Update app.py

app.py

@@ -1,3 +1,4 @@
+# app.py
 import streamlit as st
 import json
 import zipfile
@@ -10,21 +11,133 @@ from dotenv import load_dotenv
 # Load environment variables
 load_dotenv()
 
-# Import agents directly
-from topic_agent import TopicAgent
-from content_agent import ContentAgent
-from slide_agent import SlideAgent
-from code_agent import CodeAgent
+# 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(os.getenv("OPENAI_API_KEY"))
-content_agent = ContentAgent(os.getenv("OPENAI_API_KEY"))
+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.image("https://i.imgur.com/3fXQ0DB.png", width=200)
 st.title("🤖 Workshop in a Box")
 st.caption("Generate corporate AI training workshops in minutes")