agents/agents.py

import json
import os
import requests
from dotenv import load_dotenv
from openai import OpenAI

# Load environment variables
load_dotenv()

# Initialize API clients
openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) if os.getenv("OPENAI_API_KEY") else None
ELEVENLABS_API_KEY = os.getenv("ELEVENLABS_API_KEY")

class TopicAgent:
    def generate_outline(self, topic, duration, difficulty):
        if not openai_client:
            return self._mock_outline(topic, duration, difficulty)
            
        try:
            response = openai_client.chat.completions.create(
                model="gpt-4-turbo",
                messages=[
                    {
                        "role": "system",
                        "content": (
                            "You are an expert corporate trainer with 20+ years of experience creating "
                            "high-value workshops for Fortune 500 companies. Create a professional workshop outline that "
                            "includes: 1) Clear learning objectives, 2) Practical real-world exercises, "
                            "3) Industry case studies, 4) Measurable outcomes. Format as JSON."
                        )
                    },
                    {
                        "role": "user",
                        "content": (
                            f"Create a comprehensive {duration}-hour {difficulty} workshop outline on '{topic}' for corporate executives. "
                            "Structure: title, duration, difficulty, learning_goals (3-5 bullet points), "
                            "modules (5-7 modules). Each module should have: title, duration, learning_points (3 bullet points), "
                            "case_study (real company example), exercises (2 practical exercises)."
                        )
                    }
                ],
                temperature=0.3,
                max_tokens=1500,
                response_format={"type": "json_object"}
            )
            return json.loads(response.choices[0].message.content)
        except Exception as e:
            return self._mock_outline(topic, duration, difficulty)
    
    def _mock_outline(self, topic, duration, difficulty):
        return {
            "title": f"Mastering {topic} for Business Impact",
            "duration": f"{duration} hours",
            "difficulty": difficulty,
            "learning_goals": [
                "Apply advanced techniques to real business challenges",
                "Measure ROI of prompt engineering initiatives",
                "Develop organizational prompt engineering standards",
                "Implement ethical AI governance frameworks"
            ],
            "modules": [
                {
                    "title": "Strategic Foundations",
                    "duration": "45 min",
                    "learning_points": [
                        "Business value assessment framework",
                        "ROI calculation models",
                        "Stakeholder alignment strategies"
                    ],
                    "case_study": "How JPMorgan reduced operational costs by 37% with prompt optimization",
                    "exercises": [
                        "Calculate potential ROI for your organization",
                        "Develop stakeholder communication plan"
                    ]
                },
                {
                    "title": "Advanced Pattern Engineering",
                    "duration": "60 min",
                    "learning_points": [
                        "Chain-of-thought implementations",
                        "Self-correcting prompt architectures",
                        "Domain-specific pattern libraries"
                    ],
                    "case_study": "McKinsey's knowledge management transformation",
                    "exercises": [
                        "Design pattern library for your industry",
                        "Implement self-correction workflow"
                    ]
                }
            ]
        }

class ContentAgent:
    def generate_content(self, outline):
        if not openai_client:
            return self._mock_content(outline)
            
        try:
            response = openai_client.chat.completions.create(
                model="gpt-4-turbo",
                messages=[
                    {
                        "role": "system",
                        "content": (
                            "You are a senior instructional designer creating premium corporate training materials. "
                            "Develop comprehensive workshop content with: 1) Practitioner-level insights, "
                            "2) Actionable frameworks, 3) Real-world examples, 4) Practical exercises. "
                            "Avoid generic AI content - focus on business impact."
                        )
                    },
                    {
                        "role": "user",
                        "content": (
                            f"Create premium workshop content for this outline: {json.dumps(outline)}. "
                            "For each module: "
                            "1) Detailed script (executive summary, 3 key concepts, business applications) "
                            "2) Speaker notes (presentation guidance) "
                            "3) 3 discussion questions with executive-level responses "
                            "4) 2 practical exercises with solution blueprints "
                            "Format as JSON."
                        )
                    }
                ],
                temperature=0.4,
                max_tokens=3000,
                response_format={"type": "json_object"}
            )
            return json.loads(response.choices[0].message.content)
        except Exception as e:
            return self._mock_content(outline)
    
