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import json
import asyncio
from datetime import datetime
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass, asdict
import logging
from dotenv import load_dotenv
import gradio as gr
from openai import AzureOpenAI
import pandas as pd
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Load environment variables
# load_dotenv()
# Azure OpenAI client configuration
client = AzureOpenAI(
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT", "").replace('"', ''),
api_key=os.getenv("AZURE_OPENAI_KEY", "").replace('"', ''),
api_version=os.getenv("AZURE_OPENAI_API_VERSION", "2024-12-01-preview").replace('"', ''),
)
# Model configurations
LLM_DEPLOYMENT = os.getenv("AZURE_OPENAI_LLM_DEPLOYMENT", "gpt-4.1").replace('"', '')
LLM_MODEL = os.getenv("AZURE_OPENAI_LLM_MODEL", "gpt-4.1").replace('"', '')
@dataclass
class UserProgress:
"""Data class to track user learning progress"""
user_id: str
skill: str
lessons_completed: int = 0
quiz_scores: List[float] = None
current_difficulty: str = "beginner"
streak_days: int = 0
total_time_spent: int = 0 # minutes
last_activity: str = ""
def __post_init__(self):
if self.quiz_scores is None:
self.quiz_scores = []
def get_average_score(self) -> float:
"""Calculate average quiz score"""
return sum(self.quiz_scores) / len(self.quiz_scores) if self.quiz_scores else 0.0
def add_quiz_score(self, score: float):
"""Add a new quiz score and update difficulty if needed"""
self.quiz_scores.append(score)
# Adaptive difficulty adjustment
avg_score = self.get_average_score()
if avg_score >= 0.8 and len(self.quiz_scores) >= 3:
if self.current_difficulty == "beginner":
self.current_difficulty = "intermediate"
elif self.current_difficulty == "intermediate":
self.current_difficulty = "advanced"
elif avg_score < 0.6 and len(self.quiz_scores) >= 3:
if self.current_difficulty == "advanced":
self.current_difficulty = "intermediate"
elif self.current_difficulty == "intermediate":
self.current_difficulty = "beginner"
@dataclass
class Lesson:
"""Data class for lesson content"""
title: str
content: str
skill: str
difficulty: str
duration_minutes: int
key_concepts: List[str]
@dataclass
class Quiz:
"""Data class for quiz content"""
questions: List[Dict]
skill: str
difficulty: str
lesson_title: str
class LessonAgent:
"""Agent responsible for generating personalized micro-lessons"""
def __init__(self, client: AzureOpenAI):
self.client = client
self.model = LLM_DEPLOYMENT
async def generate_lesson(self, skill: str, difficulty: str = "beginner",
previous_lessons: List[str] = None) -> Lesson:
"""Generate a personalized micro-lesson"""
try:
previous_context = ""
if previous_lessons:
previous_context = f"\nPrevious lessons covered: {', '.join(previous_lessons[-3:])}"
prompt = f"""
Create a concise, engaging micro-lesson for the skill: {skill}
Difficulty level: {difficulty}
{previous_context}
Requirements:
- Lesson should be 3-5 minutes to read
- Include practical examples
- Focus on one key concept
- Make it actionable
- Include 3-5 key takeaways
Format your response as JSON with these fields:
{{
"title": "Lesson title",
"content": "Main lesson content (200-400 words)",
"duration_minutes": 4,
"key_concepts": ["concept1", "concept2", "concept3"]
}}
"""
response = self.client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": "You are an expert educator creating micro-lessons. Always respond with valid JSON."},
{"role": "user", "content": prompt}
],
temperature=0.7,
max_tokens=1000
)
lesson_data = json.loads(response.choices[0].message.content)
return Lesson(
title=lesson_data["title"],
content=lesson_data["content"],
skill=skill,
difficulty=difficulty,
duration_minutes=lesson_data["duration_minutes"],
key_concepts=lesson_data["key_concepts"]
)
except Exception as e:
logger.error(f"Error generating lesson: {e}")
# Fallback lesson
return Lesson(
title=f"Introduction to {skill}",
content=f"Let's start learning about {skill}. This is a fundamental skill that can help you grow professionally and personally.",
skill=skill,
difficulty=difficulty,
duration_minutes=3,
key_concepts=["basics", "fundamentals", "getting started"]
)
class QuizAgent:
"""Agent responsible for generating adaptive quizzes"""
def __init__(self, client: AzureOpenAI):
self.client = client
self.model = LLM_DEPLOYMENT
async def generate_quiz(self, lesson: Lesson, user_progress: UserProgress) -> Quiz:
"""Generate an adaptive quiz based on the lesson content"""
try:
avg_score = user_progress.get_average_score()
prompt = f"""
Create a quiz for this lesson:
Title: {lesson.title}
Content: {lesson.content}
Key concepts: {', '.join(lesson.key_concepts)}
User's average score: {avg_score:.1f}
Current difficulty: {lesson.difficulty}
Create 3-5 questions that test understanding of the lesson.
