from apify_client import ApifyClient

client = ApifyClient('YOUR_API_TOKEN')

# Run the actor
run = client.actor('pratikdani/company-research-analysis-agent').call(
    run_input={'domain': 'apple.com'}
)

# Get results
results = client.dataset(run['defaultDatasetId']).list_items().items

domains = ['apple.com', 'microsoft.com', 'google.com']

async def process_companies(domains):
    results = []
    for domain in domains:
        run = await client.actor('pratikdani/company-research-analysis-agent').call(
            run_input={'domain': domain}
        )
        results.append(run)
    return results

def process_company_data(data):
    # Extract specific fields
    company_info = {
        'name': data['linkedin_data'].get('name'),
        'funding': data['funding_analysis'].get('total_raised'),
        'employees': data['linkedin_data'].get('employee_count'),
        'recent_news': len(data['recent_news'])
    }
    return company_info

def generate_custom_report(data):
    # Create custom report format
    report = f"""
    # {data['linkedin_data'].get('name')} Analysis
    
    ## Key Metrics
    - Funding: ${data['funding_analysis'].get('total_raised'):,}
    - Employees: {data['linkedin_data'].get('employee_count')}
    - Latest Round: {data['funding_analysis'].get('largest_round', {}).get('type')}
    
    ## Recent News
    {format_news(data['recent_news'])}
    """
    return report

try:
    run = client.actor('pratikdani/company-research-analysis-agent').call(
        run_input={'domain': domain}
    )
except Exception as e:
    logger.error(f"Error processing {domain}: {str(e)}")
    # Implement retry logic or fallback

def validate_results(data):
    required_fields = [
        'linkedin_data',
        'funding_analysis',
        'recent_news'
    ]
    
    return all(field in data for field in required_fields)

import time
from ratelimit import limits, sleep_and_retry

@sleep_and_retry
@limits(calls=30, period=60)
def rate_limited_api_call(domain):
    return client.actor('pratikdani/company-research-analysis-agent').call(
        run_input={'domain': domain}
    )

def analyze_investment_potential(data):
    metrics = {
        'funding_history': data['funding_analysis']['funding_timeline'],
        'growth_rate': calculate_growth_rate(data),
        'market_presence': analyze_market_presence(data),
        'risk_factors': identify_risk_factors(data)
    }
    return metrics

def compare_companies(company_data_list):
    comparison = {
        'funding': {},
        'employees': {},
        'market_presence': {}
    }
    
    for data in company_data_list:
        company = data['linkedin_data']['name']
        comparison['funding'][company] = data['funding_analysis']['total_raised']
        comparison['employees'][company] = data['linkedin_data']['employee_count']
    
    return comparison

def analyze_market_trends(company_data_list):
    trends = {
        'total_funding': sum(d['funding_analysis']['total_raised'] for d in company_data_list),
        'avg_company_size': calculate_average_size(company_data_list),
        'common_investors': find_common_investors(company_data_list)
    }
    return trends