app.py

import gradio as gr
import json
import random
from datetime import datetime
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

# Simulated database for demo purposes
USERS_DB = {}
PROGRESS_DB = {}

# Course curriculum structure
CURRICULUM = {
    "beginner": {
        "title": "AI Foundations to Agent Builder",
        "duration": "80-100 hours",
        "modules": [
            {"name": "Introduction to AI and Agents", "duration": "5 hours"},
            {"name": "Understanding Language Models", "duration": "8 hours"},
            {"name": "Basic Prompt Engineering", "duration": "10 hours"},
            {"name": "No-Code Agent Building", "duration": "15 hours"},
            {"name": "Business Applications", "duration": "12 hours"},
            {"name": "First Agent Project", "duration": "20 hours"},
            {"name": "Deployment Basics", "duration": "10 hours"},
            {"name": "Career Preparation", "duration": "10 hours"}
        ]
    },
    "intermediate": {
        "title": "Agent Developer Professional",
        "duration": "120-150 hours",
        "modules": [
            {"name": "Advanced Prompt Engineering", "duration": "15 hours"},
            {"name": "Python for AI Agents", "duration": "20 hours"},
            {"name": "LangChain Fundamentals", "duration": "15 hours"},
            {"name": "Multi-Provider Integration", "duration": "20 hours"},
            {"name": "Memory and Context Management", "duration": "15 hours"},
            {"name": "Tool Creation and Integration", "duration": "20 hours"},
            {"name": "Production Deployment", "duration": "15 hours"},
            {"name": "Advanced Projects", "duration": "30 hours"}
        ]
    },
    "advanced": {
        "title": "Enterprise AI Architect",
        "duration": "180-220 hours",
        "modules": [
            {"name": "Multi-Agent Systems Design", "duration": "25 hours"},
            {"name": "Custom Framework Development", "duration": "30 hours"},
            {"name": "Scalable Architecture Patterns", "duration": "20 hours"},
            {"name": "Security and Compliance", "duration": "15 hours"},
            {"name": "Performance Optimization", "duration": "20 hours"},
            {"name": "Industry Specialization", "duration": "30 hours"},
            {"name": "Enterprise Integration", "duration": "25 hours"},
            {"name": "Capstone Project", "duration": "35 hours"}
        ]
    }
}

# Sample agent templates
AGENT_TEMPLATES = {
    "customer_service": {
        "name": "Customer Service Pro",
        "description": "Handle customer inquiries with empathy and efficiency",
        "code": """# Customer Service Agent
agent = AgentBuilder(
    name="CustomerServicePro",
    model="gpt-4",
    system_prompt=\"\"\"You are a helpful customer service agent.
    Be empathetic, solution-focused, and professional.
    Always try to resolve issues on first contact.\"\"\",
    tools=["order_lookup", "refund_process", "ticket_create"],
    memory_type="conversation",
    max_tokens=150
)

# Example usage
response = agent.process("I haven't received my order #12345")
print(response)"""
    },
    "data_analyst": {
        "name": "Data Analyst Agent",
        "description": "Automated data analysis and visualization",
        "code": """# Data Analysis Agent
analyst = DataAnalystAgent(
    name="DataInsightsPro",
    model="gpt-4",
    capabilities={
        "data_sources": ["csv", "sql", "api"],
        "analysis_types": ["descriptive", "predictive", "prescriptive"],
        "visualizations": ["charts", "dashboards", "reports"]
    },
    auto_insights=True
)

# Analyze sales data
insights = analyst.analyze("sales_data.csv", 
    questions=["What are the top performing products?",
               "Identify seasonal trends",
               "Predict next quarter revenue"])"""
    },
    "content_creator": {
        "name": "Content Creator Agent",
        "description": "Generate engaging content for multiple platforms",
        "code": """# Content Creation Agent
creator = ContentAgent(
    name="ContentGenius",
    model="claude-3-opus",
    style_guide={
        "tone": "professional yet engaging",
        "format": "SEO-optimized",
        "platforms": ["blog", "social", "email"]
    },
    fact_checking=True
)

