|
|
import os |
|
|
import json |
|
|
import requests |
|
|
from dotenv import load_dotenv |
|
|
from openai import OpenAI |
|
|
from flask import Flask, render_template_string, request |
|
|
|
|
|
|
|
|
load_dotenv() |
|
|
|
|
|
|
|
|
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") |
|
|
openai_client = OpenAI(api_key=OPENAI_API_KEY) if OPENAI_API_KEY else None |
|
|
ELEVENLABS_API_KEY = os.getenv("ELEVENLABS_API_KEY") |
|
|
|
|
|
app = Flask(__name__) |
|
|
|
|
|
|
|
|
|
|
|
class TopicAgent: |
|
|
def generate_outline(self, topic, duration, difficulty): |
|
|
if not openai_client: |
|
|
print("OpenAI API not set - using enhanced mock data for outline.") |
|
|
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: |
|
|
print(f"Error during OpenAI outline generation: {e}. Falling back to mock outline.") |
|
|
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: |
|
|
print("OpenAI API not set - using enhanced mock data for content.") |
|
|
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: |
|
|
print(f"Error during OpenAI content generation: {e}. Falling back to mock content.") |
|
|
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: |
|
|
print("OpenAI API not set - using enhanced mock slides.") |
|
|
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: |
|
|
print(f"Error during slide generation: {e}. Using mock slides.") |
|
|
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 |
|
|
|
|
|
 |
|
|
|
|
|
- **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: |
|
|
print("OpenAI API not set - using enhanced mock code.") |
|
|
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: |
|
|
print(f"Error during code generation: {e}. Using mock code.") |
|
|
return self._professional_code(content) |
|
|
|
|
|
def _professional_code(self, content): |
|
|
return f"""# Enterprise-Grade Prompt Engineering Lab |
|
|
# Business Solution Framework |
|
|
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 |
|
|
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 |
|
|
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: |
|
|
print("OpenAI API not set - skipping design generation.") |
|
|
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: |
|
|
print(f"Error during design generation: {e}.") |
|
|
return None |
|
|
|
|
|
|
|
|
class VoiceoverAgent: |
|
|
def __init__(self): |
|
|
self.api_key = ELEVENLABS_API_KEY |
|
|
self.voice_id = "21m00Tcm4TlvDq8ikWAM" |
|
|
self.model = "eleven_monolingual_v1" |
|
|
|
|
|
def generate_voiceover(self, text, voice_id=None): |
|
|
if not self.api_key: |
|
|
print("ElevenLabs API key not set - skipping voiceover generation.") |
|
|
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) |
|
|
response.raise_for_status() |
|
|
return response.content |
|
|
except requests.exceptions.RequestException as e: |
|
|
print(f"Error generating voiceover: {e}") |
|
|
return None |
|
|
|
|
|
def get_voices(self): |
|
|
if not self.api_key: |
|
|
print("ElevenLabs API key not set - cannot fetch voices.") |
|
|
return [] |
|
|
|
|
|
try: |
|
|
url = "https://api.elevenlabs.io/v1/voices" |
|
|
headers = {"xi-api-key": self.api_key} |
|
|
response = requests.get(url, headers=headers) |
|
|
response.raise_for_status() |
|
|
return response.json().get("voices", []) |
|
|
except requests.exceptions.RequestException as e: |
|
|
print(f"Error fetching voices: {e}") |
|
|
return [] |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
HTML_PAGE = """ |
|
|
<!doctype html> |
|
|
<html lang="en"> |
|
|
<head> |
|
|
<meta charset="utf-8" /> |
|
|
<title>Executive Workshop Configuration</title> |
|
|
<meta name="viewport" content="width=device-width,initial-scale=1" /> |
|
|
<style> |
|
|
body { font-family: system-ui,-apple-system,BlinkMacSystemFont,sans-serif; background:#f0f4f8; padding:30px; } |
|
|
.card { background:#fff; padding:20px; border-radius:12px; max-width:500px; margin:auto; box-shadow:0 10px 25px rgba(0,0,0,0.05); } |
|
|
h1 { font-size:1.75rem; margin-bottom:4px; display:flex; align-items:center; gap:8px; } |
|
|
label { display:block; margin-top:16px; font-weight:600; } |
|
|
input { width:100%; padding:10px 14px; border:1px solid #cbd5e1; border-radius:6px; font-size:1rem; transition: all .2s; color:#1f2937; background:#fff; } |
|
|
input:focus { outline:none; border-color:#2563eb; box-shadow:0 0 0 3px rgba(59,130,246,0.35); } |
|
|
input::placeholder { color:#94a3b8; } |
|
|
::selection { background: rgba(59,130,246,0.4); color:#000; } |
|
|
.status { margin-bottom:12px; padding:12px; border-radius:8px; } |
|
|
.warn { background:#fff8e1; border:1px solid #f5e1a4; color:#886f1b; } |
|
|
.ok { background:#e6f6ed; border:1px solid #b8e0c5; color:#1e5f3d; } |
|
|
.note { margin-top:8px; font-size:.9rem; color:#475569; } |
|
|
</style> |
|
|
</head> |
|
|
<body> |
|
|
<div class="card"> |
|
|
<div class="status {{ 'ok' if openai_set else 'warn' }}"> |
|
|
{% if openai_set %} |
|
|
<div class="ok">OpenAI API Key Found</div> |
|
|
{% else %} |
|
|
<div class="warn">OpenAI API not set - using enhanced mock data</div> |
|
|
{% endif %} |
|
|
{% if elevenlabs_set %} |
|
|
<div class="ok" style="margin-top:6px;">ElevenLabs API Key Found</div> |
|
|
{% else %} |
|
|
<div class="warn" style="margin-top:6px;">ElevenLabs API Key not set</div> |
|
|
{% endif %} |
|
|
</div> |
|
|
<h1>Executive Workshop Configuration</h1> |
|
|
<form method="post" action="/submit"> |
|
|
<label for="focus">Workshop Focus</label> |
|
|
<input id="focus" name="focus" placeholder="e.g., AI-Driven Business Transformation" value="{{ prefill }}" autocomplete="off" /> |
|
|
<div class="note">Type here to set the workshop's focus. Selection and text are styled for clarity.</div> |
|
|
<button type="submit" style="margin-top:16px; padding:10px 16px; border:none; background:#2563eb; color:#fff; border-radius:6px; cursor:pointer;">Save</button> |
|
|
</form> |
|
|
</div> |
|
|
</body> |
|
|
</html> |
|
|
""" |
|
|
|
|
|
@app.route("/", methods=["GET"]) |
|
|
def index(): |
|
|
return render_template_string( |
|
|
HTML_PAGE, |
|
|
openai_set=bool(OPENAI_API_KEY), |
|
|
elevenlabs_set=bool(ELEVENLABS_API_KEY), |
|
|
prefill="" |
|
|
) |
|
|
|
|
|
@app.route("/submit", methods=["POST"]) |
|
|
def submit(): |
|
|
focus = request.form.get("focus", "") |
|
|
|
|
|
return render_template_string( |
|
|
HTML_PAGE, |
|
|
openai_set=bool(OPENAI_API_KEY), |
|
|
elevenlabs_set=bool(ELEVENLABS_API_KEY), |
|
|
prefill=focus |
|
|
) |
|
|
|
|
|
if __name__ == "__main__": |
|
|
print(f"Loaded OPENAI_API_KEY: {bool(OPENAI_API_KEY)}, ELEVENLABS_API_KEY: {bool(ELEVENLABS_API_KEY)}") |
|
|
app.run(host="0.0.0.0", port=8080, debug=True, use_reloader=False) |
|
|
|
