Dropshipping Research Tool Demo in Python

• dropshipping-tool-demo

  1 16 1.1 Python

  This demo compares products from Walmart and eBay to find a profit. Sponsored by SerpApi.

$ git clone https://github.com/chukhraiartur/dropshipping-tool-demo.git

• streamlit

  254 31,506 9.8 Python

  Streamlit — A faster way to build and share data apps.

  

from serpapi import EbaySearch, WalmartSearch import streamlit as st import streamlit.components.v1 as components import pandas as pd import time, os, Levenshtein def get_walmart_results(query: str): params = { 'api_key': os.getenv('SERPAPI_API_KEY'), # https://serpapi.com/manage-api-key 'engine': 'walmart', # search engine 'query': query, # search query } search = WalmartSearch(params) # data extraction on the SerpApi backend results = search.get_dict() # JSON -> Python dict return results.get('organic_results', []) def get_ebay_results(query: str): params = { 'api_key': os.getenv('SERPAPI_API_KEY'), # https://serpapi.com/manage-api-key 'engine': 'ebay', # search engine '_nkw': query, # search query 'ebay_domain': 'ebay.com', # ebay domain } search = EbaySearch(params) # data extraction on the SerpApi backend results = search.get_dict() # JSON -> Python dict return results.get('organic_results', []) def compare_walmart_with_ebay(query: str, number_of_products: int, percentage_of_uniqueness: float): data = [] walmart_results = get_walmart_results(query) for walmart_result in walmart_results[:number_of_products]: ebay_results = get_ebay_results(walmart_result['title']) for ebay_result in ebay_results: if Levenshtein.ratio(walmart_result['title'], ebay_result['title']) < percentage_of_uniqueness: continue walmart_price = walmart_result.get('primary_offer', {}).get('offer_price') ebay_price = ebay_result.get('price', {}).get('extracted') if not ebay_price: ebay_price = ebay_result.get('price', {}).get('from', {}).get('extracted') profit = 0 if walmart_price and ebay_price: profit = round(walmart_price - ebay_price, 2) data.append({ 'Walmart': { 'thumbnail': walmart_result['thumbnail'], 'title': walmart_result['title'], 'link': walmart_result['product_page_url'], 'price': walmart_price }, 'eBay': { 'thumbnail': ebay_result['thumbnail'], 'title': ebay_result['title'], 'link': ebay_result['link'], 'price': ebay_price }, 'Profit': profit }) return data def compare_ebay_with_walmart(query: str, number_of_products: int, percentage_of_uniqueness: float): data = [] ebay_results = get_ebay_results(query) for ebay_result in ebay_results[:number_of_products]: walmart_results = get_walmart_results(ebay_result['title']) for walmart_result in walmart_results: if Levenshtein.ratio(ebay_result['title'], walmart_result['title']) < percentage_of_uniqueness: continue ebay_price = ebay_result.get('price', {}).get('extracted') walmart_price = walmart_result.get('primary_offer', {}).get('offer_price') if not ebay_price: ebay_price = ebay_result.get('price', {}).get('from', {}).get('extracted') profit = 0 if ebay_price and walmart_price: profit = round(ebay_price - walmart_price, 2) data.append({ 'eBay': { 'thumbnail': ebay_result['thumbnail'], 'title': ebay_result['title'], 'link': ebay_result['link'], 'price': ebay_price }, 'Walmart': { 'thumbnail': walmart_result['thumbnail'], 'title': walmart_result['title'], 'link': walmart_result['product_page_url'], 'price': walmart_price }, 'Profit': profit }) return data def create_table(data: list, where_to_sell: str): with open('table_style.css') as file: style = file.read() products = '' for product in data: profit_color = 'lime' if product['Profit'] >= 0 else 'red' if where_to_sell == 'Walmart': products += f''' {product['Walmart']['thumbnail']}" width="50"> {product['Walmart']['link']}" target="_blank">{product['Walmart']['title']} {str(product['Walmart']['price'])}$ {product['eBay']['thumbnail']}" width="50"> {product['eBay']['link']}" target="_blank">{product['eBay']['title']} {str(product['eBay']['price'])}$ {profit_color}">{str(product['Profit'])}$ ''' elif where_to_sell == 'eBay': products += f''' {product['eBay']['thumbnail']}" width="50"> {product['eBay']['link']}" target="_blank">{product['eBay']['title']} {str(product['eBay']['price'])}$ {product['Walmart']['thumbnail']}" width="50"> {product['Walmart']['link']}" target="_blank">{product['Walmart']['title']} {str(product['Walmart']['price'])}$ {profit_color}">{str(product['Profit'])}$ ''' table = f''' {style} {products} {list(data[0].keys())[0]} {list(data[0].keys())[1]} {list(data[0].keys())[2]} ''' return table def save_to_json(data: list): json_file = pd.DataFrame(data=data).to_json(index=False, orient='table') st.download_button( label='Download