phase3_agents.py

import os
from phi.agent import Agent
from phi.model.openai import OpenAIChat
import markdown2
import pdfkit
from bni_agent import get_bni_benefits
from rag_agent import recommend_bni_connections

# Load environment variables (API keys, etc.)
from dotenv import load_dotenv
load_dotenv()

#####################################################################################
#                                    PHASE 3                                        #
#####################################################################################

##############################
# 1️⃣ Reasoning Agent        #
##############################
reasoning_agent = Agent(
    name="Reasoning Agent",
    model=OpenAIChat(id="gpt-4o"),
    description="Processes all collected data and generates structured AI adoption strategies.",
    show_tool_calls=True,
    markdown=True,
)

def generate_ai_strategy(company_data: str, industry_trends: str, ai_use_cases: str) -> str:
    """Generates a structured AI adoption strategy based on company details and industry insights."""
    query = f"""
    You are an AI business strategist analyzing a company's AI adoption potential. Given:
    - **Company Overview:** {company_data}
    - **Industry Trends:** {industry_trends}
    - **AI Use Cases:** {ai_use_cases}
    
    Generate a structured AI strategy including:
    1. Key AI Opportunities
    2. Recommended AI Tools & Technologies
    3. AI Implementation Roadmap
    4. Future Scalability Plan
    """
    response = reasoning_agent.run(query)
    return response.content  # Returns the generated AI strategy


##############################
# 2️⃣ AI Integration Advisor  #
##############################
ai_integration_agent = Agent(
    name="AI Integration Advisor",
    model=OpenAIChat(id="gpt-4o"),
    description="Suggests AI implementation strategies based on industry insights and company operations.",
    show_tool_calls=True,
    markdown=True,
)

def suggest_ai_integration(company_data: str, ai_strategy: str) -> str:
    """Suggests a structured AI implementation plan."""
    query = f"""
    Based on the AI adoption strategy:
    - **Company Context:** {company_data}
    - **AI Strategy Summary:** {ai_strategy}

    Provide a structured AI implementation plan including:
    1. Step-by-step AI adoption process
    2. Required AI Technologies & Infrastructure
    3. Workforce training & AI skill development
    4. Risk considerations (data security, compliance, ethical AI)
    5. Key performance indicators (KPIs) for AI success.
    """
    response = ai_integration_agent.run(query)
    return response.content  # Returns AI integration plan


##############################
# 3️⃣ Revenue Growth Agent    #
##############################
revenue_growth_agent = Agent(
    name="Revenue Growth Agent",
    model=OpenAIChat(id="gpt-4o"),
    description="Identifies AI-driven opportunities to enhance revenue and efficiency.",
    show_tool_calls=True,
    markdown=True,
)

def identify_revenue_opportunities(company_data: str, ai_strategy: str) -> str:
    """Identifies AI-driven revenue generation opportunities for a business."""
    query = f"""
    You are an AI business analyst identifying AI-driven revenue growth opportunities for:
    - **Company Overview:** {company_data}
    - **AI Strategy:** {ai_strategy}

    Provide:
    1. AI-based Monetization Strategies (new revenue streams)
    2. Cost Reduction & Operational Efficiency
    3. Market Expansion through AI-driven solutions
    4. Competitive Positioning & Differentiation using AI.
    """
    response = revenue_growth_agent.run(query)
    return response.content  # Returns revenue opportunities


##############################
# 4️⃣ BNI Agent: Membership Benefits #
##############################
def get_bni_membership_benefits(company_data: str) -> str:
    """Fetches BNI membership benefits tailored to the user's company."""
    return get_bni_benefits(company_data)


##############################
# 5️⃣ BNI RAG Agent: Pearl Chapter Connections #
##############################
def get_bni_recommendations(company_data: str) -> str:
    """Recommends relevant BNI Pearl Chapter members based on the user's company data."""
    return recommend_bni_connections(company_data)


##############################
# 6️⃣ Report Generation Agent #
##############################
def generate_report(company_name: str, company_data: str, industry_trends: str, ai_use_cases: str,
                    ai_strategy: str, ai_integration: str, revenue_opportunities: str,
                    bni_benefits: str, bni_recommendations: str) -> str:
    """
    Generates a structured AI strategy report in PDF format using `pdfkit`.
    
    Returns:
        str: Filename of the generated PDF.
    """
    report_content = f"""
    # AI Strategy Report for {company_name}
    
    ## 1️⃣ Company Overview
    {company_data}
    
    ## 2️⃣ Industry Trends
    {industry_trends}
    
    ## 3️⃣ AI Use Cases
    {ai_use_cases}
    
    ## 4️⃣ AI Adoption Strategy
    {ai_strategy}
    
    ## 5️⃣ AI Implementation Plan
    {ai_integration}
    
    ## 6️⃣ Revenue Growth Opportunities
    {revenue_opportunities}

    ## 7️⃣ How BNI Can Help Your Business
    {bni_benefits}
    
    ## 8️⃣ Recommended BNI Pearl Chapter Connections
    {bni_recommendations}
    
    """

    # Convert to Markdown
    markdown_report = markdown2.markdown(report_content)

    # Convert Markdown to PDF (Ensure wkhtmltopdf is installed)
    pdf_filename = f"{company_name}_AI_Report.pdf"
    pdfkit.from_string(markdown_report, pdf_filename)

    return pdf_filename  # Returns the generated PDF filename