Delete agents.py

agents.py

@@ -1,401 +0,0 @@
-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 []