Update app.py
Browse files
app.py
CHANGED
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# app.py
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import streamlit as st
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import json
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import zipfile
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import time
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import os
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import openai
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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# Initialize OpenAI API key
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# Combined agent classes
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class TopicAgent:
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def generate_outline(self, topic, duration, difficulty):
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if not openai.api_key:
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response = openai.ChatCompletion.create(
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model="gpt-4",
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messages=[
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{
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)
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return json.loads(response.choices[0].message
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except:
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return self._mock_outline(topic, duration, difficulty)
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def _mock_outline(self, topic, duration, difficulty):
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"duration": f"{duration} hours",
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"difficulty": difficulty,
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"goals": [
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"Develop
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"
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"
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],
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"modules": [
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{
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"title":
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"duration": "
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"
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]
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},
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{
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"title":
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"duration": "
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]
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}
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]
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@@ -79,16 +105,27 @@ class ContentAgent:
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response = openai.ChatCompletion.create(
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model="gpt-4",
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messages=[
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{
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)
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return json.loads(response.choices[0].message
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except:
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return self._mock_content(outline)
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def _mock_content(self, outline):
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"workshop_title": f"Mastering {outline['topic']}",
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"modules": [
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{
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"title":
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"script":
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"speaker_notes":
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"quiz": [
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{
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"question":
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"options": ["
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"answer": "
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}
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]
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]
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}
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class SlideAgent:
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def generate_slides(self, content):
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class CodeAgent:
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def generate_code(self, content):
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# Initialize agents
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topic_agent = TopicAgent()
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content_agent = ContentAgent()
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slide_agent = SlideAgent()
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code_agent = CodeAgent()
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#
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st.
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# Sidebar configuration
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with st.sidebar:
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st.header("Configuration")
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workshop_topic = st.text_input("Workshop Topic", "Advanced Prompt Engineering")
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duration = st.slider("Duration (hours)", 1.0, 8.0,
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difficulty = st.selectbox("Difficulty
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include_code = st.checkbox("Include Code Labs", True)
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if st.button("β¨ Generate Workshop", type="primary"):
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# Results display
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if
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st.success(f"
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# Download button
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st.download_button(
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label="π₯ Download Workshop Package",
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data=st.session_state.zip_buffer.getvalue(),
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file_name=f"{workshop_topic.replace(' ', '_')}_workshop.zip",
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mime="application/zip"
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)
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# Preview sections
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with st.expander("Workshop Outline"):
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st.json(st.session_state.outline)
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with st.expander("Content Script"):
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st.write(st.session_state.content)
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with st.expander("Slide Deck Preview"):
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st.markdown(st.session_state.slides)
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if st.session_state.code_labs:
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with st.expander("Code Labs"):
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st.code(st.session_state.code_labs)
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# Sales
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st.divider()
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st.subheader("Ready to
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st.
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import streamlit as st
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import json
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import zipfile
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import time
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import os
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import openai
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import requests
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from PIL import Image
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import base64
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import textwrap
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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# Initialize OpenAI API key
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openai.api_key = os.getenv("OPENAI_API_KEY")
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# =============================
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# ENHANCED AGENT IMPLEMENTATION
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# =============================
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class TopicAgent:
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def generate_outline(self, topic, duration, difficulty):
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if not openai.api_key:
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response = openai.ChatCompletion.create(
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model="gpt-4",
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messages=[
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{
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"role": "system",
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"content": "You're an expert corporate trainer creating comprehensive AI workshop outlines."
