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import gradio as gr |
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import json |
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import random |
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from datetime import datetime |
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import pandas as pd |
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import plotly.express as px |
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import plotly.graph_objects as go |
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USERS_DB = {} |
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PROGRESS_DB = {} |
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CURRICULUM = { |
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"beginner": { |
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"title": "AI Foundations to Agent Builder", |
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"duration": "80-100 hours", |
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"modules": [ |
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{"name": "Introduction to AI and Agents", "duration": "5 hours"}, |
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{"name": "Understanding Language Models", "duration": "8 hours"}, |
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{"name": "Basic Prompt Engineering", "duration": "10 hours"}, |
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{"name": "No-Code Agent Building", "duration": "15 hours"}, |
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{"name": "Business Applications", "duration": "12 hours"}, |
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{"name": "First Agent Project", "duration": "20 hours"}, |
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{"name": "Deployment Basics", "duration": "10 hours"}, |
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{"name": "Career Preparation", "duration": "10 hours"} |
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] |
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}, |
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"intermediate": { |
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"title": "Agent Developer Professional", |
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"duration": "120-150 hours", |
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"modules": [ |
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{"name": "Advanced Prompt Engineering", "duration": "15 hours"}, |
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{"name": "Python for AI Agents", "duration": "20 hours"}, |
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{"name": "LangChain Fundamentals", "duration": "15 hours"}, |
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{"name": "Multi-Provider Integration", "duration": "20 hours"}, |
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{"name": "Memory and Context Management", "duration": "15 hours"}, |
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{"name": "Tool Creation and Integration", "duration": "20 hours"}, |
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{"name": "Production Deployment", "duration": "15 hours"}, |
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{"name": "Advanced Projects", "duration": "30 hours"} |
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] |
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}, |
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"advanced": { |
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"title": "Enterprise AI Architect", |
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"duration": "180-220 hours", |
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"modules": [ |
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{"name": "Multi-Agent Systems Design", "duration": "25 hours"}, |
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{"name": "Custom Framework Development", "duration": "30 hours"}, |
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{"name": "Scalable Architecture Patterns", "duration": "20 hours"}, |
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{"name": "Security and Compliance", "duration": "15 hours"}, |
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{"name": "Performance Optimization", "duration": "20 hours"}, |
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{"name": "Industry Specialization", "duration": "30 hours"}, |
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{"name": "Enterprise Integration", "duration": "25 hours"}, |
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{"name": "Capstone Project", "duration": "35 hours"} |
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] |
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} |
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} |
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AGENT_TEMPLATES = { |
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"customer_service": { |
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"name": "Customer Service Pro", |
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"description": "Handle customer inquiries with empathy and efficiency", |
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"code": """# Customer Service Agent |
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agent = AgentBuilder( |
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name="CustomerServicePro", |
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model="gpt-4", |
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system_prompt=\"\"\"You are a helpful customer service agent. |
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Be empathetic, solution-focused, and professional. |
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Always try to resolve issues on first contact.\"\"\", |
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tools=["order_lookup", "refund_process", "ticket_create"], |
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memory_type="conversation", |
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max_tokens=150 |
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) |
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# Example usage |
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response = agent.process("I haven't received my order #12345") |
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print(response)""" |
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}, |
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"data_analyst": { |
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"name": "Data Analyst Agent", |
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"description": "Automated data analysis and visualization", |
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"code": """# Data Analysis Agent |
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analyst = DataAnalystAgent( |
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name="DataInsightsPro", |
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model="gpt-4", |
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capabilities={ |
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"data_sources": ["csv", "sql", "api"], |
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"analysis_types": ["descriptive", "predictive", "prescriptive"], |
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"visualizations": ["charts", "dashboards", "reports"] |
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}, |
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auto_insights=True |
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) |
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# Analyze sales data |
