app.py

import advertools as adv
import streamlit as st
import tempfile
import pandas as pd
from urllib.parse import urlparse
import base64
import requests
import time
from bs4 import BeautifulSoup
import re
#
def get_seo_powersuite_data(domains, api_key):
    url_domain_inlink_rank = "https://api.seopowersuite.com/backlinks/v1.0/get-domain-inlink-rank"
    url_refdomains_count = "https://api.seopowersuite.com/backlinks/v1.0/get-refdomains-count"
    headers = {"Content-Type": "application/json"}
    
    results = []
    for i in range(0, len(domains), 100):
        batch_domains = domains[i:i+100]
        
        # Get domain inlink rank
        start_time = time.time()
        payload_domain_inlink_rank = {"target": list(batch_domains)}
        params_domain_inlink_rank = {"apikey": api_key, "output": "json"}
        response_domain_inlink_rank = requests.post(url_domain_inlink_rank, json=payload_domain_inlink_rank, headers=headers, params=params_domain_inlink_rank)
        duration = time.time() - start_time
        print(f"get-domain-inlink-rank API call for {len(batch_domains)} domains took {duration:.2f} seconds")
        
        if response_domain_inlink_rank.status_code == 200:
            data_domain_inlink_rank = response_domain_inlink_rank.json()
            domain_inlink_rank_dict = {page["url"]: page["domain_inlink_rank"] for page in data_domain_inlink_rank["pages"]}
        else:
            st.error(f"Error fetching domain inlink rank data from SEO PowerSuite API: {response_domain_inlink_rank.status_code}")
            st.error("Error Response:")
            st.write(response_domain_inlink_rank.text)
            return None
        
        # Get refdomains count
        start_time = time.time()
        payload_refdomains_count = {"target": list(batch_domains), "mode": "domain"}
        params_refdomains_count = {"apikey": api_key, "output": "json"}
        response_refdomains_count = requests.post(url_refdomains_count, json=payload_refdomains_count, headers=headers, params=params_refdomains_count)
        duration = time.time() - start_time
        print(f"get-refdomains-count API call for {len(batch_domains)} domains took {duration:.2f} seconds")
        
        if response_refdomains_count.status_code == 200:
            data_refdomains_count = response_refdomains_count.json()
            for metric in data_refdomains_count["metrics"]:
                result = {
                    "target": metric["target"],
                    "domain_inlink_rank": domain_inlink_rank_dict.get(metric["target"], None),
                    "refdomains": metric["refdomains"]
                }
                results.append(result)
        else:
            st.error(f"Error fetching refdomains count data from SEO PowerSuite API: {response_refdomains_count.status_code}")
            st.error("Error Response:")
            st.write(response_refdomains_count.text)
            return None
        
    return pd.DataFrame(results)

def get_peter_lowe_domains():
    url = "https://pgl.yoyo.org/adservers/serverlist.php?hostformat=adblockplus&mimetype=plaintext"
    response = requests.get(url)
    lines = response.text.split('\n')
    domains = [line.strip('|^') for line in lines if line.startswith('||')]
    return set(domains)

def extract_hostname(url):
    return urlparse(url).netloc

def remove_subdomain(domain):
    parts = domain.split('.')
    if len(parts) > 2:
        return '.'.join(parts[-2:])
    return domain

def domain_matches_blacklist(domain, regex_patterns):
    for pattern in regex_patterns:
        if re.search(pattern, domain, re.IGNORECASE):
            return 'Yes'
    return 'No'

def find_sitemap(url):
    robots_url = f"{urlparse(url).scheme}://{urlparse(url).netloc}/robots.txt"
    try:
        robots_response = requests.get(robots_url)
        if robots_response.status_code == 200:
            for line in robots_response.text.split("\n"):
                if line.startswith("Sitemap:"):
                    return line.split(":", 1)[1].strip()
    except requests.exceptions.RequestException:
        pass
        
