app.py

import streamlit as st
import json
import zipfile
import io
import time
import os
import openai
import requests
from PIL import Image
import base64
import textwrap
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Initialize OpenAI API key
openai.api_key = os.getenv("OPENAI_API_KEY")

# =============================
# ENHANCED AGENT IMPLEMENTATION
# =============================

class TopicAgent:
    def generate_outline(self, topic, duration, difficulty):
        if not openai.api_key:
            return self._mock_outline(topic, duration, difficulty)
            
        try:
            response = openai.ChatCompletion.create(
                model="gpt-4",
                messages=[
                    {
                        "role": "system",
                        "content": "You're an expert corporate trainer creating comprehensive AI workshop outlines."
                    },
                    {
                        "role": "user",
                        "content": (
                            f"Create a detailed {duration}-hour {difficulty} workshop outline on {topic}. "
                            "Include: 4-6 modules with specific learning objectives, hands-on exercises, "
                            "and real-world case studies. Format as JSON with keys: "
                            "{'topic', 'duration', 'difficulty', 'goals', 'modules': ["
                            "{'title', 'duration', 'learning_objectives', 'case_study', 'exercises'}]}"
                        )
                    }
                ],
                temperature=0.3,
                max_tokens=1500
            )
            return json.loads(response.choices[0].message['content'])
        except Exception as e:
            st.error(f"Outline generation error: {str(e)}")
            return self._mock_outline(topic, duration, difficulty)
    
    def _mock_outline(self, topic, duration, difficulty):
        return {
            "topic": topic,
            "duration": f"{duration} hours",
            "difficulty": difficulty,
            "goals": [
                "Master core concepts and advanced techniques",
                "Develop practical implementation skills",
                "Learn industry best practices and case studies",
                "Build confidence in real-world applications"
            ],
            "modules": [
                {
                    "title": "Foundations of Prompt Engineering",
                    "duration": "90 min",
                    "learning_objectives": [
                        "Understand prompt components and structure",
                        "Learn prompt patterns and anti-patterns",
                        "Master zero-shot and few-shot prompting"
                    ],
                    "case_study": "How Anthropic improved customer support with prompt engineering",
                    "exercises": [
                        "Craft effective prompts for different scenarios",
                        "Optimize prompts for specific AI models"
                    ]
                },
                {
                    "title": "Advanced Techniques & Strategies",
                    "duration": "120 min",
                    "learning_objectives": [
                        "Implement chain-of-thought prompting",
                        "Use meta-prompts for complex tasks",
                        "Apply self-consistency methods"
                    ],
                    "case_study": "OpenAI's approach to prompt engineering in GPT-4",
                    "exercises": [
                        "Design prompts for multi-step reasoning",
                        "Create self-correcting prompt systems"
                    ]
                }
            ]
        }

class ContentAgent:
    def generate_content(self, outline):
        if not openai.api_key:
            return self._mock_content(outline)
            
        try:
            response = openai.ChatCompletion.create(
                model="gpt-4",
                messages=[
                    {
                        "role": "system",
                        "content": "You're a corporate training content developer creating detailed workshop materials."
                    },
                    {
                        "role": "user",
                        "content": (
                            f"Expand this workshop outline into comprehensive content: {json.dumps(outline)}. "
                            "For each module, include: detailed script (3-5 paragraphs), speaker notes (bullet points), "
                            "3 quiz questions with explanations, and exercise instructions. Format as JSON with keys: "
                            "{'workshop_title', 'modules': [{'title', 'script', 'speaker_notes', 'quiz': ["
                            "{'question', 'options', 'answer', 'explanation'}], 'exercise_instructions'}]}"
                        )
                    }
                ],
                temperature=0.4,
                max_tokens=2000
            )
            return json.loads(response.choices[0].message['content'])
        except Exception as e:
            st.error(f"Content generation error: {str(e)}")
            return self._mock_content(outline)
    
    def _mock_content(self, outline):
        return {
            "workshop_title": f"Mastering {outline['topic']}",
            "modules": [
                {
                    "title": "Foundations of Prompt Engineering",
                    "script": "This module introduces the core concepts of effective prompt engineering...",
                    "speaker_notes": [
                        "Emphasize the importance of clear instructions",
                        "Show examples of good vs bad prompts",
                        "Discuss token limitations and their impact"
                    ],
                    "quiz": [
                        {
                            "question": "What's the most important element of a good prompt?",
                            "options": ["Length", "Specificity", "Complexity", "Creativity"],
                            "answer": "Specificity",
                            "explanation": "Specific prompts yield more accurate and relevant responses"
                        }
                    ],
                    "exercise_instructions": "Create a prompt that extracts key insights from a financial report..."
                }
            ]
        }

class SlideAgent:
    def generate_slides(self, content):
        if not openai.api_key:
            return self._mock_slides(content)
            