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insights = analyst.analyze("sales_data.csv", |
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questions=["What are the top performing products?", |
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"Identify seasonal trends", |
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"Predict next quarter revenue"])""" |
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}, |
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"content_creator": { |
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"name": "Content Creator Agent", |
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"description": "Generate engaging content for multiple platforms", |
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"code": """# Content Creation Agent |
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creator = ContentAgent( |
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name="ContentGenius", |
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model="claude-3-opus", |
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style_guide={ |
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"tone": "professional yet engaging", |
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"format": "SEO-optimized", |
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"platforms": ["blog", "social", "email"] |
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}, |
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fact_checking=True |
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) |
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# Generate blog post |
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post = creator.create( |
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type="blog_post", |
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topic="The Future of AI Agents", |
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keywords=["AI agents", "automation", "future of work"], |
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length=1000 |
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)""" |
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} |
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} |
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def create_header(): |
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return """ |
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<div style="text-align: center; padding: 20px;"> |
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<h1 style="color: #6366f1; font-size: 3em; margin-bottom: 10px;">🎓 AgenticAI Academy</h1> |
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<p style="font-size: 1.2em; color: #666;">Master AI Agent Development • From Beginner to Expert</p> |
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<p style="margin-top: 20px;"> |
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<span style="background: #e0f2fe; color: #0369a1; padding: 5px 15px; border-radius: 20px; margin: 0 5px;">$32B Market by 2030</span> |
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<span style="background: #fef3c7; color: #d97706; padding: 5px 15px; border-radius: 20px; margin: 0 5px;">31.2% Annual Growth</span> |
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<span style="background: #fce7f3; color: #be185d; padding: 5px 15px; border-radius: 20px; margin: 0 5px;">$100K-250K+ Salaries</span> |
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</p> |
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</div> |
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""" |
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def register_user(name, email, experience_level, learning_goal): |
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"""Register a new user""" |
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if email in USERS_DB: |
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return "❌ Email already registered. Please login instead." |
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USERS_DB[email] = { |
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"name": name, |
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"experience_level": experience_level, |
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"learning_goal": learning_goal, |
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"registered": datetime.now().isoformat(), |
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"path": "beginner" if experience_level == "No coding experience" else "intermediate" |
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} |
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PROGRESS_DB[email] = { |
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"completed_modules": [], |
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"current_module": 0, |
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"total_hours": 0, |
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"projects": [] |
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} |
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return f"✅ Welcome to AgenticAI Academy, {name}! Your {USERS_DB[email]['path']} learning path is ready." |
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def get_personalized_curriculum(email): |
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"""Get personalized curriculum based on user profile""" |
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if email not in USERS_DB: |
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return "Please register first to see your personalized curriculum." |
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user = USERS_DB[email] |
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path = user["path"] |
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curriculum = CURRICULUM[path] |
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progress = PROGRESS_DB.get(email, {}) |
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completed = len(progress.get("completed_modules", [])) |
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output = f"## 📚 Your Learning Path: {curriculum['title']}\n\n" |
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output += f"**Total Duration:** {curriculum['duration']}\n" |
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output += f"**Progress:** {completed}/{len(curriculum['modules'])} modules completed\n\n" |
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output += "### Modules:\n" |
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for i, module in enumerate(curriculum['modules']): |
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status = "✅" if i < completed else "⏳" |
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output += f"{i+1}. {status} **{module['name']}** ({module['duration']})\n" |
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return output |
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def generate_agent_code(agent_type, use_case, model_choice, include_memory, include_tools): |
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"""Generate custom agent code based on parameters""" |
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template = AGENT_TEMPLATES.get(agent_type, AGENT_TEMPLATES["customer_service"]) |
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code = f"""# Generated {agent_type.replace('_', ' ').title()} Agent |
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# Use case: {use_case} |
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from agenticai import AgentBuilder, MemoryStore, ToolRegistry |
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# Initialize agent |
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agent = AgentBuilder( |