JSON', file_name='comparison-results.json', mime='application/json', data=json_file, ) def save_to_csv(data: list): csv_file = pd.DataFrame(data=data).to_csv(index=False) st.download_button( label="Download CSV", file_name='comparison-results.csv', mime='text/csv', data=csv_file ) def main(): st.title('💸Product Comparison') st.markdown(body='This demo compares products from Walmart and eBay to find a profit. SerpApi Demo Project ([repository](https://github.com/chukhraiartur/dropshipping-tool-demo)). Made with [Streamlit](https://streamlit.io/) and [SerpApi](http://serpapi.com/) 🧡') if 'visibility' not in st.session_state: st.session_state.visibility = 'visible' st.session_state.disabled = False SEARCH_QUERY: str = st.text_input( label='Search query', placeholder='Search', help='Multiple search queries is not supported.' ) WHERE_TO_SELL = st.selectbox( label='Where to sell', options=('Walmart', 'eBay'), help='Select the platform where you want to sell products. The program will look for the same products on another site and calculate the profit.' ) NUMBER_OF_PRODUCTS: int = st.slider( label='Number of products to search', min_value=1, max_value=20, value=10, help='Limit the number of products to analyze.' ) PERCENTAGE_OF_UNIQUENESS: int = st.slider( label='Percentage of uniqueness', min_value=1, max_value=100, value=50, help='The percentage of uniqueness is used to compare how similar one title is to another. The higher this parameter, the more accurate the result.' ) SAVE_OPTION = st.selectbox( label='Choose file format to save', options=(None, 'JSON', 'CSV'), help='By default data won\'t be saved. Choose JSON or CSV format if you want to save the results.' ) col1, col2, col3, col4, col5 = st.columns(5) with col3: submit_button_holder = st.empty() submit_search = submit_button_holder.button(label='Compare products') if submit_search and not SEARCH_QUERY: st.error(body='Looks like you click a button without a search query. Please enter a search query 👆') st.stop() if submit_search and SEARCH_QUERY and WHERE_TO_SELL: with st.spinner(text='Parsing Product Data...'): comparison_results = [] if WHERE_TO_SELL == 'Walmart': comparison_results = compare_walmart_with_ebay(SEARCH_QUERY, NUMBER_OF_PRODUCTS, PERCENTAGE_OF_UNIQUENESS/100) elif WHERE_TO_SELL == 'eBay': comparison_results = compare_ebay_with_walmart(SEARCH_QUERY, NUMBER_OF_PRODUCTS, PERCENTAGE_OF_UNIQUENESS/100) parsing_is_success = st.success('Done parsing 🎉') time.sleep(1) parsing_is_success.empty() submit_button_holder.empty() comparison_results_header = st.markdown(body='#### Comparison results') if comparison_results: table = create_table(comparison_results, WHERE_TO_SELL) components.html(table, height=len(comparison_results)*62 + 40) time.sleep(1) with col3: start_over_button_holder = st.empty() start_over_button = st.button(label='Start over') # centered button if SAVE_OPTION and comparison_results: with st.spinner(text=f'Saving data to {SAVE_OPTION}...'): if SAVE_OPTION == 'JSON': save_to_json(comparison_results) elif SAVE_OPTION == 'CSV': save_to_csv(comparison_results) saving_is_success = st.success('Done saving 🎉') time.sleep(1) saving_is_success.empty() submit_button_holder.empty() start_over_info_holder = st.empty() start_over_info_holder.error(body='To rerun the script, click on the "Start over" button, or refresh the page.') if start_over_button: comparison_results_header.empty() start_over_button_holder.empty() start_over_info_holder.empty() if SAVE_OPTION and not comparison_results: comparison_results_header.empty() no_data_holder = st.empty() no_data_holder.error(body='No product found. Click "Start Over" button and try different search query.') if start_over_button: no_data_holder.empty() start_over_button_holder.empty() if SAVE_OPTION is None and comparison_results: start_over_info_holder = st.empty() start_over_info_holder.error(body='To rerun the script, click on the "Start over" button, or refresh the page.') if start_over_button: comparison_results_header.empty() start_over_button_holder.empty() start_over_info_holder.empty() if SAVE_OPTION is None and not comparison_results: comparison_results_header.empty() no_data_holder = st.empty() no_data_holder.error(body='No product found. Click "Start Over" button and try different search query.') if start_over_button: comparison_results_header.empty() no_data_holder.empty() start_over_button_holder.empty() if __name__ == '__main__': main()

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• google-search-results-nodejs

  57 72 2.7 JavaScript

  SerpApi client library for Node.js. Previously: Google Search Results Node.js.