# Generate blog post
post = creator.create(
    type="blog_post",
    topic="The Future of AI Agents",
    keywords=["AI agents", "automation", "future of work"],
    length=1000
)"""
    }
}

def create_header():
    return """
    <div style="text-align: center; padding: 20px;">
        <h1 style="color: #6366f1; font-size: 3em; margin-bottom: 10px;">🎓 AgenticAI Academy</h1>
        <p style="font-size: 1.2em; color: #666;">Master AI Agent Development • From Beginner to Expert</p>
        <p style="margin-top: 20px;">
            <span style="background: #e0f2fe; color: #0369a1; padding: 5px 15px; border-radius: 20px; margin: 0 5px;">$32B Market by 2030</span>
            <span style="background: #fef3c7; color: #d97706; padding: 5px 15px; border-radius: 20px; margin: 0 5px;">31.2% Annual Growth</span>
            <span style="background: #fce7f3; color: #be185d; padding: 5px 15px; border-radius: 20px; margin: 0 5px;">$100K-250K+ Salaries</span>
        </p>
    </div>
    """

def register_user(name, email, experience_level, learning_goal):
    """Register a new user"""
    if email in USERS_DB:
        return "❌ Email already registered. Please login instead."
    
    USERS_DB[email] = {
        "name": name,
        "experience_level": experience_level,
        "learning_goal": learning_goal,
        "registered": datetime.now().isoformat(),
        "path": "beginner" if experience_level == "No coding experience" else "intermediate"
    }
    
    PROGRESS_DB[email] = {
        "completed_modules": [],
        "current_module": 0,
        "total_hours": 0,
        "projects": []
    }
    
    return f"✅ Welcome to AgenticAI Academy, {name}! Your {USERS_DB[email]['path']} learning path is ready."

def get_personalized_curriculum(email):
    """Get personalized curriculum based on user profile"""
    if email not in USERS_DB:
        return "Please register first to see your personalized curriculum."
    
    user = USERS_DB[email]
    path = user["path"]
    curriculum = CURRICULUM[path]
    
    progress = PROGRESS_DB.get(email, {})
    completed = len(progress.get("completed_modules", []))
    
    output = f"## 📚 Your Learning Path: {curriculum['title']}\n\n"
    output += f"**Total Duration:** {curriculum['duration']}\n"
    output += f"**Progress:** {completed}/{len(curriculum['modules'])} modules completed\n\n"
    
    output += "### Modules:\n"
    for i, module in enumerate(curriculum['modules']):
        status = "✅" if i < completed else "⏳"
        output += f"{i+1}. {status} **{module['name']}** ({module['duration']})\n"
    
    return output

def generate_agent_code(agent_type, use_case, model_choice, include_memory, include_tools):
    """Generate custom agent code based on parameters"""
    template = AGENT_TEMPLATES.get(agent_type, AGENT_TEMPLATES["customer_service"])
    
    code = f"""# Generated {agent_type.replace('_', ' ').title()} Agent
# Use case: {use_case}

from agenticai import AgentBuilder, MemoryStore, ToolRegistry

# Initialize agent
agent = AgentBuilder(
    name="{agent_type}_agent",
    model="{model_choice}",
    temperature=0.7
)

# Configure system prompt
agent.set_system_prompt(\"\"\"
{template['description']}
Specific use case: {use_case}
\"\"\")
"""
    
    if include_memory:
        code += """
# Add memory capabilities
agent.add_memory(
    type="long_term",
    store=MemoryStore(
        vector_db="pinecone",
        embedding_model="text-embedding-ada-002"
    )
)
"""
    
    if include_tools:
        code += """
# Add tools
agent.add_tools([
    "web_search",
    "calculator", 
    "code_executor",
    "file_handler"
])
"""
    
    code += """
# Example usage
response = agent.process("Your query here")
print(response)

# Deploy agent
# agent.deploy(endpoint="/api/agent", port=8080)
"""
    
    return code

def create_progress_visualization(email):
    """Create visual progress chart"""
    if email not in PROGRESS_DB:
        return None
    
    progress = PROGRESS_DB[email]
    user = USERS_DB.get(email, {})
    path = user.get("path", "beginner")
    curriculum = CURRICULUM[path]
    