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name="{agent_type}_agent", |
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model="{model_choice}", |
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temperature=0.7 |
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) |
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# Configure system prompt |
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agent.set_system_prompt(\"\"\" |
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{template['description']} |
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Specific use case: {use_case} |
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\"\"\") |
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""" |
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if include_memory: |
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code += """ |
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# Add memory capabilities |
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agent.add_memory( |
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type="long_term", |
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store=MemoryStore( |
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vector_db="pinecone", |
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embedding_model="text-embedding-ada-002" |
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) |
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) |
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""" |
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if include_tools: |
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code += """ |
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# Add tools |
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agent.add_tools([ |
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"web_search", |
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"calculator", |
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"code_executor", |
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"file_handler" |
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]) |
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""" |
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code += """ |
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# Example usage |
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response = agent.process("Your query here") |
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print(response) |
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# Deploy agent |
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# agent.deploy(endpoint="/api/agent", port=8080) |
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""" |
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return code |
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def create_progress_visualization(email): |
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"""Create visual progress chart""" |
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if email not in PROGRESS_DB: |
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return None |
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progress = PROGRESS_DB[email] |
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user = USERS_DB.get(email, {}) |
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path = user.get("path", "beginner") |
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curriculum = CURRICULUM[path] |
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modules = [m["name"] for m in curriculum["modules"]] |
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completed = progress.get("completed_modules", []) |
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status = ["Completed" if i < len(completed) else "Pending" for i in range(len(modules))] |
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hours = [m["duration"].split()[0] for m in curriculum["modules"]] |
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df = pd.DataFrame({ |
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"Module": modules, |
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"Status": status, |
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"Hours": [int(h) for h in hours] |
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}) |
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fig = px.bar(df, x="Hours", y="Module", orientation='h', color="Status", |
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color_discrete_map={"Completed": "#10b981", "Pending": "#e5e7eb"}, |
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title="Your Learning Progress") |
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fig.update_layout( |
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showlegend=False, |
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xaxis_title="Duration (hours)", |
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yaxis_title="", |
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height=400 |
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) |
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return fig |
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def simulate_agent_conversation(agent_type, user_input): |
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"""Simulate agent responses""" |
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responses = { |
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"customer_service": [ |
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"I understand your concern. Let me look into that for you right away.", |
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"I've found your order #12345. It's currently in transit and should arrive within 2 days.", |
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"I apologize for the inconvenience. I'm processing a full refund for you now.", |
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"Is there anything else I can help you with today?" |
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], |
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"data_analyst": [ |
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"I've analyzed your data and found some interesting patterns.", |
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"The top performing category shows 34% growth month-over-month.", |
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"Based on historical trends, I predict a 15% increase in Q2.", |
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"I've created a visualization to better illustrate these insights." |
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], |
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"content_creator": [ |
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"I've drafted an engaging blog post optimized for your keywords.", |
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"The content includes relevant statistics and expert insights.", |
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"I've structured it with SEO-friendly headers and meta descriptions.", |
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"Would you like me to adjust the tone or add more sections?" |
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] |
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} |
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agent_responses = responses.get(agent_type, responses["customer_service"]) |
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response = random.choice(agent_responses) |
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return f"**Agent Response:** {response}\n\n*This is a simulated response. In the full course, you'll build real agents that connect to actual AI models.*" |
