Upload app.py

app.py

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+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+import gradio as gr
+
+# Function to scrape Crunchbase for companies matching the description
+def scrape_crunchbase(description):
+    # Simulate a search query on Crunchbase
+    search_url = f"https://www.crunchbase.com/textsearch?q={description}"
+    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"
+    }
+    response = requests.get(search_url, headers=headers)
+    soup = BeautifulSoup(response.content, 'html.parser')
+    
+    # Extract company details
+    companies = []
+    for item in soup.find_all('div', class_='result-info'):  # Adjust based on Crunchbase's HTML structure
+        company_name = item.find('a', class_='result-name').text.strip()
+        company_url = "https://www.crunchbase.com" + item.find('a', class_='result-name')['href']
+        
+        # Fetch additional details from the company's Crunchbase page
+        company_response = requests.get(company_url, headers=headers)
+        company_soup = BeautifulSoup(company_response.content, 'html.parser')
+        
+        # Extract relevant fields
+        short_description = company_soup.find('meta', attrs={'name': 'description'})['content'] if company_soup.find('meta', attrs={'name': 'description'}) else 'N/A'
+        founded_on = company_soup.find('span', text='Founded').find_next('span').text.strip() if company_soup.find('span', text='Founded') else 'N/A'
+        ipo_status = company_soup.find('span', text='IPO Status').find_next('span').text.strip() if company_soup.find('span', text='IPO Status') else 'N/A'
+        contact_email = company_soup.find('a', href=lambda href: href and 'mailto:' in href)['href'].replace('mailto:', '') if company_soup.find('a', href=lambda href: href and 'mailto:' in href) else 'N/A'
+        legal_name = company_soup.find('span', text='Legal Name').find_next('span').text.strip() if company_soup.find('span', text='Legal Name') else 'N/A'
+        website = company_soup.find('a', href=lambda href: href and 'http' in href)['href'] if company_soup.find('a', href=lambda href: href and 'http' in href) else 'N/A'
+        city = company_soup.find('span', text='City').find_next('span').text.strip() if company_soup.find('span', text='City') else 'N/A'
+        region = company_soup.find('span', text='Region').find_next('span').text.strip() if company_soup.find('span', text='Region') else 'N/A'
+        country = company_soup.find('span', text='Country').find_next('span').text.strip() if company_soup.find('span', text='Country') else 'N/A'
+        continent = company_soup.find('span', text='Continent').find_next('span').text.strip() if company_soup.find('span', text='Continent') else 'N/A'
+        rank_org_company = company_soup.find('span', text='Rank Org Company').find_next('span').text.strip() if company_soup.find('span', text='Rank Org Company') else 'N/A'
+        operating_status = company_soup.find('span', text='Operating Status').find_next('span').text.strip() if company_soup.find('span', text='Operating Status') else 'N/A'
+        last_funding_type = company_soup.find('span', text='Last Funding Type').find_next('span').text.strip() if company_soup.find('span', text='Last Funding Type') else 'N/A'
+        total_rounds = company_soup.find('span', text='Total Rounds').find_next('span').text.strip() if company_soup.find('span', text='Total Rounds') else 'N/A'
+        total_investors = company_soup.find('span', text='Total Investors').find_next('span').text.strip() if company_soup.find('span', text='Total Investors') else 'N/A'
+        total_money_raised_usd = company_soup.find('span', text='Total Money Raised USD').find_next('span').text.strip() if company_soup.find('span', text='Total Money Raised USD') else 'N/A'
+        last_round_money_raised_usd = company_soup.find('span', text='Last Round Money Raised USD').find_next('span').text.strip() if company_soup.find('span', text='Last Round Money Raised USD') else 'N/A'
+        most_recent_funding_date = company_soup.find('span', text='Most Recent Funding Date').find_next('span').text.strip() if company_soup.find('span', text='Most Recent Funding Date') else 'N/A'
+        industries = company_soup.find('span', text='Industries').find_next('span').text.strip() if company_soup.find('span', text='Industries') else 'N/A'
+        similar_companies_permalinks = company_soup.find('span', text='Similar Companies Permalinks').find_next('span').text.strip() if company_soup.find('span', text='Similar Companies Permalinks') else 'N/A'
+        min_employees = company_soup.find('span', text='Min Employees').find_next('span').text.strip() if company_soup.find('span', text='Min Employees') else 'N/A'
+        max_employees = company_soup.find('span', text='Max Employees').find_next('span').text.strip() if company_soup.find('span', text='Max Employees') else 'N/A'
+        max_score = company_soup.find('span', text='Max Score').find_next('span').text.strip() if company_soup.find('span', text='Max Score') else 'N/A'
+        
+        # Add company details to the list
+        companies.append({
+            'Url': company_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 list to a DataFrame
+    df = pd.DataFrame(companies)
+    return df.head(5)  # Return the top 5 companies
+
+# Gradio Interface
+def gradio_interface(description):
+    df = scrape_crunchbase(description)
+    return df
+
+# Launch Gradio App
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs="text",
+    outputs="dataframe",
+    title="Crunchbase Company Search",
+    description="Enter a company services description to find the top 5 matching companies on Crunchbase."
+)
+
+iface.launch()