    def _mock_content(self, outline):
        return {
            "workshop_title": outline.get("title", "Premium AI Workshop"),
            "modules": [
                {
                    "title": "Strategic Foundations",
                    "script": (
                        "## Executive Summary\n"
                        "This module establishes the business case for advanced prompt engineering, "
                        "focusing on measurable ROI and stakeholder alignment.\n\n"
                        "### Key Concepts:\n"
                        "1. **Value Assessment Framework**: Quantify potential savings and revenue opportunities\n"
                        "2. **ROI Calculation Models**: Custom models for different industries\n"
                        "3. **Stakeholder Alignment**: Executive communication strategies\n\n"
                        "### Business Applications:\n"
                        "- Cost reduction in customer service operations\n"
                        "- Acceleration of R&D processes\n"
                        "- Enhanced competitive intelligence"
                    ),
                    "speaker_notes": [
                        "Emphasize real dollar impact - use JPMorgan case study numbers",
                        "Show ROI calculator template",
                        "Highlight C-suite communication strategies"
                    ],
                    "discussion_questions": [
                        {
                            "question": "How could prompt engineering impact your bottom line?",
                            "response": "Typical results: 30-40% operational efficiency gains, 15-25% innovation acceleration"
                        }
                    ],
                    "exercises": [
                        {
                            "title": "ROI Calculation Workshop",
                            "instructions": "Calculate potential savings using our enterprise ROI model",
                            "solution": "Template: (Current Cost × Efficiency Gain) - Implementation Cost"
                        }
                    ]
                }
            ]
        }

class SlideAgent:
    def generate_slides(self, content):
        if not openai_client:
            return self._professional_slides(content)
            
        try:
            response = openai_client.chat.completions.create(
                model="gpt-4-turbo",
                messages=[
                    {
                        "role": "system",
                        "content": (
                            "You are a McKinsey-level presentation specialist. Create professional slides with: "
                            "1) Clean, executive-friendly design 2) Data visualization frameworks "
                            "3) Action-oriented content 4) Brand-compliant styling. "
                            "Use Marp Markdown format with the 'gaia' theme."
                        )
                    },
                    {
                        "role": "user",
                        "content": (
                            f"Create a boardroom-quality slide deck for: {json.dumps(content)}. "
                            "Structure: Title slide, module slides (objective, 3 key points, case study, exercise), "
                            "summary slide. Include placeholders for data visualization."
                        )
                    }
                ],
                temperature=0.2,
                max_tokens=2500
            )
            return response.choices[0].message.content
        except Exception as e:
            return self._professional_slides(content)
    
    def _professional_slides(self, content):
        return f"""---
marp: true
theme: gaia
class: lead
paginate: true
backgroundColor: #fff
backgroundImage: url('https://marp.app/assets/hero-background.svg')
---

# {content.get('workshop_title', 'Executive AI Workshop')}
## Transforming Business Through Advanced AI

---
<!-- _class: invert -->
## Module 1: Strategic Foundations
### Driving Measurable Business Value

![bg right:40% w:450](https://images.pexels.com/photos/3184292/pexels-photo-3184292.jpeg)

- **ROI Framework**: Quantifying impact
- **Stakeholder Alignment**: Executive buy-in strategies
- **Implementation Roadmap**: Phased adoption plan

---
## Case Study: Financial Services Transformation
### JPMorgan Chase

| Metric | Before | After | Improvement |
|--------|--------|-------|-------------|
| Operation Costs | $4.2M | $2.6M | 38% reduction |
| Process Time | 14 days | 3 days | 79% faster |
| Error Rate | 8.2% | 0.4% | 95% reduction |