    # Create progress data
    modules = [m["name"] for m in curriculum["modules"]]
    completed = progress.get("completed_modules", [])
    status = ["Completed" if i < len(completed) else "Pending" for i in range(len(modules))]
    hours = [m["duration"].split()[0] for m in curriculum["modules"]]
    
    # Create DataFrame
    df = pd.DataFrame({
        "Module": modules,
        "Status": status,
        "Hours": [int(h) for h in hours]
    })
    
    # Create Plotly figure
    fig = px.bar(df, x="Hours", y="Module", orientation='h', color="Status",
                 color_discrete_map={"Completed": "#10b981", "Pending": "#e5e7eb"},
                 title="Your Learning Progress")
    
    fig.update_layout(
        showlegend=False,
        xaxis_title="Duration (hours)",
        yaxis_title="",
        height=400
    )
    
    return fig

def simulate_agent_conversation(agent_type, user_input):
    """Simulate agent responses"""
    responses = {
        "customer_service": [
            "I understand your concern. Let me look into that for you right away.",
            "I've found your order #12345. It's currently in transit and should arrive within 2 days.",
            "I apologize for the inconvenience. I'm processing a full refund for you now.",
            "Is there anything else I can help you with today?"
        ],
        "data_analyst": [
            "I've analyzed your data and found some interesting patterns.",
            "The top performing category shows 34% growth month-over-month.",
            "Based on historical trends, I predict a 15% increase in Q2.",
            "I've created a visualization to better illustrate these insights."
        ],
        "content_creator": [
            "I've drafted an engaging blog post optimized for your keywords.",
            "The content includes relevant statistics and expert insights.",
            "I've structured it with SEO-friendly headers and meta descriptions.",
            "Would you like me to adjust the tone or add more sections?"
        ]
    }
    
    # Simulate processing
    agent_responses = responses.get(agent_type, responses["customer_service"])
    response = random.choice(agent_responses)
    
    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.*"

def create_market_stats():
    """Create market statistics visualization"""
    # Market growth data
    years = list(range(2024, 2031))
    market_size = [5.88, 7.73, 10.16, 13.36, 17.57, 23.11, 30.39]
    
    fig = go.Figure()
    
    fig.add_trace(go.Scatter(
        x=years,
        y=market_size,
        mode='lines+markers',
        name='Market Size',
        line=dict(color='#6366f1', width=3),
        marker=dict(size=8)
    ))
    
    fig.update_layout(
        title="AI Education Market Growth (Billions USD)",
        xaxis_title="Year",
        yaxis_title="Market Size (Billions)",
        showlegend=False,
        height=400
    )
    
    return fig

# Create Gradio interface
with gr.Blocks(title="AgenticAI Academy", theme=gr.themes.Base()) as app:
    gr.HTML(create_header())
    
    with gr.Tabs():
        # Registration Tab
        with gr.TabItem("🚀 Get Started"):
            with gr.Row():
                with gr.Column():
                    gr.Markdown("""
                    ### Start Your AI Agent Development Journey
                    
                    Join thousands of professionals learning to build production-ready AI agents.
                    Our hands-on curriculum takes you from beginner to expert, with real projects
                    and industry certifications.
                    """)
                    
                    name_input = gr.Textbox(label="Full Name", placeholder="John Doe")
                    email_input = gr.Textbox(label="Email", placeholder="[email protected]")
                    experience_level = gr.Radio(
                        label="Current Experience Level",
                        choices=["No coding experience", "Some programming knowledge", "Experienced developer"],
                        value="No coding experience"
                    )
                    learning_goal = gr.Dropdown(
                        label="Primary Learning Goal",
                        choices=[
                            "Career transition to AI",
                            "Add AI skills to current role",
                            "Build AI products/startups",
                            "Enterprise AI implementation"
                        ],
                        value="Career transition to AI"
                    )
                    
                    register_btn = gr.Button("Start Free Trial", variant="primary", size="lg")
                    registration_output = gr.Textbox(label="Registration Status", lines=2)
                    
                with gr.Column():
                    gr.Markdown("""
                    ### 📊 Market Opportunity
                    