Mix question types: multiple choice, true/false, and short answer.
Format as JSON:
{{
"questions": [
{{
"type": "multiple_choice",
"question": "Question text?",
"options": ["A", "B", "C", "D"],
"correct_answer": "A",
"explanation": "Why this is correct"
}},
{{
"type": "true_false",
"question": "Statement to evaluate",
"correct_answer": true,
"explanation": "Explanation"
}}
]
}}
"""
response = self.client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": "You are a quiz expert. Always respond with valid JSON."},
{"role": "user", "content": prompt}
],
temperature=0.7,
max_tokens=1200
)
quiz_data = json.loads(response.choices[0].message.content)
return Quiz(
questions=quiz_data["questions"],
skill=lesson.skill,
difficulty=lesson.difficulty,
lesson_title=lesson.title
)
except Exception as e:
logger.error(f"Error generating quiz: {e}")
# Fallback quiz
return Quiz(
questions=[{
"type": "multiple_choice",
"question": f"What is the main topic of this lesson about {lesson.skill}?",
"options": [lesson.skill, "Something else", "Not sure", "All of the above"],
"correct_answer": lesson.skill,
"explanation": f"This lesson focuses on {lesson.skill}"
}],
skill=lesson.skill,
difficulty=lesson.difficulty,
lesson_title=lesson.title
)
class ProgressAgent:
"""Agent responsible for tracking progress and making recommendations"""
def __init__(self):
self.user_data: Dict[str, UserProgress] = {}
def get_user_progress(self, user_id: str, skill: str) -> UserProgress:
"""Get or create user progress tracking"""
key = f"{user_id}_{skill}"
if key not in self.user_data:
self.user_data[key] = UserProgress(user_id=user_id, skill=skill)
return self.user_data[key]
def update_progress(self, user_id: str, skill: str, lesson_completed: bool = False,
quiz_score: float = None) -> UserProgress:
"""Update user progress after lesson/quiz completion"""
progress = self.get_user_progress(user_id, skill)
if lesson_completed:
progress.lessons_completed += 1
progress.last_activity = datetime.now().strftime("%Y-%m-%d %H:%M")
if quiz_score is not None:
progress.add_quiz_score(quiz_score)
return progress
def get_recommendation(self, progress: UserProgress) -> str:
"""Generate learning recommendations based on progress"""
avg_score = progress.get_average_score()
if progress.lessons_completed == 0:
return "🎯 Ready to start your learning journey! Begin with your first lesson."
elif avg_score >= 0.8:
return f"🌟 Excellent work! You're mastering {progress.skill}. Ready for the next challenge?"
elif avg_score >= 0.6:
return f"📈 Good progress! Keep practicing {progress.skill} to build confidence."
else:
return f"💪 Don't give up! Review the concepts and try again. Practice makes perfect!"
class AgenticSkillBuilder:
"""Main orchestrator for the agentic skill building platform"""
def __init__(self):
self.lesson_agent = LessonAgent(client)
self.quiz_agent = QuizAgent(client)
self.progress_agent = ProgressAgent()
self.current_lesson: Optional[Lesson] = None
self.current_quiz: Optional[Quiz] = None
self.current_user = "demo_user" # In a real app, this would be from authentication
# Predefined skills
self.predefined_skills = [
"Python Programming", "Spanish Language", "Public Speaking",
"Data Science", "Machine Learning", "JavaScript", "Project Management",
"Digital Marketing", "Creative Writing", "Photography"
]
async def start_lesson(self, skill: str) -> Tuple[str, str, str]:
"""Start a new lesson for the selected skill"""
try:
progress = self.progress_agent.get_user_progress(self.current_user, skill)
# Get list of previous lesson titles for context
previous_lessons = [] # In a real app, you'd store this
self.current_lesson = await self.lesson_agent.generate_lesson(
skill, progress.current_difficulty, previous_lessons
)
lesson_content = f"""
# 📚 {self.current_lesson.title}
**Skill:** {self.current_lesson.skill} | **Level:** {self.current_lesson.difficulty.title()} | **Duration:** ~{self.current_lesson.duration_minutes} min
{self.current_lesson.content}
### 🔑 Key Concepts:
{chr(10).join([f"• {concept}" for concept in self.current_lesson.key_concepts])}
"""
return lesson_content, "✅ Complete Lesson", ""
except Exception as e:
logger.error(f"Error starting lesson: {e}")
return f"❌ Error generating lesson: {str(e)}", "Try Again", ""
async def complete_lesson_and_start_quiz(self) -> Tuple[str, str, str]:
"""Mark lesson as complete and start the quiz"""
if not self.current_lesson:
return "⚠️ No active lesson to complete.", "", ""
try:
# Update progress
progress = self.progress_agent.update_progress(
self.current_user, self.current_lesson.skill, lesson_completed=True
)
# Generate quiz
self.current_quiz = await self.quiz_agent.generate_quiz(self.current_lesson, progress)
quiz_content = f"""
# 🧠 Quiz: {self.current_lesson.title}
Test your understanding of the lesson. Answer all questions to see your results!