  

from serpapi import EbaySearch, WalmartSearch import streamlit as st import streamlit.components.v1 as components import pandas as pd import time, os, Levenshtein def get_walmart_results(query: str): params = { 'api_key': os.getenv('SERPAPI_API_KEY'), # https://serpapi.com/manage-api-key 'engine': 'walmart', # search engine 'query': query, # search query } search = WalmartSearch(params) # data extraction on the SerpApi backend results = search.get_dict() # JSON -> Python dict return results.get('organic_results', []) def get_ebay_results(query: str): params = { 'api_key': os.getenv('SERPAPI_API_KEY'), # https://serpapi.com/manage-api-key 'engine': 'ebay', # search engine '_nkw': query, # search query 'ebay_domain': 'ebay.com', # ebay domain } search = EbaySearch(params) # data extraction on the SerpApi backend results = search.get_dict() # JSON -> Python dict return results.get('organic_results', []) def compare_walmart_with_ebay(query: str, number_of_products: int, percentage_of_uniqueness: float): data = [] walmart_results = get_walmart_results(query) for walmart_result in walmart_results[:number_of_products]: ebay_results = get_ebay_results(walmart_result['title']) for ebay_result in ebay_results: if Levenshtein.ratio(walmart_result['title'], ebay_result['title']) < percentage_of_uniqueness: continue walmart_price = walmart_result.get('primary_offer', {}).get('offer_price') ebay_price = ebay_result.get('price', {}).get('extracted') if not ebay_price: ebay_price = ebay_result.get('price', {}).get('from', {}).get('extracted') profit = 0 if walmart_price and ebay_price: profit = round(walmart_price - ebay_price, 2) data.append({ 'Walmart': { 'thumbnail': walmart_result['thumbnail'], 'title': walmart_result['title'], 'link': walmart_result['product_page_url'], 'price': walmart_price }, 'eBay': { 'thumbnail': ebay_result['thumbnail'], 'title': ebay_result['title'], 'link': ebay_result['link'], 'price': ebay_price }, 'Profit': profit }) return data def compare_ebay_with_walmart(query: str, number_of_products: int, percentage_of_uniqueness: float): data = [] ebay_results = get_ebay_results(query) for ebay_result in ebay_results[:number_of_products]: walmart_results = get_walmart_results(ebay_result['title']) for walmart_result in walmart_results: if Levenshtein.ratio(ebay_result['title'], walmart_result['title']) < percentage_of_uniqueness: continue ebay_price = ebay_result.get('price', {}).get('extracted') walmart_price = walmart_result.get('primary_offer', {}).get('offer_price') if not ebay_price: ebay_price = ebay_result.get('price', {}).get('from', {}).get('extracted') profit = 0 if ebay_price and walmart_price: profit = round(ebay_price - walmart_price, 2) data.append({ 'eBay': { 'thumbnail': ebay_result['thumbnail'], 'title': ebay_result['title'], 'link': ebay_result['link'], 'price': ebay_price }, 'Walmart': { 'thumbnail': walmart_result['thumbnail'], 'title': walmart_result['title'], 'link': walmart_result['product_page_url'], 'price': walmart_price }, 'Profit': profit }) return data def create_table(data: list, where_to_sell: str): with open('table_style.css') as file: style = file.read() products = '' for product in data: profit_color = 'lime' if product['Profit'] >= 0 else 'red' if where_to_sell == 'Walmart': products += f''' {product['Walmart']['thumbnail']}" width="50"> {product['Walmart']['link']}" target="_blank">{product['Walmart']['title']} {str(product['Walmart']['price'])}$ {product['eBay']['thumbnail']}" width="50"> {product['eBay']['link']}" target="_blank">{product['eBay']['title']} {str(product['eBay']['price'])}$ {profit_color}">{str(product['Profit'])}$ ''' elif where_to_sell == 'eBay': products += f''' {product['eBay']['thumbnail']}" width="50"> {product['eBay']['link']}" target="_blank">{product['eBay']['title']} {str(product['eBay']['price'])}$ {product['Walmart']['thumbnail']}" width="50"> {product['Walmart']['link']}" target="_blank">{product['Walmart']['title']} {str(product['Walmart']['price'])}$ {profit_color}">{str(product['Profit'])}$ ''' table = f''' {style} {products} {list(data[0].keys())[0]} {list(data[0].keys())[1]} {list(data[0].keys())[2]} ''' return table def save_to_json(data: list): json_file = pd.DataFrame(data=data).to_json(index=False, orient='table') st.download_button( label='Download JSON', file_name='comparison-results.json', mime='application/json', data=json_file, ) def