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},
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{
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"role": "user",
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"content": (
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f"Create a detailed {duration}-hour {difficulty} workshop outline on {topic}. "
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"Include: 4-6 modules with specific learning objectives, hands-on exercises, "
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"and real-world case studies. Format as JSON with keys: "
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"{'topic', 'duration', 'difficulty', 'goals', 'modules': ["
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"{'title', 'duration', 'learning_objectives', 'case_study', 'exercises'}]}"
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)
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}
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],
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temperature=0.3,
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max_tokens=1500
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)
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return json.loads(response.choices[0].message['content'])
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except Exception as e:
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st.error(f"Outline generation error: {str(e)}")
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return self._mock_outline(topic, duration, difficulty)
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def _mock_outline(self, topic, duration, difficulty):
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"duration": f"{duration} hours",
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"difficulty": difficulty,
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"goals": [
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"Master core concepts and advanced techniques",
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"Develop practical implementation skills",
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"Learn industry best practices and case studies",
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"Build confidence in real-world applications"
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],
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"modules": [
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{
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"title": "Foundations of Prompt Engineering",
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"duration": "90 min",
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"learning_objectives": [
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"Understand prompt components and structure",
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"Learn prompt patterns and anti-patterns",
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"Master zero-shot and few-shot prompting"
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],
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"case_study": "How Anthropic improved customer support with prompt engineering",
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"exercises": [
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"Craft effective prompts for different scenarios",
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"Optimize prompts for specific AI models"
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]
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},
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{
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"title": "Advanced Techniques & Strategies",
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"duration": "120 min",
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"learning_objectives": [
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"Implement chain-of-thought prompting",
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"Use meta-prompts for complex tasks",
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"Apply self-consistency methods"
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],
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"case_study": "OpenAI's approach to prompt engineering in GPT-4",
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"exercises": [
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"Design prompts for multi-step reasoning",
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"Create self-correcting prompt systems"
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]
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}
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]
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response = openai.ChatCompletion.create(
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model="gpt-4",
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messages=[
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{
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"role": "system",
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"content": "You're a corporate training content developer creating detailed workshop materials."
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},
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{
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"role": "user",
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"content": (
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f"Expand this workshop outline into comprehensive content: {json.dumps(outline)}. "
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"For each module, include: detailed script (3-5 paragraphs), speaker notes (bullet points), "
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"3 quiz questions with explanations, and exercise instructions. Format as JSON with keys: "
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"{'workshop_title', 'modules': [{'title', 'script', 'speaker_notes', 'quiz': ["
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"{'question', 'options', 'answer', 'explanation'}], 'exercise_instructions'}]}"
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)
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}
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],
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temperature=0.4,
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max_tokens=2000
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)
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return json.loads(response.choices[0].message['content'])
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except Exception as e:
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st.error(f"Content generation error: {str(e)}")
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return self._mock_content(outline)
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def _mock_content(self, outline):
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"workshop_title": f"Mastering {outline['topic']}",
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"modules": [
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{
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"title": "Foundations of Prompt Engineering",
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"script": "This module introduces the core concepts of effective prompt engineering...",
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"speaker_notes": [
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"Emphasize the importance of clear instructions",
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"Show examples of good vs bad prompts",
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"Discuss token limitations and their impact"
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],
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"quiz": [
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{
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"question": "What's the most important element of a good prompt?",
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"options": ["Length", "Specificity", "Complexity", "Creativity"],
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"answer": "Specificity",
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"explanation": "Specific prompts yield more accurate and relevant responses"
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}
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],
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"exercise_instructions": "Create a prompt that extracts key insights from a financial report..."
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}
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]
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}
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class SlideAgent:
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def generate_slides(self, content):
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if not openai.api_key:
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return self._mock_slides(content)
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try:
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response = openai.ChatCompletion.create(
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model="gpt-4",
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messages=[
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{
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"role": "system",
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"content": "You create professional slide decks in Markdown format using Marp syntax."
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},
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{
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"role": "user",
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"content": (
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f"Create a slide deck for this workshop content: {json.dumps(content)}. "
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"Use Marp Markdown format with themes and visual elements. "
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"Include: title slide, module slides with key points, case studies, "
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"exercise instructions, and summary slides. Make it visually appealing."
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)
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}
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],
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temperature=0.2,
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max_tokens=2500
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)
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return response.choices[0].message['content']
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except Exception as e:
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st.error(f"Slide generation error: {str(e)}")
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return self._mock_slides(content)
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def _mock_slides(self, content):
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return f"""---
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marp: true
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theme: gaia
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backgroundColor: #fff
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backgroundImage: url('https://marp.app/assets/hero-background.svg')
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---
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# {content['workshop_title']}
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## Comprehensive Corporate Training Program
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---
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## Module 1: Foundations of Prompt Engineering
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- Core concepts and principles
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- Patterns and anti-patterns
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- Practical implementation techniques
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---
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## Case Study
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### Anthropic's Customer Support Implementation
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- 40% faster resolution times
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- 25% reduction in training costs
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- 92% customer satisfaction
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---
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## Exercises
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1. Craft effective prompts for different scenarios
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2. Optimize prompts for specific AI models
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3. Analyze and refine prompt performance
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"""
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class CodeAgent:
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def generate_code(self, content):
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if not openai.api_key:
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+
return self._mock_code(content)
|
228 |
+
|
229 |
+
try:
|
230 |
+
response = openai.ChatCompletion.create(
|
231 |
+
model="gpt-4",
|
232 |
+
messages=[
|
233 |
+
{
|
234 |
+
"role": "system",
|
235 |
+
"content": "You create practical code labs for technical workshops."