"""
# Add questions to the content
for i, q in enumerate(self.current_quiz.questions, 1):
quiz_content += f"\n**Question {i}:** {q['question']}\n"
if q['type'] == 'multiple_choice':
quiz_content += f"Options: {', '.join(q['options'])}\n"
elif q['type'] == 'true_false':
quiz_content += "Answer: True or False\n"
return quiz_content, "📝 Submit Quiz", ""
except Exception as e:
logger.error(f"Error generating quiz: {e}")
return f"❌ Error generating quiz: {str(e)}", "", ""
def submit_quiz(self, *answers) -> Tuple[str, str, str]:
"""Process quiz submission and show results"""
if not self.current_quiz:
return "⚠️ No active quiz to submit.", "", ""
try:
correct_answers = 0
total_questions = len(self.current_quiz.questions)
results = []
for i, (question, answer) in enumerate(zip(self.current_quiz.questions, answers)):
if answer is None or answer == "":
continue
is_correct = False
correct_answer = question['correct_answer']
if question['type'] == 'multiple_choice':
is_correct = answer.strip().upper() == str(correct_answer).upper()
elif question['type'] == 'true_false':
is_correct = answer.lower() == str(correct_answer).lower()
if is_correct:
correct_answers += 1
results.append({
'question': question['question'],
'your_answer': answer,
'correct_answer': correct_answer,
'is_correct': is_correct,
'explanation': question.get('explanation', '')
})
score = correct_answers / total_questions if total_questions > 0 else 0
# Update progress with quiz score
progress = self.progress_agent.update_progress(
self.current_user, self.current_lesson.skill, quiz_score=score
)
# Generate results content
results_content = f"""
# 🎯 Quiz Results
**Score:** {correct_answers}/{total_questions} ({score:.1%})
**Performance:** {'🌟 Excellent!' if score >= 0.8 else '👍 Good work!' if score >= 0.6 else '💪 Keep practicing!'}
### Detailed Results:
"""
for i, result in enumerate(results, 1):
status = "✅" if result['is_correct'] else "❌"
results_content += f"""
**Q{i}:** {result['question']}
{status} Your answer: {result['your_answer']}
Correct answer: {result['correct_answer']}
{result['explanation']}
"""
# Add progress and recommendations
recommendation = self.progress_agent.get_recommendation(progress)
results_content += f"""
### 📊 Your Progress
- **Lessons completed:** {progress.lessons_completed}
- **Average score:** {progress.get_average_score():.1%}
- **Current level:** {progress.current_difficulty.title()}
### 🎯 Recommendation
{recommendation}
"""
return results_content, "🔄 Start New Lesson", ""
except Exception as e:
logger.error(f"Error processing quiz: {e}")
return f"❌ Error processing quiz: {str(e)}", "", ""
# Initialize the main application
app = AgenticSkillBuilder()
def create_interface():
"""Create the Gradio interface"""
with gr.Blocks(
title="SkillSprout",
theme=gr.themes.Soft(),
css="""
.gradio-container {
max-width: 800px !important;
margin: auto !important;
}
"""
) as demo:
# Header
gr.Markdown("""
# 🌱 SkillSprout
### AI-Powered Microlearning Platform
Learn new skills through bite-sized lessons and adaptive quizzes powered by Azure OpenAI!
""")
# State variables
current_skill = gr.State("")
with gr.Tab("🎯 Start Learning"):
gr.Markdown("### Choose a skill to begin your microlearning journey")
with gr.Row():
with gr.Column():
skill_dropdown = gr.Dropdown(
choices=app.predefined_skills,
label="📚 Select a Skill",
info="Choose from popular skills..."
)
custom_skill = gr.Textbox(
label="✍️ Or enter a custom skill",
info="e.g., Cooking, Guitar, Time Management..."
