from re import L
import dspy

from dspygen.modules.linkedin_article_module import linkedin_article_call
from dspygen.rm.doc_retriever import DocRetriever
from dspygen.utils.file_tools import count_tokens

task_master = """The following article provides an illustrative exploration of the capabilities and potential applications of the TaskMaster Domain-Specific Language (DSL) in automating the journey of a company from inception to an Initial Public Offering (IPO). The content is based on hypothetical scenarios and use cases designed to showcase the innovative features and benefits of TaskMaster DSL. Any resemblance to actual companies, products, or processes is purely coincidental.
Disclaimer Highlights:
Hypothetical Scenario: The scenarios, companies, and processes described in this article are fictional and meant for illustrative purposes only.
No Actual Endorsement: Mention of specific technologies, companies, or products does not constitute an endorsement or recommendation.
Fictional Implementations: The implementation details and examples provided are speculative and intended to demonstrate the potential of TaskMaster DSL.
No Investment Advice: This article does not provide financial, legal, or investment advice. Readers should seek professional guidance before making any business or investment decisions.
Future Predictions: Any predictions or forward-looking statements about the capabilities or impact of TaskMaster DSL are speculative and should not be taken as guaranteed outcomes.
By continuing to read this article, you acknowledge that it is a speculative exploration of TaskMaster DSL's capabilities and agree to treat the content as a conceptual framework rather than a factual account.
Introduction:
The journey of a startup from inception to an Initial Public Offering (IPO) is fraught with numerous challenges, including developing a robust tech stack, ensuring product-market fit, and maintaining meticulous documentation. TaskMaster DSL, an advanced domain-specific language designed for hyper-automation, has been instrumental in creating the IPO Printer—a groundbreaking tool that automates the entire process of preparing a company for IPO. This article explores how TaskMaster DSL was utilized to implement the IPO Printer, detailing its capabilities and global implications for startups and established enterprises alike.
Understanding the IPO Printer
The IPO Printer is an innovative solution that automates the complex and multifaceted process of preparing a company for an IPO. By leveraging the TaskMaster DSL, the IPO Printer can:
Automate Documentation:
Develop and Optimize Tech Stack:
Ensure Product-Market Fit:
Streamline Compliance:
Key Components and Features
1. Comprehensive Documentation Automation
TaskMaster DSL has been pivotal in automating the creation of essential documents required for an IPO. The system can generate:
SEC Filings: Automatically prepares Form S-1 registration statements, quarterly and annual reports (10-Q, 10-K), and other necessary SEC documents.
Business Plans and Pitch Decks: Creates detailed business plans and professional pitch decks tailored for potential investors.
Due Diligence Reports: Compiles all necessary due diligence documentation, including financial statements, operational reports, and compliance records.
Example TaskMaster DSL Workflow for Document Generation:
system_name: "IPOPrinter"
jobs:
  - name: GenerateSECFiling
    runner: python
    steps:
      - name: CreateFormS1
        code: |
          from document_generator import create_form_s1
          create_form_s1(company_data)
      - name: GenerateQuarterlyReports
        code: |
          from document_generator import generate_reports
          generate_reports('10-Q', quarterly_data)

  - name: CreateBusinessPlan
    runner: python
    steps:
      - name: CompileBusinessPlan
        code: |
          from business_plan_creator import compile_plan
          compile_plan(company_strategy, market_analysis)

  - name: CompileDueDiligence
    runner: python
    steps:
      - name: GatherFinancials
        code: |
          from due_diligence import gather_financials
          gather_financials(financial_data)
      - name: CreateOperationalReport
        code: |
          from due_diligence import create_operational_report
          create_operational_report(operational_data)
2. Developing and Optimizing Tech Stack
TaskMaster DSL ensures that the tech stack is robust, scalable, and secure, crucial for the operational success of a company heading towards an IPO. It includes:
Infrastructure Automation: Sets up cloud infrastructure using IaC (Infrastructure as Code) principles.
CI/CD Pipelines: Implements automated pipelines for continuous integration and delivery.
Monitoring and Logging: Establishes comprehensive monitoring and logging systems to ensure operational reliability.
Example TaskMaster DSL Workflow for Tech Stack Development:
system_name: "IPOPrinter"
jobs:
  - name: SetupInfrastructure
    runner: python
    steps:
      - name: ProvisionCloudResources
        code: |
          from infrastructure import provision_resources
          provision_resources(cloud_config)