                    The AI education market is experiencing explosive growth, creating unprecedented
                    opportunities for skilled professionals.
                    """)
                    market_chart = gr.Plot(value=create_market_stats())
                    
                    gr.Markdown("""
                    ### 💰 Earning Potential
                    - **Freelance:** $50-500/hour
                    - **Full-time:** $100K-250K+ annually
                    - **Consulting:** $1,000-5,000/day
                    - **Products:** Unlimited scaling potential
                    """)
            
            register_btn.click(
                register_user,
                inputs=[name_input, email_input, experience_level, learning_goal],
                outputs=registration_output
            )
        
        # Curriculum Tab
        with gr.TabItem("📚 Curriculum"):
            with gr.Row():
                with gr.Column(scale=1):
                    curriculum_email = gr.Textbox(
                        label="Enter your email to see personalized curriculum",
                        placeholder="[email protected]"
                    )
                    show_curriculum_btn = gr.Button("Show My Curriculum", variant="primary")
                    
                with gr.Column(scale=2):
                    curriculum_output = gr.Markdown()
                    progress_chart = gr.Plot()
            
            show_curriculum_btn.click(
                get_personalized_curriculum,
                inputs=[curriculum_email],
                outputs=curriculum_output
            ).then(
                create_progress_visualization,
                inputs=[curriculum_email],
                outputs=progress_chart
            )
            
            gr.Markdown("""
            ### 🎓 Available Learning Paths
            
            #### Beginner Path: AI Foundations to Agent Builder (80-100 hours)
            Perfect for non-technical professionals. Learn to build AI agents using visual tools
            and no-code platforms. Focus on business applications and practical implementation.
            
            #### Intermediate Path: Agent Developer Professional (120-150 hours)
            For developers ready to add AI to their toolkit. Master Python-based agent development,
            advanced prompt engineering, and multi-provider integration.
            
            #### Advanced Path: Enterprise AI Architect (180-220 hours)
            Design and deploy complex multi-agent systems. Learn scalable architectures,
            security best practices, and industry-specific solutions.
            """)
        
        # Agent Builder Tab
        with gr.TabItem("🔧 Agent Builder"):
            gr.Markdown("""
            ### Build Your Custom AI Agent
            
            Use our code generator to create a starting template for your AI agent.
            In the full course, you'll learn to build and deploy these agents with real AI models.
            """)
            
            with gr.Row():
                with gr.Column():
                    agent_type = gr.Dropdown(
                        label="Agent Type",
                        choices=["customer_service", "data_analyst", "content_creator"],
                        value="customer_service"
                    )
                    use_case = gr.Textbox(
                        label="Specific Use Case",
                        placeholder="E.g., E-commerce support, Financial analysis, Blog writing"
                    )
                    model_choice = gr.Dropdown(
                        label="AI Model",
                        choices=["gpt-4", "gpt-3.5-turbo", "claude-3-opus", "gemini-pro"],
                        value="gpt-4"
                    )
                    include_memory = gr.Checkbox(label="Include Memory System", value=True)
                    include_tools = gr.Checkbox(label="Include External Tools", value=True)
                    
                    generate_btn = gr.Button("Generate Agent Code", variant="primary")
                
                with gr.Column():
                    code_output = gr.Code(label="Generated Agent Code", language="python")
            
            generate_btn.click(
                generate_agent_code,
                inputs=[agent_type, use_case, model_choice, include_memory, include_tools],
                outputs=code_output
            )
        
        # Interactive Demo Tab
        with gr.TabItem("🤖 Try an Agent"):
            gr.Markdown("""
            ### Interactive Agent Demo
            
            Experience what AI agents can do. This is a simulation - in the course,
            you'll build real agents that connect to actual AI models.
            """)
            
            with gr.Row():
                with gr.Column():
                    demo_agent_type = gr.Radio(
                        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
            )
        
        # Resources Tab
        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)
            """)

# Launch the app
if __name__ == "__main__":
    app.launch()