|
236 |
+
},
|
237 |
+
{
|
238 |
+
"role": "user",
|
239 |
+
"content": (
|
240 |
+
f"Create a Jupyter notebook with code exercises for this workshop: {json.dumps(content)}. "
|
241 |
+
"Include: setup instructions, practical exercises with solutions, "
|
242 |
+
"and real-world implementation examples. Use Python with popular AI libraries."
|
243 |
+
)
|
244 |
+
}
|
245 |
+
],
|
246 |
+
temperature=0.3,
|
247 |
+
max_tokens=2000
|
248 |
+
)
|
249 |
+
return response.choices[0].message['content']
|
250 |
+
except Exception as e:
|
251 |
+
st.error(f"Code generation error: {str(e)}")
|
252 |
+
return self._mock_code(content)
|
253 |
+
|
254 |
+
def _mock_code(self, content):
|
255 |
+
return f"""# {content['workshop_title']} - Code Labs
|
256 |
+
|
257 |
+
import openai
|
258 |
+
import pandas as pd
|
259 |
+
|
260 |
+
## Exercise 1: Basic Prompt Engineering
|
261 |
+
def generate_response(prompt):
|
262 |
+
response = openai.ChatCompletion.create(
|
263 |
+
model="gpt-4",
|
264 |
+
messages=[{{"role": "user", "content": prompt}}]
|
265 |
+
)
|
266 |
+
return response.choices[0].message['content']
|
267 |
+
|
268 |
+
# Test your function
|
269 |
+
print(generate_response("Explain quantum computing in simple terms"))
|
270 |
+
|
271 |
+
## Exercise 2: Advanced Prompt Patterns
|
272 |
+
# TODO: Implement chain-of-thought prompting
|
273 |
+
# TODO: Create meta-prompts for complex tasks
|
274 |
+
|
275 |
+
## Real-World Implementation
|
276 |
+
# TODO: Build a customer support question classifier
|
277 |
+
"""
|
278 |
+
|
279 |
+
class DesignAgent:
|
280 |
+
def generate_design(self, slide_content):
|
281 |
+
if not openai.api_key:
|
282 |
+
return None
|
283 |
+
|
284 |
+
try:
|
285 |
+
response = openai.Image.create(
|
286 |
+
prompt=f"Create a professional slide background for a corporate AI workshop about: {slide_content[:500]}",
|
287 |
+
n=1,
|
288 |
+
size="1024x1024"
|
289 |
+
)
|
290 |
+
return response['data'][0]['url']
|
291 |
+
except Exception as e:
|
292 |
+
st.error(f"Design generation error: {str(e)}")
|
293 |
+
return None
|
294 |
|
295 |
# Initialize agents
|
296 |
topic_agent = TopicAgent()
|
297 |
content_agent = ContentAgent()
|
298 |
slide_agent = SlideAgent()
|
299 |
code_agent = CodeAgent()
|
300 |
+
design_agent = DesignAgent()
|
301 |
+
|
302 |
+
# =====================
|
303 |
+
# STREAMLIT APPLICATION
|
304 |
+
# =====================
|
305 |
+
|
306 |
+
st.set_page_config(
|
307 |
+
page_title="Workshop in a Box Pro",
|
308 |
+
layout="wide",
|
309 |
+
initial_sidebar_state="expanded"
|
310 |
+
)
|
311 |
|
312 |
+
# Custom CSS
|
313 |
+
st.markdown("""
|
314 |
+
<style>
|
315 |
+
.stApp {
|
316 |
+
background: linear-gradient(135deg, #6a11cb 0%, #2575fc 100%);
|
317 |
+
color: #fff;
|
318 |
+
}
|
319 |
+
.stTextInput>div>div>input, .stSlider>div>div>div>div {
|
320 |
+
background-color: rgba(255,255,255,0.1) !important;
|
321 |
+
color: white !important;
|
322 |
+
}
|
323 |
+
.stButton>button {
|
324 |
+
background: linear-gradient(to right, #00b09b, #96c93d) !important;
|
325 |
+
color: white !important;
|
326 |
+
border: none;
|
327 |
+
border-radius: 30px;
|
328 |
+
padding: 10px 25px;
|
329 |
+
font-size: 16px;
|
330 |
+
font-weight: bold;
|
331 |
+