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def create_market_stats(): |
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"""Create market statistics visualization""" |
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years = list(range(2024, 2031)) |
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market_size = [5.88, 7.73, 10.16, 13.36, 17.57, 23.11, 30.39] |
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fig = go.Figure() |
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fig.add_trace(go.Scatter( |
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x=years, |
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y=market_size, |
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mode='lines+markers', |
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name='Market Size', |
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line=dict(color='#6366f1', width=3), |
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marker=dict(size=8) |
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)) |
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fig.update_layout( |
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title="AI Education Market Growth (Billions USD)", |
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xaxis_title="Year", |
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yaxis_title="Market Size (Billions)", |
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showlegend=False, |
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height=400 |
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) |
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return fig |
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with gr.Blocks(title="AgenticAI Academy", theme=gr.themes.Base()) as app: |
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gr.HTML(create_header()) |
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with gr.Tabs(): |
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with gr.TabItem("🚀 Get Started"): |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown(""" |
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### Start Your AI Agent Development Journey |
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Join thousands of professionals learning to build production-ready AI agents. |
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Our hands-on curriculum takes you from beginner to expert, with real projects |
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and industry certifications. |
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""") |
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name_input = gr.Textbox(label="Full Name", placeholder="John Doe") |
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email_input = gr.Textbox(label="Email", placeholder="[email protected]") |
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experience_level = gr.Radio( |
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label="Current Experience Level", |
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choices=["No coding experience", "Some programming knowledge", "Experienced developer"], |
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value="No coding experience" |
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) |
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learning_goal = gr.Dropdown( |
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label="Primary Learning Goal", |
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choices=[ |
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"Career transition to AI", |
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"Add AI skills to current role", |
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"Build AI products/startups", |
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"Enterprise AI implementation" |
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], |
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value="Career transition to AI" |
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) |
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register_btn = gr.Button("Start Free Trial", variant="primary", size="lg") |
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registration_output = gr.Textbox(label="Registration Status", lines=2) |
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with gr.Column(): |
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gr.Markdown(""" |
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### 📊 Market Opportunity |
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The AI education market is experiencing explosive growth, creating unprecedented |
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opportunities for skilled professionals. |
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""") |
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market_chart = gr.Plot(value=create_market_stats()) |
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gr.Markdown(""" |
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### 💰 Earning Potential |
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- **Freelance:** $50-500/hour |
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- **Full-time:** $100K-250K+ annually |
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- **Consulting:** $1,000-5,000/day |
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- **Products:** Unlimited scaling potential |
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""") |
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register_btn.click( |
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register_user, |
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inputs=[name_input, email_input, experience_level, learning_goal], |
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outputs=registration_output |
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) |
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with gr.TabItem("📚 Curriculum"): |
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with gr.Row(): |
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with gr.Column(scale=1): |
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curriculum_email = gr.Textbox( |
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label="Enter your email to see personalized curriculum", |
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placeholder="[email protected]" |
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) |
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show_curriculum_btn = gr.Button("Show My Curriculum", variant="primary") |
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with gr.Column(scale=2): |
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curriculum_output = gr.Markdown() |
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progress_chart = gr.Plot() |
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show_curriculum_btn.click( |
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get_personalized_curriculum, |
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inputs=[curriculum_email], |
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outputs=curriculum_output |
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).then( |
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create_progress_visualization, |
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inputs=[curriculum_email], |
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outputs=progress_chart |
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) |
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gr.Markdown(""" |
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### 🎓 Available Learning Paths |