        try:
            response = openai.ChatCompletion.create(
                model="gpt-4",
                messages=[
                    {
                        "role": "system",
                        "content": "You create professional slide decks in Markdown format using Marp syntax."
                    },
                    {
                        "role": "user",
                        "content": (
                            f"Create a slide deck for this workshop content: {json.dumps(content)}. "
                            "Use Marp Markdown format with themes and visual elements. "
                            "Include: title slide, module slides with key points, case studies, "
                            "exercise instructions, and summary slides. Make it visually appealing."
                        )
                    }
                ],
                temperature=0.2,
                max_tokens=2500
            )
            return response.choices[0].message['content']
        except Exception as e:
            st.error(f"Slide generation error: {str(e)}")
            return self._mock_slides(content)
    
    def _mock_slides(self, content):
        return f"""---
marp: true
theme: gaia
backgroundColor: #fff
backgroundImage: url('https://marp.app/assets/hero-background.svg')
---

# {content['workshop_title']}
## Comprehensive Corporate Training Program

---

## Module 1: Foundations of Prompt Engineering
![w:250](https://images.unsplash.com/photo-1677442135722-5fcdbdf1b7e6)

- Core concepts and principles
- Patterns and anti-patterns
- Practical implementation techniques

---

## Case Study
### Anthropic's Customer Support Implementation
- 40% faster resolution times
- 25% reduction in training costs
- 92% customer satisfaction

---

## Exercises
1. Craft effective prompts for different scenarios
2. Optimize prompts for specific AI models
3. Analyze and refine prompt performance

"""

class CodeAgent:
    def generate_code(self, content):
        if not openai.api_key:
            return self._mock_code(content)
            
        try:
            response = openai.ChatCompletion.create(
                model="gpt-4",
                messages=[
                    {
                        "role": "system",
                        "content": "You create practical code labs for technical workshops."
                    },
                    {
                        "role": "user",
                        "content": (
                            f"Create a Jupyter notebook with code exercises for this workshop: {json.dumps(content)}. "
                            "Include: setup instructions, practical exercises with solutions, "
                            "and real-world implementation examples. Use Python with popular AI libraries."
                        )
                    }
                ],
                temperature=0.3,
                max_tokens=2000
            )
            return response.choices[0].message['content']
        except Exception as e:
            st.error(f"Code generation error: {str(e)}")
            return self._mock_code(content)
    
    def _mock_code(self, content):
        return f"""# {content['workshop_title']} - Code Labs

import openai
import pandas as pd

## Exercise 1: Basic Prompt Engineering
def generate_response(prompt):
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{{"role": "user", "content": prompt}}]
    )
    return response.choices[0].message['content']

# Test your function
print(generate_response("Explain quantum computing in simple terms"))

## Exercise 2: Advanced Prompt Patterns
# TODO: Implement chain-of-thought prompting
# TODO: Create meta-prompts for complex tasks

## Real-World Implementation
# TODO: Build a customer support question classifier
"""

class DesignAgent:
    def generate_design(self, slide_content):
        if not openai.api_key:
            return None
            
        try:
            response = openai.Image.create(
                prompt=f"Create a professional slide background for a corporate AI workshop about: {slide_content[:500]}",
                n=1,
                size="1024x1024"
            )
            return response['data'][0]['url']
        except Exception as e:
            st.error(f"Design generation error: {str(e)}")
            return None

# Initialize agents
topic_agent = TopicAgent()
content_agent = ContentAgent()
slide_agent = SlideAgent()
code_agent = CodeAgent()
design_agent = DesignAgent()

# =====================
# STREAMLIT APPLICATION
# =====================

st.set_page_config(
    page_title="Workshop in a Box Pro",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS
st.markdown("""
<style>
    .stApp {
        background: linear-gradient(135deg, #6a11cb 0%, #2575fc 100%);
        color: #fff;
    }
    .stTextInput>div>div>input, .stSlider>div>div>div>div {
        background-color: rgba(255,255,255,0.1) !important;
        color: white !important;
    }
    .stButton>button {
        background: linear-gradient(to right, #00b09b, #96c93d) !important;
        color: white !important;
        border: none;
        border-radius: 30px;
        padding: 10px 25px;
        font-size: 16px;
        font-weight: bold;
    }
    .stDownloadButton>button {
        background: linear-gradient(to right, #ff5e62, #ff9966) !important;
    }
    .stExpander {
        background-color: rgba(0,0,0,0.2) !important;
        border-radius: 10px;
        padding: 15px;
    }
</style>
""", unsafe_allow_html=True)

# Header
col1, col2 = st.columns([1, 3])
with col1:
    st.image("https://cdn-icons-png.flaticon.com/512/1995/1995485.png", width=100)
with col2:
    st.title("🤖 Workshop in a Box Pro")
    st.caption("Generate Premium Corporate AI Training Workshops in Minutes")