    sitemap_urls = [
        "/sitemap.xml", "/wp-sitemap.xml", "/?sitemap=1", "/sitemap_index/xml",
        "/sitemap-index.xml", "/sitemap.php", "/sitemap.txt", "/sitemap.xml.gz",
        "/sitemap/", "/sitemap/sitemap.xml", "/sitemapindex.xml", "/sitemap/index.xml", "/sitemap1.xml"
    ]
    
    for sitemap_url in sitemap_urls:
        try:
            sitemap_response = requests.get(f"{urlparse(url).scheme}://{urlparse(url).netloc}{sitemap_url}")
            if sitemap_response.status_code == 200:
                return f"{urlparse(url).scheme}://{urlparse(url).netloc}{sitemap_url}"
        except requests.exceptions.RequestException:
            pass
            
    return None

def crawl_posts(df, page_count):
    crawl_results = []
    for i, row in df.head(page_count).iterrows():
        url = row['loc']
        try:
            response = requests.get(url)
            if response.status_code == 200:
                html = response.text
                soup = BeautifulSoup(html, 'html.parser')
                title = soup.title.text if soup.title else ''
                meta_desc = soup.find('meta', attrs={'name': 'description'})['content'] if soup.find('meta', attrs={'name': 'description'}) else ''
                links = []
                for a in soup.find_all('a', href=True):
                    link_url = a['href']
                    link_text = a.text.strip()
                    link_nofollow = 'nofollow' in a.get('rel', [])
                    links.append({'url': link_url, 'text': link_text, 'nofollow': link_nofollow})
                crawl_results.append({
                    'url': url,
                    'title': title,
                    'meta_desc': meta_desc,
                    'links': links
                })
        except requests.exceptions.RequestException:
            pass
    return pd.DataFrame(crawl_results)

def download_csv(df, filename):
    csv = df.to_csv(index=False)
    b64 = base64.b64encode(csv.encode()).decode()
    href = f'<a href="data:file/csv;base64,{b64}" download="{filename}.csv">Download {filename} CSV</a>'
    return href

def main():
    st.title("Website Crawler")
    
    urls = st.text_area("Enter the website URLs (one per line):", value="")
    page_count = st.number_input("Enter the number of pages to crawl:", value=2000, min_value=1, step=1)
    
    col1, col2 = st.columns(2)
    with col1:
        domain_filter_regex_input = st.text_area("Filter out Unique Outbound Domains:", help="This uses a regex filter to find domains in the unique outbound domains list. Enter one regex per line.", value="instagram\nfacebook\ntwitter\nlinkedin\nsnapchat\ntiktok\nreddit\npinterest\namazon\ncdn\nyoutube\nyoutu.be")
    with col2:
        domain_match_regex_input = st.text_area("Domain Blacklist:", help="This uses a regex filter to match domains in the Unique Outbound Domains to the blacklist entered. Enter one regex per line.", value="xyz\ncasino\ncbd\nessay")
    use_seo_powersuite = st.checkbox("Use SEO PowerSuite")
    api_key = None
    if use_seo_powersuite:
        api_key = st.text_input("Enter the SEO PowerSuite API key:", type="password")
    download_links = st.checkbox("Show Download Links")
    
    if st.button("Crawl"):
        if urls:
            url_list = [url.strip() for url in urls.split('\n') if url.strip()]
            if url_list:
                all_link_df = pd.DataFrame()
                all_unique_outbound_links_df = pd.DataFrame()
                all_final_df = pd.DataFrame()
                all_analysis_df = pd.DataFrame()
                
                for url in url_list:
                    with st.spinner(f"Finding sitemap for {url}..."):
                        sitemap_url = find_sitemap(url)
                        if sitemap_url:
                            with st.spinner(f"Crawling {url}..."):
                                sitemap_df = adv.sitemap_to_df(sitemap_url)
                                crawl_results = crawl_posts(sitemap_df, page_count)
                                