save_to_csv(data: list): csv_file = pd.DataFrame(data=data).to_csv(index=False) st.download_button( label="Download CSV", file_name='comparison-results.csv', mime='text/csv', data=csv_file ) def main(): st.title('💸Product Comparison') st.markdown(body='This demo compares products from Walmart and eBay to find a profit. SerpApi Demo Project ([repository](https://github.com/chukhraiartur/dropshipping-tool-demo)). Made with [Streamlit](https://streamlit.io/) and [SerpApi](http://serpapi.com/) 🧡') if 'visibility' not in st.session_state: st.session_state.visibility = 'visible' st.session_state.disabled = False SEARCH_QUERY: str = st.text_input( label='Search query', placeholder='Search', help='Multiple search queries is not supported.' ) WHERE_TO_SELL = st.selectbox( label='Where to sell', options=('Walmart', 'eBay'), help='Select the platform where you want to sell products. The program will look for the same products on another site and calculate the profit.' ) NUMBER_OF_PRODUCTS: int = st.slider( label='Number of products to search', min_value=1, max_value=20, value=10, help='Limit the number of products to analyze.' ) PERCENTAGE_OF_UNIQUENESS: int = st.slider( label='Percentage of uniqueness', min_value=1, max_value=100, value=50, help='The percentage of uniqueness is used to compare how similar one title is to another. The higher this parameter, the more accurate the result.' ) SAVE_OPTION = st.selectbox( label='Choose file format to save', options=(None, 'JSON', 'CSV'), help='By default data won\'t be saved. Choose JSON or CSV format if you want to save the results.' ) col1, col2, col3, col4, col5 = st.columns(5) with col3: submit_button_holder = st.empty() submit_search = submit_button_holder.button(label='Compare products') if submit_search and not SEARCH_QUERY: st.error(body='Looks like you click a button without a search query. Please enter a search query 👆') st.stop() if submit_search and SEARCH_QUERY and WHERE_TO_SELL: with st.spinner(text='Parsing Product Data...'): comparison_results = [] if WHERE_TO_SELL == 'Walmart': comparison_results = compare_walmart_with_ebay(SEARCH_QUERY, NUMBER_OF_PRODUCTS, PERCENTAGE_OF_UNIQUENESS/100) elif WHERE_TO_SELL == 'eBay': comparison_results = compare_ebay_with_walmart(SEARCH_QUERY, NUMBER_OF_PRODUCTS, PERCENTAGE_OF_UNIQUENESS/100) parsing_is_success = st.success('Done parsing 🎉') time.sleep(1) parsing_is_success.empty() submit_button_holder.empty() comparison_results_header = st.markdown(body='#### Comparison results') if comparison_results: table = create_table(comparison_results, WHERE_TO_SELL) components.html(table, height=len(comparison_results)*62 + 40) time.sleep(1) with col3: start_over_button_holder = st.empty() start_over_button = st.button(label='Start over') # centered button if SAVE_OPTION and comparison_results: with st.spinner(text=f'Saving data to {SAVE_OPTION}...'): if SAVE_OPTION == 'JSON': save_to_json(comparison_results) elif SAVE_OPTION == 'CSV': save_to_csv(comparison_results) saving_is_success = st.success('Done saving 🎉') time.sleep(1) saving_is_success.empty() submit_button_holder.empty() start_over_info_holder = st.empty() start_over_info_holder.error(body='To rerun the script, click on the "Start over" button, or refresh the page.') if start_over_button: comparison_results_header.empty() start_over_button_holder.empty() start_over_info_holder.empty() if SAVE_OPTION and not comparison_results: comparison_results_header.empty() no_data_holder = st.empty() no_data_holder.error(body='No product found. Click "Start Over" button and try different search query.') if start_over_button: no_data_holder.empty() start_over_button_holder.empty() if SAVE_OPTION is None and comparison_results: start_over_info_holder = st.empty() start_over_info_holder.error(body='To rerun the script, click on the "Start over" button, or refresh the page.') if start_over_button: comparison_results_header.empty() start_over_button_holder.empty() start_over_info_holder.empty() if SAVE_OPTION is None and not comparison_results: comparison_results_header.empty() no_data_holder = st.empty() no_data_holder.error(body='No product found. Click "Start Over" button and try different search query.') if start_over_button: comparison_results_header.empty() no_data_holder.empty() start_over_button_holder.empty() if __name__ == '__main__': main()

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