# Sidebar configuration
with st.sidebar:
    st.header("⚙️ Workshop Configuration")
    workshop_topic = st.text_input("Workshop Topic", "Advanced Prompt Engineering")
    duration = st.slider("Duration (hours)", 1.0, 8.0, 3.0, 0.5)
    difficulty = st.selectbox("Difficulty Level", 
                            ["Beginner", "Intermediate", "Advanced", "Expert"])
    include_code = st.checkbox("Include Code Labs", True)
    include_design = st.checkbox("Generate Visual Designs", True)
    
    if st.button("✨ Generate Workshop", type="primary", use_container_width=True):
        st.session_state.generating = True

# Generation pipeline
if hasattr(st.session_state, 'generating'):
    with st.spinner("🚀 Creating your premium workshop materials..."):
        start_time = time.time()
        
        # Agent pipeline
        outline = topic_agent.generate_outline(workshop_topic, duration, difficulty)
        content = content_agent.generate_content(outline)
        slides = slide_agent.generate_slides(content)
        code_labs = code_agent.generate_code(content) if include_code else None
        design_url = design_agent.generate_design(slides) if include_design else None
        
        # Prepare download package
        zip_buffer = io.BytesIO()
        with zipfile.ZipFile(zip_buffer, "a") as zip_file:
            zip_file.writestr("outline.json", json.dumps(outline, indent=2))
            zip_file.writestr("content.json", json.dumps(content, indent=2))
            zip_file.writestr("slides.md", slides)
            if code_labs:
                zip_file.writestr("code_labs.ipynb", code_labs)
            if design_url:
                try:
                    img_data = requests.get(design_url).content
                    zip_file.writestr("slide_design.png", img_data)
                except:
                    pass
        
        # Store results
        st.session_state.outline = outline
        st.session_state.content = content
        st.session_state.slides = slides
        st.session_state.code_labs = code_labs
        st.session_state.design_url = design_url
        st.session_state.zip_buffer = zip_buffer
        st.session_state.gen_time = round(time.time() - start_time, 2)
        st.session_state.generated = True
        st.session_state.generating = False

# Results display
if hasattr(st.session_state, 'generated'):
    st.success(f"✅ Premium workshop materials generated in {st.session_state.gen_time} seconds!")
    
    # Download button
    st.download_button(
        label="📥 Download Workshop Package",
        data=st.session_state.zip_buffer.getvalue(),
        file_name=f"{workshop_topic.replace(' ', '_')}_workshop.zip",
        mime="application/zip",
        use_container_width=True
    )
    
    # Preview sections
    with st.expander("📝 Workshop Outline", expanded=True):
        st.json(st.session_state.outline)
    
    with st.expander("📄 Content Script"):
        st.write(st.session_state.content)
    
    with st.expander("🖥️ Slide Deck Preview"):
        st.markdown("```markdown\n" + textwrap.dedent(st.session_state.slides[:2000]) + "\n```")
    
    if st.session_state.code_labs:
        with st.expander("💻 Code Labs"):
            st.code(st.session_state.code_labs)
    
    if st.session_state.design_url:
        with st.expander("🎨 Generated Design"):
            st.image(st.session_state.design_url, caption="Custom Slide Design")

# Sales and booking section
st.divider()
st.subheader("🚀 Ready to Deliver This Workshop?")
st.markdown("""
### Premium Corporate Training Package
- **Live Workshop Delivery**: $10,000 per session
- **On-Demand Course**: $5,000 (unlimited access)
- **Pilot Program**: $1,000 refundable deposit

✨ **All inclusive**: Customization, materials, and follow-up support
""")

col1, col2 = st.columns(2)
with col1:
    st.link_button("📅 Book a Live Workshop", "https://calendly.com/your-link", 
                  use_container_width=True)
with col2:
    st.link_button("💳 Purchase On-Demand Course", "https://your-store.com", 
                  use_container_width=True)

# Debug info
with st.sidebar:
    st.divider()
    if openai.api_key:
        st.success("OpenAI API Connected")
    else:
        st.warning("OpenAI API not set - using enhanced mock data")
    
    st.info("""
    **Premium Features:**
    - AI-generated slide designs
    - Real-world case studies
    - Practical code labs
    - Professional templates
    """)

# How it works section
st.divider()
st.subheader("💡 How It Works")
st.markdown("""
1. **Configure** your workshop topic and parameters
2. **Generate** premium training materials in seconds
3. **Customize** the content to your specific needs
4. **Deliver** high-value corporate training at $10K/session
5. **Reuse** the materials for unlimited revenue

*"Created 3 workshops in 15 minutes and booked $30K in contracts"* - Sarah T., AI Training Consultant
""")