                                if not crawl_results.empty:
                                    link_df = pd.DataFrame(crawl_results['links'].explode().tolist())
                                    link_df = link_df[~link_df['url'].str.startswith(('/','#'))]
                                    link_df['internal'] = link_df['url'].apply(lambda x: extract_hostname(url) in extract_hostname(x))
                                    link_df = link_df[link_df['internal'] == False]  # Filter out internal links
                                    link_df.insert(0, 'Originating Domain', url)  # Add 'Originating Domain' column
                                    link_df = link_df[['Originating Domain', 'url', 'text', 'nofollow']]  # Remove the 'internal' column
                                    
                                    outbound_links_df = link_df.copy()  # Create a copy of link_df for outbound links                            
                                    unique_links_df = link_df['url'].value_counts().reset_index()
                                    unique_links_df = unique_links_df[~unique_links_df['url'].str.startswith(('/','#'))]
                                    unique_links_df.columns = ['Link', 'Count']
                                    unique_links_df.insert(0, 'Originating Domain', url)
                                    
                                    unique_outbound_links_df = outbound_links_df['url'].value_counts().reset_index()
                                    unique_outbound_links_df = unique_outbound_links_df[~unique_outbound_links_df['url'].str.startswith(('/','#'))]
                                    unique_outbound_links_df.columns = ['Link', 'Count']
                                    unique_outbound_links_df.insert(0, 'Originating Domain', url)
                                    
                                    outbound_links_df['url'] = outbound_links_df['url'].astype(str)
                                    domain_df = outbound_links_df['url'].apply(extract_hostname).value_counts().reset_index()
                                    domain_df.columns = ['Domain', 'Count']
                                    domain_df = domain_df[domain_df['Domain'] != '']
                                    peter_lowe_domains = get_peter_lowe_domains()
                                    domain_df['In Peter Lowe List'] = domain_df['Domain'].apply(lambda x: 'Yes' if remove_subdomain(x) in peter_lowe_domains else 'No')
                                    domain_df.insert(0, 'Originating Domain', url)
                                    
                                    # Determine the 'DoFollow' value for each domain
                                    domain_df['DoFollow'] = domain_df['Domain'].apply(lambda x: any(outbound_links_df[(outbound_links_df['url'].str.contains(x)) & (outbound_links_df['nofollow'] == False)]))
                                    
                                    if not domain_df.empty:
                                        if domain_filter_regex_input:
                                            domain_filter_regex_patterns = domain_filter_regex_input.split('\n')
                                            domain_filter_regex = '|'.join(domain_filter_regex_patterns)
                                            domain_df = domain_df[~domain_df['Domain'].str.contains(domain_filter_regex, case=False, regex=True)]
                                            
                                        if not domain_df.empty:
                                            if domain_match_regex_input:
                                                domain_match_regex_patterns = domain_match_regex_input.split('\n')
                                                domain_df['Blacklist'] = domain_df['Domain'].apply(lambda x: domain_matches_blacklist(x, domain_match_regex_patterns) == 'Yes')
                                            else:
                                                domain_df['Blacklist'] = False
                                                
                                            total_domains = len(domain_df)
                                            peter_lowe_percentage = round((domain_df['In Peter Lowe List'] == 'No').sum() / total_domains * 100, 2)
                                            blacklist_percentage = round((domain_df['Blacklist'] == True).sum() / total_domains * 100, 2)
                                            
                                            analysis_data = {
                                                'Originating Domain': [url] * 2,
                                                'Metric': ['Percentage of domains not in Peter Lowe\'s list', 'Percentage of domains in the Blacklist'],
                                                'Value': [f"{peter_lowe_percentage}%", f"{blacklist_percentage}%"]
                                            }
                                            
                                            analysis_df = pd.DataFrame(analysis_data)
                                            
