import os
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from typing import List
from pydantic import BaseModel, Field
import markdown2
import pdfkit

# 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, competitor_analysis: str):
    query = f"""
    You are an AI business strategist analyzing a company's potential AI adoption. Given the following:
    - **Company Overview:** {company_data}
    - **Industry Trends:** {industry_trends}
    - **AI Use Cases:** {ai_use_cases}
    - **Competitor AI Strategies:** {competitor_analysis}
    Generate a structured AI adoption strategy including key opportunities, recommended AI tools, implementation roadmap, and future scalability.
    """
    response = reasoning_agent.run(query)
    return response.content 
    


##############################
# 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):
    query = f"""
    Based on the AI adoption strategy:
    - **Company Context:** {company_data}
    - **AI Strategy Summary:** {ai_strategy}
    Provide a structured AI implementation plan including step-by-step integration, required technologies, workforce training, risk considerations, and key performance indicators.
    """
    response = ai_integration_agent.run(query)
    return response.content

##############################
# 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):
    query = f"""
    You are an AI business analyst tasked with identifying AI-driven revenue growth opportunities for:
    - **Company Overview:** {company_data}
    - **AI Strategy:** {ai_strategy}
    Provide AI monetization strategies, cost-saving efficiencies, market expansion possibilities, and competitive positioning tactics.
    """
    response = revenue_growth_agent.run(query)
    return response.content  # Return the revenue opportunities


##############################
# 4️⃣ Report Generation Agent #
##############################
def generate_report(company_name: str, ai_strategy: str, ai_integration: str, revenue_opportunities: str):
    report_content = f"""
    # AI Strategy Report for {company_name}
    
    ## AI Adoption Strategy
    {ai_strategy}
    
    ## AI Implementation Plan
    {ai_integration}
    
    ## Revenue Growth Opportunities
    {revenue_opportunities}
    
    """
    
    # Convert to Markdown
    markdown_report = markdown2.markdown(report_content)

    # Define the path to wkhtmltopdf configuration
    config = pdfkit.configuration(wkhtmltopdf="/usr/bin/wkhtmltopdf")

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