User: you need to build a scrapper service which scrapes data and push it in db after cleaning 
so will look on google for more than 200 cities one by one , pupolar resutrant in bangalore then, we scrap first two pages of google and we will prepare a list of 10-15 popular websites list which cover about resurants , scrap list of resturants from there. then push it in db like resturant name , and its component like - review , price etc.
then we will give ranking to each resturant according to number of times it appears in each 10 websites .
Assistant: To build a scraper service that gathers data on popular restaurants from more than 200 cities and processes the data for storage in a database, you need to follow a structured approach. Here is a step-by-step guide to help you:

### Step 1: Define Requirements and Setup
- **Scope**: Scrape data for restaurants in 200+ cities.
- **Data Points**: Restaurant name, reviews, price, etc.
- **Ranking**: Rank restaurants based on frequency across multiple websites.
- **Tools**: Choose your tech stack, such as Python with libraries like BeautifulSoup and Requests, and a database like MongoDB or PostgreSQL.

### Step 2: Perform Initial Google Search
- **Automation**: Use an automated tool or library like Selenium or Puppeteer to perform Google searches for keywords such as "popular restaurants in Bangalore", "famous restaurants in London", etc. for each city.
- **Results Curation**: Extract the URLs from the first two pages of search results.

### Step 3: Identify Popular Websites
- **Web Page Analysis**: Analyze the search results and identify 10-15 popular and relevant websites that list restaurants.
- **Website Selection**: Choose websites based on relevance and data availability.

### Step 4: Extract Data from Websites
- **Scraping Logic**: Use web scraping libraries like BeautifulSoup, Scrapy, or Selenium to extract required data from selected websites.
  - **Restaurant Information**: Gather restaurant name, number of reviews, average rating, price range, etc.
- **Data Cleaning**: Validate and clean the extracted data by removing duplicates and correcting formats.

### Step 5: Store Data in Database
- **Database Design**: Create a database schema to fit your data model, e.g., tables for restaurants, reviews, and ranks.
- **Inserting Data**: Use database connectors (e.g., PyMySQL, SQLAlchemy, PyMongo) to insert the cleaned data into the database.

### Step 6: Implement Ranking System
- **Frequency Analysis**: Count the number of occurrences of each restaurant across the websites.
- **Ranking Calculation**: Assign a rank to each restaurant based on its frequency—the more a restaurant appears, the higher its rank.

### Step 7: Automate & Schedule
- **Automation**: Set up a cron job or use a task scheduler to automate the scraping process at specified intervals.
- **Error Handling**: Implement logging and exception handling to ensure robust operations.

### Step 8: Monitor & Maintain
- **Monitoring**: Regularly monitor the performance and logs of the scraper service.
- **Adaptation**: Update scraping scripts as websites change their structure over time.

### Considerations
- **Legal Compliance**: Check the legality and terms of service of scraping the websites.
- **Scalability**: Design the service to handle scaling, as it will be scraping and processing data for many cities.
- **Data Quality**: Ensure consistent and high-quality data for meaningful ranking outcomes.

By following these steps, you can create an effective web scraper service that gathers restaurant information from a large number of cities and provides useful rankings based on data frequency across different platforms.
User: can you name me some websites which list major cities restirants on it . like delhi , pune , hydrabsad , london , paris etc 
Assistant: Certainly! There are several well-known websites and platforms that provide listings and reviews of restaurants in major cities around the world. Here are some examples:

1. **TripAdvisor**: Offers reviews and information about restaurants in cities worldwide, including major cities like Delhi, Pune, Hyderabad, London, and Paris.

2. **Yelp**: Popular in many countries, Yelp provides user reviews and ratings for restaurants in various cities.

3. **Zomato**: Originally started in India, Zomato lists restaurants and user reviews in cities like Delhi, Pune, Hyderabad, and has expanded to other international cities.

4. **OpenTable**: Primarily used for making reservations, OpenTable also provides listings and reviews in cities like London and Paris.

5. **Google Maps/Google Local**: Provides restaurant listings, reviews, and ratings for virtually any city around the world.

6. **Eater**: Offers curated lists and news about top restaurants in many major cities globally, particularly in the U.S. and Europe.

7. **Time Out**: Known for events and entertainment, Time Out also lists restaurants in major cities with reviews and guides.

8. **Foursquare**: Offers user-generated reviews and ratings for restaurants in many major cities worldwide.

9. **LaFourchette (TheFork)**: Popular in Europe, especially in France and surrounding countries, it offers restaurant listings and bookings with reviews.

10. **Michelin Guide**: Provides a prestigious list of top-rated restaurants globally, particularly focusing on gourmet dining.

When scraping data from these websites, ensure you comply with their terms of service and consider APIs if available, as they might offer a structured way to access the data legally.
User: can we build any ai llm based scrapper , ? use gpt api to help us scrape beter and faster
Assistant: