requests.get(url, headers=headers)
        if response.status_code != 200:
            print(f"Failed to fetch the URL: {url}")
            return None
        soup = BeautifulSoup(response.content, 'html.parser')
        script_tag = soup.find('script', id='__NEXT_DATA__')
        if not script_tag:
            print("Script tag not found")
            return None
        json_data = json.loads(script_tag.string)
        json_data = json_data['props']['initialState']['menuPage']['menu']['meta']
        items = json_data['items']
        categories = json_data['categories']
        category_map = {category['id']: category['name'] for category in categories}
        modifier_groups = json_data['modifierGroups']
        modifier_groups_dict = {modifier_group['id']: modifier_group for modifier_group in modifier_groups}
        items_with_modifiers = []
        current_category = None
        current_category_position = 0
        for item in items:
            category_id = item['categoryId']
            category_name = category_map.get(category_id, 'Unknown')
            if category_name == "Unknown":
                continue
            if category_name != current_category:
                current_category = category_name
                current_category_position += 1
                item_position = 1
            else:
                item_position += 1
            item_with_modifiers = {
                "id": item['id'],
                "category_id": category_id,
                "category_name": category_name,
                "category_position": current_category_position,
                "item_position": item_position,
                "name": item['name'],
                "description": item.get('description', ''),
                "price": item['price']['formatted'],
                "img_url": item.get('image').get('url', '') if item.get('image') else '',
                "modifier_groups": [modifier_groups_dict.get(modifier_group_id, {}) for modifier_group_id in item.get('modifierGroupIds', [])],
            }
            items_with_modifiers.append(item_with_modifiers)
        return items_with_modifiers

    def save_data_to_excel(data, sheet_name, writer):
        rows = []
        max_options = 0
        # Find the maximum number of options for any modifier group
        for item in data:
            for modifier_group in item['modifier_groups']:
                num_options = len(modifier_group.get('modifierOptions', []))
                if num_options > max_options:
                    max_options = num_options
        for item in data:
            base_row = [
                item['category_name'],
                item['category_position'],
                item['item_position'],
                item['name'],
                item['description'],
                item['price'],
                item['img_url'],
            ]
            first_modifier_group = True
            for modifier_group in item['modifier_groups']:
                modifier_group_row = base_row + [
                    modifier_group.get('name', ''),
                    modifier_group.get('minSelection', ''),
                    modifier_group.get('maxSelection', '')
                ]
                options = modifier_group.get('modifierOptions', [])
                for option in options:
                    modifier_group_row += [
                        option.get('name', ''),
                        option['price']['formatted'] if option.get('price') else ''
                    ]
                # Fill in the remaining columns with empty strings if there are fewer options than max_options
                modifier_group_row += [''] * (max_options * 2 - len(options) * 2)
                if first_modifier_group:
                    rows.append(modifier_group_row)
                    first_modifier_group = False
                else:
                    rows.append([''] * len(base_row) + modifier_group_row[len(base_row):])
            if not item['modifier_groups']:
                rows.append(base_row + [''] * (max_options * 2 + 3))
        # Create column headers
        columns = [
            'Category Name', 'Category Position', 'Item Position', 'Item Name', 'Description', 'Item Price', 'Image URL', 'Modifier Group Name', 'Min Selection', 'Max Selection'
        ]
        for i in range(1, max_options + 1):
            columns += [f'Option {i} Name', f'Option {i} Price']
        df = pd.DataFrame(rows, columns=columns)
        if 'Max Selection' in df.columns:
            max_column_index = df.columns.get_loc('Max Selection')
            for i in range(max_column_index + 1, len(df.columns)):
                df.rename(columns={df.columns[i]: ''}, inplace=True)
        df.to_excel(writer, sheet_name=sheet_name, index=False)

    with pd.ExcelWriter("restaurant_data.xlsx", engine='xlsxwriter') as writer:
        for idx, url in enumerate(urls):
            data = fetch_restaurant_data(url)
            if data:
                save_data_to_excel(data, f'Sheet{idx+1}', writer)
    print("Data saved to restaurant_data.xlsx")

if __name__ == "__main__":
    city = input("Enter the city: ")
    location = input("Enter the location: ")

    urls = fetch_restaurant_links(city, location)
    
    if urls:
        Excel_final(urls)
    else:
        print("No restaurant links found or unable to fetch data.")