}
|
332 |
+
.stDownloadButton>button {
|
333 |
+
background: linear-gradient(to right, #ff5e62, #ff9966) !important;
|
334 |
+
}
|
335 |
+
.stExpander {
|
336 |
+
background-color: rgba(0,0,0,0.2) !important;
|
337 |
+
border-radius: 10px;
|
338 |
+
padding: 15px;
|
339 |
+
}
|
340 |
+
</style>
|
341 |
+
""", unsafe_allow_html=True)
|
342 |
+
|
343 |
+
# Header
|
344 |
+
col1, col2 = st.columns([1, 3])
|
345 |
+
with col1:
|
346 |
+
st.image("https://cdn-icons-png.flaticon.com/512/1995/1995485.png", width=100)
|
347 |
+
with col2:
|
348 |
+
st.title("π€ Workshop in a Box Pro")
|
349 |
+
st.caption("Generate Premium Corporate AI Training Workshops in Minutes")
|
350 |
|
351 |
# Sidebar configuration
|
352 |
with st.sidebar:
|
353 |
+
st.header("βοΈ Workshop Configuration")
|
354 |
workshop_topic = st.text_input("Workshop Topic", "Advanced Prompt Engineering")
|
355 |
+
duration = st.slider("Duration (hours)", 1.0, 8.0, 3.0, 0.5)
|
356 |
+
difficulty = st.selectbox("Difficulty Level",
|
357 |
+
["Beginner", "Intermediate", "Advanced", "Expert"])
|
358 |
include_code = st.checkbox("Include Code Labs", True)
|
359 |
+
include_design = st.checkbox("Generate Visual Designs", True)
|
360 |
|
361 |
+
if st.button("β¨ Generate Workshop", type="primary", use_container_width=True):
|
362 |
+
st.session_state.generating = True
|
363 |
+
|
364 |
+
# Generation pipeline
|
365 |
+
if hasattr(st.session_state, 'generating'):
|
366 |
+
with st.spinner("π Creating your premium workshop materials..."):
|
367 |
+
start_time = time.time()
|
368 |
+
|
369 |
+
# Agent pipeline
|
370 |
+
outline = topic_agent.generate_outline(workshop_topic, duration, difficulty)
|
371 |
+
content = content_agent.generate_content(outline)
|
372 |
+
slides = slide_agent.generate_slides(content)
|
373 |
+
code_labs = code_agent.generate_code(content) if include_code else None
|
374 |
+
design_url = design_agent.generate_design(slides) if include_design else None
|
375 |
+
|
376 |
+
# Prepare download package
|
377 |
+
zip_buffer = io.BytesIO()
|
378 |
+
with zipfile.ZipFile(zip_buffer, "a") as zip_file:
|
379 |
+
zip_file.writestr("outline.json", json.dumps(outline, indent=2))
|
380 |
+
zip_file.writestr("content.json", json.dumps(content, indent=2))
|
381 |
+
zip_file.writestr("slides.md", slides)
|
382 |
+
if code_labs:
|
383 |
+
zip_file.writestr("code_labs.ipynb", code_labs)
|
384 |
+
if design_url:
|
385 |
+
try:
|
386 |
+
img_data = requests.get(design_url).content
|
387 |
+
zip_file.writestr("slide_design.png", img_data)
|
388 |
+
except:
|
389 |
+
pass
|
390 |
+
|
391 |
+
# Store results
|
392 |
+
st.session_state.outline = outline
|
393 |
+
st.session_state.content = content
|
394 |
+
st.session_state.slides = slides
|
395 |
+
st.session_state.code_labs = code_labs
|
396 |
+
st.session_state.design_url = design_url
|
397 |
+
st.session_state.zip_buffer = zip_buffer
|
398 |
+
st.session_state.gen_time = round(time.time() - start_time, 2)
|
399 |
+
st.session_state.generated = True
|
400 |
+
st.session_state.generating = False
|
401 |
|
402 |
# Results display
|
403 |
+
if hasattr(st.session_state, 'generated'):
|
404 |
+
st.success(f"β
Premium workshop materials generated in {st.session_state.gen_time} seconds!")