                                            if use_seo_powersuite and api_key:
                                                seo_powersuite_df = get_seo_powersuite_data(domain_df['Domain'].tolist(), api_key)
                                                if seo_powersuite_df is not None:
                                                    domain_df = pd.merge(domain_df, seo_powersuite_df, left_on='Domain', right_on='target', how='left')
                                                    domain_df.drop('target', axis=1, inplace=True)
                                                    
                                                    avg_domain_inlink_rank = round(domain_df['domain_inlink_rank'].mean(), 2)
                                                    avg_domain_inlink_rank_less_than_70 = round(domain_df[domain_df['domain_inlink_rank'] < 70]['domain_inlink_rank'].mean(), 2)
                                                    avg_refdomains = round(domain_df['refdomains'].mean(), 2)
                                                    
                                                    additional_analysis_data = {
                                                        'Originating Domain': [url] * 3,
                                                        'Metric': [
                                                            'Average domain inlink rank',
                                                            'Average domain inlink rank (< 70)',
                                                            'Average number of refdomains'
                                                        ],
                                                        'Value': [
                                                            avg_domain_inlink_rank,
                                                            avg_domain_inlink_rank_less_than_70,
                                                            avg_refdomains
                                                        ]
                                                    }
                                                    
                                                    analysis_df = pd.concat([analysis_df, pd.DataFrame(additional_analysis_data)], ignore_index=True)
                                                    
                                                    desired_columns = ['Originating Domain', 'Domain', 'Count', 'In Peter Lowe List', 'DoFollow', 'Blacklist', 'domain_inlink_rank', 'refdomains']
                                                    final_df = domain_df[desired_columns]
                                            else:
                                                desired_columns = ['Originating Domain', 'Domain', 'Count', 'In Peter Lowe List', 'DoFollow', 'Blacklist']
                                                final_df = domain_df[desired_columns]
                                        else:
                                            st.warning(f"No unique outbound domains found for {url} after filtering.")
                                    else:
                                        st.warning(f"No unique outbound domains found for {url}.")
                                        
                                    all_link_df = pd.concat([all_link_df, link_df], ignore_index=True)
                                    all_unique_outbound_links_df = pd.concat([all_unique_outbound_links_df, unique_outbound_links_df], ignore_index=True)
                                    all_final_df = pd.concat([all_final_df, final_df], ignore_index=True)
                                    all_analysis_df = pd.concat([all_analysis_df, analysis_df], ignore_index=True)
                                else:
                                    st.warning(f"No posts found in the sitemap for {url}.")
                        else:
                            st.warning(f"Sitemap not found for {url}.")
                            
                st.subheader("Outbound Links")
                if download_links:
                    st.markdown(download_csv(all_link_df, "Outbound Links"), unsafe_allow_html=True)
                else:
                    st.write(all_link_df)
                    
                st.subheader("Unique Outbound Links")
                if download_links:
                    st.markdown(download_csv(all_unique_outbound_links_df, "Unique Outbound Links"), unsafe_allow_html=True)
                else:
                    st.write(all_unique_outbound_links_df)
                    
                st.subheader("Unique Outbound Domains")
                if download_links:
                    st.markdown(download_csv(all_final_df, "Unique Outbound Domains"), unsafe_allow_html=True)
                else:
                    st.write(all_final_df)
                    
                st.subheader("Analytics")
                all_analysis_df = all_analysis_df.pivot(index='Originating Domain', columns='Metric', values='Value').reset_index()
                all_analysis_df.columns.name = None
                if use_seo_powersuite and api_key:
                    numeric_columns = ['Average domain inlink rank', 'Average domain inlink rank (< 70)', 'Average number of refdomains']
                    all_analysis_df[numeric_columns] = all_analysis_df[numeric_columns].astype(int)
                if download_links:
                    st.markdown(download_csv(all_analysis_df, "Analytics"), unsafe_allow_html=True)
                else:
                    st.table(all_analysis_df)
            else:
                st.warning("Please enter at least one website URL.")
        else:
            st.warning("Please enter website URLs.")
            
if __name__ == '__main__':
    main()