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#### Beginner Path: AI Foundations to Agent Builder (80-100 hours) |
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Perfect for non-technical professionals. Learn to build AI agents using visual tools |
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and no-code platforms. Focus on business applications and practical implementation. |
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|
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#### Intermediate Path: Agent Developer Professional (120-150 hours) |
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For developers ready to add AI to their toolkit. Master Python-based agent development, |
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advanced prompt engineering, and multi-provider integration. |
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|
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#### Advanced Path: Enterprise AI Architect (180-220 hours) |
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Design and deploy complex multi-agent systems. Learn scalable architectures, |
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security best practices, and industry-specific solutions. |
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""") |
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with gr.TabItem("🔧 Agent Builder"): |
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gr.Markdown(""" |
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### Build Your Custom AI Agent |
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|
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Use our code generator to create a starting template for your AI agent. |
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In the full course, you'll learn to build and deploy these agents with real AI models. |
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""") |
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|
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with gr.Row(): |
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with gr.Column(): |
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agent_type = gr.Dropdown( |
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label="Agent Type", |
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choices=["customer_service", "data_analyst", "content_creator"], |
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value="customer_service" |
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) |
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use_case = gr.Textbox( |
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label="Specific Use Case", |
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placeholder="E.g., E-commerce support, Financial analysis, Blog writing" |
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) |
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model_choice = gr.Dropdown( |
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label="AI Model", |
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choices=["gpt-4", "gpt-3.5-turbo", "claude-3-opus", "gemini-pro"], |
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value="gpt-4" |
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) |
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include_memory = gr.Checkbox(label="Include Memory System", value=True) |
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include_tools = gr.Checkbox(label="Include External Tools", value=True) |
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|
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generate_btn = gr.Button("Generate Agent Code", variant="primary") |
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|
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with gr.Column(): |
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code_output = gr.Code(label="Generated Agent Code", language="python") |
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|
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generate_btn.click( |
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generate_agent_code, |
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inputs=[agent_type, use_case, model_choice, include_memory, include_tools], |
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outputs=code_output |
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) |
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|
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with gr.TabItem("🤖 Try an Agent"): |
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gr.Markdown(""" |
|
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### Interactive Agent Demo |
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|
|
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Experience what AI agents can do. This is a simulation - in the course, |
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|
you'll build real agents that connect to actual AI models. |
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""") |
|
|
|
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with gr.Row(): |
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with gr.Column(): |
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|
demo_agent_type = gr.Radio( |
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|
label="Select Agent Type", |
|
|
choices=["customer_service", "data_analyst", "content_creator"], |
|
|
value="customer_service" |
|
|
) |
|
|
user_input = gr.Textbox( |
|
|
label="Your Message", |
|
|
placeholder="Type your message to the agent...", |
|
|
lines=3 |
|
|
) |
|
|
send_btn = gr.Button("Send Message", variant="primary") |
|
|
|
|
|
with gr.Column(): |
|
|
agent_response = gr.Markdown(label="Agent Response") |
|
|
|
|
|
send_btn.click( |
|
|
simulate_agent_conversation, |
|
|
inputs=[demo_agent_type, user_input], |
|
|
outputs=agent_response |
|
|
) |
|
|
|
|
|
|
|
|
with gr.TabItem("📖 Resources"): |
|
|
gr.Markdown(""" |
|
|
### 🎯 Free Resources to Get Started |
|
|
|
|
|
#### 📺 Video Tutorials |
|
|
- [Introduction to AI Agents](https://youtube.com/agenticai) (30 min) |
|
|
- [Your First Prompt Engineering](https://youtube.com/agenticai) (45 min) |
|
|
- [Building with No-Code Tools](https://youtube.com/agenticai) (60 min) |
|
|
|
|
|
#### 📄 Documentation |
|
|
- [AI Agent Fundamentals Guide](https://docs.agenticai.academy) |
|
|
- [Prompt Engineering Best Practices](https://docs.agenticai.academy/prompts) |
|
|
- [API Integration Handbook](https://docs.agenticai.academy/apis) |
|
|
|
|
|
#### 💬 Community |
|
|
- [Discord Server](https://discord.gg/agenticai) - 5,000+ members |
|
|
- [GitHub Repository](https://github.com/agenticai-academy) |
|
|
- [LinkedIn Group](https://linkedin.com/groups/agenticai) |
|
|
|
|
|
#### 🏆 Success Stories |
|
|
- **Sarah M.**: Marketing Manager → AI Consultant ($150K → $225K) |
|
|
- **James L.**: Developer → AI Agency Owner ($30K/month revenue) |
|
|
- **Tech Corp**: Trained 50 developers, saved $2M through automation |
|
|
|
|
|
#### 🎓 Certifications |
|
|
1. **Certified AI Agent Practitioner** - Entry level certification |
|
|
2. **Certified AI Agent Developer** - Professional certification |
|
|
3. **Certified Enterprise AI Architect** - Advanced certification |
|
|
|
|
|
#### 💰 Pricing |
|
|
- **Starter**: $35/month - Core curriculum access |
|
|
- **Professional**: $60/month - Full features + certification |
|
|
- **Enterprise**: Custom pricing - Team features + support |
|
|
|
|
|
### 📞 Contact |
|
|
- Email: [email protected] |
|
|
- Schedule Demo: [Book a Call](https://calendly.com/agenticai) |
|
|
""") |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
app.launch() |