)
start_btn = gr.Button("🚀 Start Learning", variant="primary", size="lg")
# Lesson content area
lesson_output = gr.Markdown(visible=False)
lesson_btn = gr.Button("Complete Lesson", visible=False)
# Quiz area
quiz_output = gr.Markdown(visible=False)
# Dynamic quiz inputs (will be created based on quiz content)
quiz_inputs = []
for i in range(5): # Max 5 questions
quiz_inputs.append(gr.Textbox(label=f"Answer {i+1}", visible=False))
quiz_submit_btn = gr.Button("Submit Quiz", visible=False)
# Results area
results_output = gr.Markdown(visible=False)
restart_btn = gr.Button("Start New Lesson", visible=False)
with gr.Tab("📊 Progress Dashboard"):
gr.Markdown("### Your Learning Analytics")
progress_display = gr.Markdown("""
**Welcome to your progress dashboard!**
Complete some lessons to see your learning analytics here.
""")
refresh_progress_btn = gr.Button("🔄 Refresh Progress")
with gr.Tab("🔗 MCP Endpoints"):
gr.Markdown("""
### Model Context Protocol Integration
This application exposes MCP endpoints for integration with external agents:
- **GET /lesson/{skill}** - Fetch next lesson for a skill
- **GET /progress/{user_id}** - Get user progress data
- **POST /quiz/submit** - Submit quiz results
*Coming soon: Full MCP server implementation*
""")
# Event handlers
async def handle_start_learning(skill_choice, custom_skill_input):
skill = custom_skill_input.strip() if custom_skill_input.strip() else skill_choice
if not skill:
return [
gr.update(value="⚠️ Please select or enter a skill to continue."),
gr.update(visible=False),
gr.update(visible=False),
skill
] + [gr.update(visible=False, value="") for _ in range(5)]
lesson_content, btn_text, _ = await app.start_lesson(skill)
return [
gr.update(value=lesson_content),
gr.update(value=btn_text, visible=True),
gr.update(visible=False),
skill
] + [gr.update(visible=False, value="") for _ in range(5)]
async def handle_complete_lesson():
quiz_content, btn_text, _ = await app.complete_lesson_and_start_quiz()
# Show quiz inputs based on number of questions
quiz_updates = []
if app.current_quiz:
for i, question in enumerate(app.current_quiz.questions):
if i < len(quiz_inputs):
label = f"Q{i+1}: {question['question'][:50]}..."
quiz_updates.append(gr.update(label=label, visible=True))
else:
quiz_updates.append(gr.update(visible=False))
# Hide remaining inputs
for i in range(len(app.current_quiz.questions), len(quiz_inputs)):
quiz_updates.append(gr.update(visible=False))
else:
quiz_updates = [gr.update(visible=False) for _ in range(len(quiz_inputs))]
return [
gr.update(visible=False),
gr.update(value=quiz_content, visible=True),
gr.update(value=btn_text, visible=True),
gr.update(visible=False)
] + quiz_updates
def handle_submit_quiz(*answers):
# Filter out None values and empty strings
valid_answers = [ans for ans in answers if ans is not None and ans != ""]
results_content, btn_text, _ = app.submit_quiz(*valid_answers)
return [
gr.update(visible=False),
gr.update(value=results_content, visible=True),
gr.update(value=btn_text, visible=True),
gr.update(visible=False)
] + [gr.update(visible=False) for _ in range(len(quiz_inputs))]
def handle_restart():
return [
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False),
""
] + [gr.update(visible=False, value="") for _ in range(len(quiz_inputs))]
def update_progress_display():
if not app.progress_agent.user_data:
return "**No learning data yet.** Complete some lessons to see your progress!"
progress_content = "# 📊 Your Learning Progress\n\n"
for key, progress in app.progress_agent.user_data.items():
progress_content += f"""
**Skill:** {progress.skill}
- Lessons completed: {progress.lessons_completed}
- Average quiz score: {progress.get_average_score():.1%}
- Current difficulty: {progress.current_difficulty.title()}
- Last activity: {progress.last_activity or 'Never'}
"""
return progress_content
# Wire up the events
start_btn.click(
handle_start_learning,
inputs=[skill_dropdown, custom_skill],
outputs=[lesson_output, lesson_btn, quiz_output, current_skill] + quiz_inputs[:5]
)
lesson_btn.click(
handle_complete_lesson,
outputs=[lesson_btn, quiz_output, quiz_submit_btn, results_output] + quiz_inputs
)
quiz_submit_btn.click(
handle_submit_quiz,
inputs=quiz_inputs,
outputs=[quiz_submit_btn, results_output, restart_btn, quiz_output] + quiz_inputs
)
restart_btn.click(
handle_restart,
outputs=[lesson_output, quiz_output, results_output, lesson_btn, restart_btn, current_skill] + quiz_inputs
)
refresh_progress_btn.click(
update_progress_display,
outputs=[progress_display]
)
return demo
def main():
"""Main application entry point"""
demo = create_interface()
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
show_error=True
)
if __name__ == "__main__":
main()
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