  - name: ImplementCICD
    runner: python
    steps:
      - name: SetupCIPipeline
        code: |
          from cicd import setup_ci_pipeline
          setup_ci_pipeline(repo_url, ci_config)
      - name: ConfigureCDPipeline
        code: |
          from cicd import configure_cd_pipeline
          configure_cd_pipeline(cd_config)

  - name: SetupMonitoring
    runner: python
    steps:
      - name: EstablishMonitoring
        code: |
          from monitoring import setup_monitoring
          setup_monitoring(monitoring_tools)
      - name: ConfigureLogging
        code: |
          from logging import configure_logging
          configure_logging(logging_tools)
3. Ensuring Product-Market Fit
TaskMaster DSL integrates AI-driven tools to analyze market trends and customer feedback, ensuring that the product offerings are aligned with market demands.
Market Analysis: Uses machine learning algorithms to analyze market trends and predict customer preferences.
Customer Feedback Automation: Collects and analyzes customer feedback to drive product improvements.
Example TaskMaster DSL Workflow for Product-Market Fit:
system_name: "IPOPrinter"
jobs:
  - name: AnalyzeMarket
    runner: python
    steps:
      - name: PerformMarketAnalysis
        code: |
          from market_analysis import analyze_market
          market_data = analyze_market(industry, competitors)

  - name: CollectCustomerFeedback
    runner: python
    steps:
      - name: GatherFeedback
        code: |
          from customer_feedback import collect_feedback
          feedback_data = collect_feedback(customer_interactions)
      - name: AnalyzeFeedback
        code: |
          from customer_feedback import analyze_feedback
          insights = analyze_feedback(feedback_data)
4. Streamlining Compliance
TaskMaster DSL automates compliance checks, ensuring that all regulatory requirements are met consistently.
Compliance Audits: Automatically conducts regular audits to ensure regulatory compliance.
Documentation and Record-Keeping: Maintains comprehensive records of all compliance-related activities.
Example TaskMaster DSL Workflow for Compliance:
system_name: "IPOPrinter"
jobs:
  - name: ConductComplianceAudit
    runner: python
    steps:
      - name: PerformAudit
        code: |
          from compliance import perform_audit
          audit_results = perform_audit(compliance_checklist)

  - name: MaintainRecords
    runner: python
    steps:
      - name: UpdateComplianceRecords
        code: |
          from record_keeping import update_records
          update_records(audit_results, compliance_documents)
Conclusion
The IPO Printer, powered by TaskMaster DSL, represents a revolutionary approach to preparing companies for IPOs. By automating critical processes, from documentation to tech stack development, and ensuring product-market fit and compliance, TaskMaster DSL enables companies to navigate the complex journey to an IPO with unprecedented efficiency and confidence. This groundbreaking tool not only accelerates the IPO process but also sets a new standard for enterprise automation, empowering businesses to achieve their full potential in the global market.

"""

def main():
    """Main function"""
    from dspygen.utils.dspy_tools import init_ol
    init_ol(model="phi3:14b-instruct")
    dspy.configure(experimental=True)
    # art = linkedin_article_call("Business in 2030")
    # print(art)
    import os
    from pathlib import Path

    def list_pdfs_in_downloads():
        downloads_path = Path.home() / "Downloads"
        pdf_files = [file for file in downloads_path.glob("*.pdf")]
        # Sort the PDF files by size (largest to smallest)
        pdf_files.sort(key=lambda x: x.stat().st_size, reverse=True)
        return [str(file.absolute()) for file in pdf_files]

    pdf_list = list_pdfs_in_downloads()
    print("List of PDFs in Downloads folder (sorted by size, largest to smallest):")
    for pdf_path in pdf_list:
        size_mb = Path(pdf_path).stat().st_size / (1024 * 1024)  # Convert to MB
        print(f"{pdf_path} - {size_mb:.2f} MB")

        dr = DocRetriever(pdf_path)
        # print(count_tokens(dr.forward()))
        text = dr.forward()

        art2 = linkedin_article_call(text + task_master)
        print(art2)
        # Get the PDF file name without extension
        pdf_name = Path(pdf_path).stem

        # Create a safe filename by replacing spaces with underscores and removing special characters
        safe_filename = ''.join(c if c.isalnum() or c in ('-', '_') else '_' for c in pdf_name)

        # Create the output file path
        output_file = Path(os.getcwd()) / f"{safe_filename}_linkedin_article.txt"

        # Write the article to the file
        with open(output_file, 'w', encoding='utf-8') as f:
            f.write(art2)

        print(f"Article saved to: {output_file}")

        

if __name__ == '__main__':
    main()
