import requests
from bs4 import BeautifulSoup
import pandas as pd
import gradio as gr

def scrape_crunchbase(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.content, 'html.parser')
    
    # Extract relevant information
    company_name = soup.find('h1').text.strip() if soup.find('h1') else 'N/A'
    short_description = soup.find('meta', attrs={'name': 'description'})['content'] if soup.find('meta', attrs={'name': 'description'}) else 'N/A'
    founded_on = soup.find('span', text='Founded').find_next('span').text.strip() if soup.find('span', text='Founded') else 'N/A'
    ipo_status = soup.find('span', text='IPO Status').find_next('span').text.strip() if soup.find('span', text='IPO Status') else 'N/A'
    contact_email = soup.find('a', href=lambda href: href and 'mailto:' in href)['href'].replace('mailto:', '') if soup.find('a', href=lambda href: href and 'mailto:' in href) else 'N/A'
    legal_name = soup.find('span', text='Legal Name').find_next('span').text.strip() if soup.find('span', text='Legal Name') else 'N/A'
    website = soup.find('a', href=lambda href: href and 'http' in href)['href'] if soup.find('a', href=lambda href: href and 'http' in href) else 'N/A'
    city = soup.find('span', text='City').find_next('span').text.strip() if soup.find('span', text='City') else 'N/A'
    region = soup.find('span', text='Region').find_next('span').text.strip() if soup.find('span', text='Region') else 'N/A'
    country = soup.find('span', text='Country').find_next('span').text.strip() if soup.find('span', text='Country') else 'N/A'
    continent = soup.find('span', text='Continent').find_next('span').text.strip() if soup.find('span', text='Continent') else 'N/A'
    rank_org_company = soup.find('span', text='Rank Org Company').find_next('span').text.strip() if soup.find('span', text='Rank Org Company') else 'N/A'
    operating_status = soup.find('span', text='Operating Status').find_next('span').text.strip() if soup.find('span', text='Operating Status') else 'N/A'
    last_funding_type = soup.find('span', text='Last Funding Type').find_next('span').text.strip() if soup.find('span', text='Last Funding Type') else 'N/A'
    total_rounds = soup.find('span', text='Total Rounds').find_next('span').text.strip() if soup.find('span', text='Total Rounds') else 'N/A'
    total_investors = soup.find('span', text='Total Investors').find_next('span').text.strip() if soup.find('span', text='Total Investors') else 'N/A'
    total_money_raised_usd = soup.find('span', text='Total Money Raised USD').find_next('span').text.strip() if soup.find('span', text='Total Money Raised USD') else 'N/A'
    last_round_money_raised_usd = soup.find('span', text='Last Round Money Raised USD').find_next('span').text.strip() if soup.find('span', text='Last Round Money Raised USD') else 'N/A'
    most_recent_funding_date = soup.find('span', text='Most Recent Funding Date').find_next('span').text.strip() if soup.find('span', text='Most Recent Funding Date') else 'N/A'
    industries = soup.find('span', text='Industries').find_next('span').text.strip() if soup.find('span', text='Industries') else 'N/A'
    similar_companies_permalinks = soup.find('span', text='Similar Companies Permalinks').find_next('span').text.strip() if soup.find('span', text='Similar Companies Permalinks') else 'N/A'
    min_employees = soup.find('span', text='Min Employees').find_next('span').text.strip() if soup.find('span', text='Min Employees') else 'N/A'
    max_employees = soup.find('span', text='Max Employees').find_next('span').text.strip() if soup.find('span', text='Max Employees') else 'N/A'
    max_score = soup.find('span', text='Max Score').find_next('span').text.strip() if soup.find('span', text='Max Score') else 'N/A'
    
    # Create a dictionary with the extracted data
    data = {
        'Url': url,
        'Company Name': company_name,
        'Short Description': short_description,
        'Founded On': founded_on,
        'Ipo Status': ipo_status,
        'Contact Email': contact_email,
        'Legal Name': legal_name,
        'Website': website,
        'City': city,
        'Region': region,
        'Country': country,
        'Continent': continent,
        'Rank Org Company': rank_org_company,
        'Operating Status': operating_status,
        'Last Funding Type': last_funding_type,
        'Total Rounds': total_rounds,
        'Total Investors': total_investors,
        'Total Money Raised USD': total_money_raised_usd,
        'Last Round Money Raised USD': last_round_money_raised_usd,
        'Most Recent Funding Date': most_recent_funding_date,
        'Industries': industries,
        'Similar Companies Permalinks': similar_companies_permalinks,
        'Min Employees': min_employees,
        'Max Employees': max_employees,
        'Max Score': max_score
    }
    
    # Convert the dictionary to a DataFrame
    df = pd.DataFrame([data])
    
    return df

def scrape_and_display(url):
    df = scrape_crunchbase(url)
    return df

# Create a Gradio interface
iface = gr.Interface(
    fn=scrape_and_display,
    inputs="text",
    outputs="dataframe",
    title="Crunchbase Scraper",
    description="Enter a Crunchbase URL to scrape company information."
)

iface.launch()