|
405 |
|
406 |
# Download button
|
407 |
st.download_button(
|
408 |
label="π₯ Download Workshop Package",
|
409 |
data=st.session_state.zip_buffer.getvalue(),
|
410 |
file_name=f"{workshop_topic.replace(' ', '_')}_workshop.zip",
|
411 |
+
mime="application/zip",
|
412 |
+
use_container_width=True
|
413 |
)
|
414 |
|
415 |
# Preview sections
|
416 |
+
with st.expander("π Workshop Outline", expanded=True):
|
417 |
st.json(st.session_state.outline)
|
418 |
|
419 |
+
with st.expander("π Content Script"):
|
420 |
st.write(st.session_state.content)
|
421 |
|
422 |
+
with st.expander("π₯οΈ Slide Deck Preview"):
|
423 |
+
st.markdown("```markdown\n" + textwrap.dedent(st.session_state.slides[:2000]) + "\n```")
|
424 |
|
425 |
if st.session_state.code_labs:
|
426 |
+
with st.expander("π» Code Labs"):
|
427 |
st.code(st.session_state.code_labs)
|
428 |
+
|
429 |
+
if st.session_state.design_url:
|
430 |
+
with st.expander("π¨ Generated Design"):
|
431 |
+
st.image(st.session_state.design_url, caption="Custom Slide Design")
|
432 |
|
433 |
+
# Sales and booking section
|
434 |
st.divider()
|
435 |
+
st.subheader("π Ready to Deliver This Workshop?")
|
436 |
+
st.markdown("""
|
437 |
+
### Premium Corporate Training Package
|
438 |
+
- **Live Workshop Delivery**: $10,000 per session
|
439 |
+
- **On-Demand Course**: $5,000 (unlimited access)
|
440 |
+
- **Pilot Program**: $1,000 refundable deposit
|
441 |
+
|
442 |
+
β¨ **All inclusive**: Customization, materials, and follow-up support
|
443 |
+
""")
|
444 |
+
|
445 |
+
col1, col2 = st.columns(2)
|
446 |
+
with col1:
|
447 |
+
st.link_button("π
Book a Live Workshop", "https://calendly.com/your-link",
|
448 |
+
use_container_width=True)
|
449 |
+
with col2:
|
450 |
+
st.link_button("π³ Purchase On-Demand Course", "https://your-store.com",
|
451 |
+
use_container_width=True)
|
452 |
+
|
453 |
+
# Debug info
|
454 |
+
with st.sidebar:
|
455 |
+
st.divider()
|
456 |
+
if openai.api_key:
|
457 |
+
st.success("OpenAI API Connected")
|
458 |
+
else:
|
459 |
+
st.warning("OpenAI API not set - using enhanced mock data")
|
460 |
+
|
461 |
+
st.info("""
|
462 |
+
**Premium Features:**
|
463 |
+
- AI-generated slide designs
|
464 |
+
- Real-world case studies
|
465 |
+
- Practical code labs
|
466 |
+
- Professional templates
|
467 |
+
""")
|
468 |
+
|
469 |
+
# How it works section
|
470 |
+
st.divider()
|
471 |
+
st.subheader("π‘ How It Works")
|
472 |
+
st.markdown("""
|
473 |
+
1. **Configure** your workshop topic and parameters
|
474 |
+
2. **Generate** premium training materials in seconds
|
475 |
+
3. **Customize** the content to your specific needs
|
476 |
+
4. **Deliver** high-value corporate training at $10K/session
|
477 |
+
5. **Reuse** the materials for unlimited revenue
|
478 |
+
|
479 |
+
*"Created 3 workshops in 15 minutes and booked $30K in contracts"* - Sarah T., AI Training Consultant
|
480 |
+
""")
|