---
## Practical Exercise: ROI Calculation
```mermaid
graph TD
    A[Current Costs] --> B[Potential Savings]
    C[Implementation Costs] --> D[Net ROI]
    B --> D
Document current process costs

Estimate efficiency gains

Calculate net ROI

Q&A
Let's discuss your specific challenges
"""

class CodeAgent:
    def generate_code(self, content):
        if not openai_client:
            return self._professional_code(content)
            
        try:
            response = openai_client.chat.completions.create(
                model="gpt-4-turbo",
                messages=[
                    {
                        "role": "system",
                        "content": (
                            "You are an enterprise solutions architect. Create professional-grade code labs with: "
                            "1) Production-ready patterns 2) Comprehensive documentation "
                            "3) Enterprise security practices 4) Scalable architectures. "
                            "Use Python with the latest best practices."
                        )
                    },
                    {
                        "role": "user",
                        "content": (
                            f"Create a professional code lab for: {json.dumps(content)}. "
                            "Include: Setup instructions, business solution patterns, "
                            "enterprise integration examples, and security best practices."
                        )
                    }
                ],
                temperature=0.3,
                max_tokens=2500
            )
            return response.choices[0].message.content
        except Exception as e:
            return self._professional_code(content)

    def _professional_code(self, content):
        return f"""# Enterprise-Grade Prompt Engineering Lab
Business Solution Framework
python
class PromptOptimizer:
    def __init__(self, model="gpt-4-turbo"):
        self.model = model
        self.pattern_library = {{
            "financial_analysis": "Extract key metrics from financial reports",
            "customer_service": "Resolve tier-2 support tickets"
        }}
    
    def optimize_prompt(self, business_case):
        # Implement enterprise optimization logic
        return f"Business-optimized prompt for {{business_case}}"
    
    def calculate_roi(self, current_cost, expected_efficiency):
        return current_cost * expected_efficiency

# Example usage
optimizer = PromptOptimizer()
print(optimizer.calculate_roi(500000, 0.35))  # $175,000 savings

Security Best Practices
python
def secure_prompt_handling(user_input):
    # Implement OWASP security standards
    sanitized = sanitize_input(user_input)
    validate_business_context(sanitized)
    return apply_enterprise_guardrails(sanitized)

Integration Pattern: CRM System
python
def integrate_with_salesforce(prompt, salesforce_data):
    # Enterprise integration example
    enriched_prompt = f"{{prompt}} using {{salesforce_data}}"
    return call_ai_api(enriched_prompt)
"""

class DesignAgent:
    def generate_design(self, slide_content):
        if not openai_client:
            return None
            
        try:
            response = openai_client.images.generate(
                model="dall-e-3",
                prompt=(
                    f"Professional corporate slide background for '{slide_content[:200]}' workshop. "
                    "Modern business style, clean lines, premium gradient, boardroom appropriate. "
                    "Include abstract technology elements in corporate colors."
                ),
                n=1,
                size="1024x1024"
            )
            return response.data[0].url
        except Exception as e:
            return None

class VoiceoverAgent:
    def __init__(self):
        self.api_key = ELEVENLABS_API_KEY
        self.voice_id = "21m00Tcm4TlvDq8ikWAM"  # Default voice ID
        self.model = "eleven_monolingual_v1"
        
    def generate_voiceover(self, text, voice_id=None):
        if not self.api_key:
            return None
            
        try:
            voice = voice_id if voice_id else self.voice_id
            
            url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice}"
            headers = {
                "Accept": "audio/mpeg",
                "Content-Type": "application/json",
                "xi-api-key": self.api_key
            }
            data = {
                "text": text,
                "model_id": self.model,
                "voice_settings": {
                    "stability": 0.7,
                    "similarity_boost": 0.8,
                    "style": 0.5,
                    "use_speaker_boost": True
                }
            }
            response = requests.post(url, json=data, headers=headers)
            
            if response.status_code == 200:
                return response.content
            return None
        except Exception as e:
            return None
    
    def get_voices(self):
        if not self.api_key:
            return []
            
        try:
            url = "https://api.elevenlabs.io/v1/voices"
            headers = {"xi-api-key": self.api_key}
            response = requests.get(url, headers=headers)
            
            if response.status_code == 200:
                return response.json().get("voices", [])
            return []
        except Exception as e:
            return []