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# app.py
import streamlit as st
import json
import zipfile
import io
import time
import os
import openai
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Initialize OpenAI API key
if os.getenv("OPENAI_API_KEY"):
openai.api_key = os.getenv("OPENAI_API_KEY")
# Combined agent classes
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 AI workshop outlines"},
{"role": "user", "content": (
f"Create a {duration}-hour {difficulty} workshop outline on {topic}. "
"Include: 1) Key learning goals, 2) 4 modules with titles and durations, "
"3) Hands-on exercises per module. Output as JSON."
)}
]
)
return json.loads(response.choices[0].message.content)
except:
return self._mock_outline(topic, duration, difficulty)
def _mock_outline(self, topic, duration, difficulty):
return {
"topic": topic,
"duration": f"{duration} hours",
"difficulty": difficulty,
"goals": [
f"Master advanced {topic} techniques",
"Develop industry-specific applications",
"Build and evaluate complex AI workflows",
"Implement best practices for production"
],
"modules": [
{
"title": f"Fundamentals of {topic}",
"duration": "30 min",
"learning_points": [
"Core principles and terminology",
"Patterns and anti-patterns",
"Evaluation frameworks"
]
},
{
"title": f"{topic} for Enterprise Applications",
"duration": "45 min",
"learning_points": [
"Industry-specific use cases",
"Integration with existing systems",
"Scalability considerations"
]
}
]
}
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 create detailed workshop content from outlines"},
{"role": "user", "content": (
f"Create workshop content from this outline: {json.dumps(outline)}. "
"Include: 1) Detailed scripts, 2) Speaker notes, 3) 3 quiz questions per module, "
"4) Hands-on exercises. Output as JSON."
)}
]
)
return json.loads(response.choices[0].message.content)
except:
return self._mock_content(outline)
def _mock_content(self, outline):
return {
"workshop_title": f"Mastering {outline['topic']}",
"modules": [
{
"title": module["title"],
"script": f"Comprehensive script for {module['title']}...",
"speaker_notes": f"Key talking points: {', '.join(module['learning_points'])}",
"exercises": [f"Exercise about {point}" for point in module["learning_points"]],
"quiz": [
{
"question": f"Question about {module['title']}",
"options": ["A", "B", "C", "D"],
"answer": "A"
}
]
} for module in outline["modules"]
]
}
class SlideAgent:
def generate_slides(self, content):
markdown_slides = f"# {content['workshop_title']}\n\n"
for i, module in enumerate(content["modules"]):
markdown_slides += f"## Module {i+1}: {module['title']}\n\n"
markdown_slides += f"### Key Learning Points:\n- {module['speaker_notes']}\n\n"
markdown_slides += "### Exercises:\n"
for j, exercise in enumerate(module["exercises"]):
markdown_slides += f"{j+1}. {exercise}\n"
markdown_slides += "\n---\n"
return markdown_slides
class CodeAgent:
def generate_code(self, content):
return f"# {content['workshop_title']} Code Labs\n\n" + \
"import pandas as pd\n\n" + \
"# Hands-on exercises for:\n" + \
"\n".join([f"# - {module['title']}" for module in content["modules"]])
# Initialize agents
topic_agent = TopicAgent()
content_agent = ContentAgent()
slide_agent = SlideAgent()
code_agent = CodeAgent()
# Streamlit UI
st.set_page_config(page_title="Workshop in a Box", layout="wide")
st.title("🤖 Workshop in a Box")
st.caption("Generate corporate AI training workshops in minutes")
# Sidebar configuration
with st.sidebar:
st.header("Configuration")
workshop_topic = st.text_input("Workshop Topic", "Advanced Prompt Engineering")
duration = st.slider("Duration (hours)", 1.0, 8.0, 2.0)
difficulty = st.selectbox("Difficulty", ["Beginner", "Intermediate", "Advanced"])
include_code = st.checkbox("Include Code Labs", True)
if st.button("✨ Generate Workshop", type="primary"):
with st.spinner("Creating your workshop materials..."):
# Agent pipeline
start_time = time.time()
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 {}
# 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)
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.zip_buffer = zip_buffer
st.session_state.gen_time = round(time.time() - start_time, 2)
# Results display
if "outline" in st.session_state:
st.success(f"Generated workshop materials 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"
)
# Preview sections
with st.expander("Workshop Outline"):
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(st.session_state.slides)
if st.session_state.code_labs:
with st.expander("Code Labs"):
st.code(st.session_state.code_labs)
# Sales CTA
st.divider()
st.subheader("Ready to deliver this workshop?")
st.write("**$10K per corporate engagement | $1K refundable pilot deposit**")
st.link_button("🚀 Book Pilot Workshop", "https://calendly.com/your-link")
# Debug: Show API status
if os.getenv("OPENAI_API_KEY"):
st.sidebar.success("OpenAI API connected")
else:
st.sidebar.warning("OpenAI API not set - using mock data") |