import gradio as gr import os import json import uuid from datetime import datetime from groq import Groq # Set up Groq API key GROQ_API_KEY = os.environ.get("GROQ_API_KEY") if not GROQ_API_KEY: raise ValueError("GROQ_API_KEY environment variable not set.") client = Groq(api_key=GROQ_API_KEY) # Default system prompt SYSTEM_PROMPT = ( "You are an intelligent, friendly, and highly adaptable Teaching Assistant Chatbot. " "Your mission is to help users of all ages and skill levels—from complete beginners to seasoned professionals—learn Python, Data Science, and Artificial Intelligence. " "You explain concepts clearly using real-world analogies, examples, and interactive exercises. " "You ask questions to assess the learner's level, adapt accordingly, and provide learning paths tailored to their pace and goals. " "Your responses are structured, engaging, and supportive. " "You can explain code snippets, generate exercises and quizzes, and recommend projects. " "You never overwhelm users with jargon. Instead, you scaffold complex concepts in simple, digestible steps." ) # Define learning paths with format options for different learning styles LEARNING_PATHS = { "python_beginner": { "title": "Python Fundamentals", "description": "Learn Python basics from variables to functions", "modules": [ "Variables & Data Types", "Control Flow", "Functions", "Data Structures", "File I/O" ], "resources": { "Visual": ["Interactive Python visualizers", "Flowcharts for algorithms", "Concept maps"], "Reading/Writing": ["Comprehensive Python documentation", "Written tutorials", "Practice exercises with solutions"], "Hands-on Projects": ["Mini-projects for each concept", "Code challenges", "GitHub repositories with starter code"], "Video Tutorials": ["Python beginner video series", "Live coding sessions", "Animated explanations"], "Interactive Exercises": ["Interactive coding environments", "Quizzes after each topic", "Peer programming exercises"], "Combination": ["Mix of videos, reading materials and hands-on practice"] } }, "python_intermediate": { "title": "Intermediate Python", "description": "Advance your Python skills with OOP and more", "modules": [ "Object-Oriented Programming", "Modules & Packages", "Error Handling", "List Comprehensions", "Decorators & Generators" ], "resources": { "Visual": ["OOP visualizers", "Package dependency graphs", "Error handling flowcharts"], "Reading/Writing": ["In-depth Python guides", "Advanced documentation", "Design pattern examples"], "Hands-on Projects": ["Medium-sized applications", "Library contributions", "Optimization challenges"], "Video Tutorials": ["Advanced Python concept videos", "Live implementation sessions", "Code reviews"], "Interactive Exercises": ["Advanced coding challenges", "Refactoring exercises", "Implementation quizzes"], "Combination": ["Mix of videos, reading materials and advanced projects"] } }, "data_science_beginner": { "title": "Data Science Foundations", "description": "Begin your data science journey", "modules": [ "Numpy Basics", "Pandas Fundamentals", "Data Visualization", "Basic Statistics", "Intro to Machine Learning" ], "resources": { "Visual": ["Data visualization galleries", "Statistical concept diagrams", "Algorithm flowcharts"], "Reading/Writing": ["Data science textbooks", "Research papers", "Case studies"], "Hands-on Projects": ["Dataset analysis projects", "Visualization portfolios", "Simple ML implementations"], "Video Tutorials": ["Data science fundamentals series", "Tool-specific tutorials", "Statistical concept videos"], "Interactive Exercises": ["Data analysis notebooks", "Interactive statistics demos", "ML model building exercises"], "Combination": ["Mix of data analysis, visualization practice and statistical theory"] } }, "data_science_advanced": { "title": "Advanced Data Science", "description": "Master complex data science concepts", "modules": [ "Advanced ML Algorithms", "Feature Engineering", "Time Series Analysis", "Natural Language Processing", "Deep Learning Basics" ], "resources": { "Visual": ["Complex algorithm visualizations", "Neural network architecture diagrams", "Feature importance plots"], "Reading/Writing": ["Academic papers", "Advanced statistics guides", "Mathematical foundations"], "Hands-on Projects": ["Kaggle competitions", "Research implementations", "Production-level ML systems"], "Video Tutorials": ["Expert talks", "Research paper walkthroughs", "Implementation guides"], "Interactive Exercises": ["Advanced model tuning", "Algorithm implementation", "Real-world data challenges"], "Combination": ["Mix of theoretical study, research implementation and real-world applications"] } }, "ai_specialization": { "title": "AI Specialization", "description": "Focus on artificial intelligence concepts", "modules": [ "Neural Networks", "Computer Vision", "Advanced NLP", "Reinforcement Learning", "AI Ethics" ], "resources": { "Visual": ["Neural network visualizers", "Computer vision demonstrations", "AI system diagrams"], "Reading/Writing": ["Research papers", "AI textbooks", "Ethics case studies"], "Hands-on Projects": ["Model implementations", "AI application development", "Research reproductions"], "Video Tutorials": ["Research paper explanations", "Implementation walkthroughs", "Expert interviews"], "Interactive Exercises": ["Model training exercises", "Hyperparameter tuning", "AI debugging workshops"], "Combination": ["Mix of theoretical study, practical implementation and ethical discussions"] } } } # Learning resources expanded for different learning styles LEARNING_RESOURCES = { "python": { "Visual": [ {"title": "Python Tutor - Code Visualization", "url": "http://pythontutor.com/"}, {"title": "Real Python - Visual Guides", "url": "https://realpython.com/"}, {"title": "Visualize Python - Concept Maps", "url": "https://pythonvisualization.example.org/"} ], "Reading/Writing": [ {"title": "Python Documentation", "url": "https://docs.python.org/3/"}, {"title": "Real Python", "url": "https://realpython.com/"}, {"title": "Python for Everybody Book", "url": "https://www.py4e.com/"}, {"title": "Automate the Boring Stuff with Python", "url": "https://automatetheboringstuff.com/"} ], "Hands-on Projects": [ {"title": "Exercism Python Track", "url": "https://exercism.org/tracks/python"}, {"title": "Project Euler", "url": "https://projecteuler.net/"}, {"title": "Python Projects on GitHub", "url": "https://github.com/topics/python-projects"}, ], "Video Tutorials": [ {"title": "YouTube - Python Programming Tutorials", "url": "https://www.youtube.com/results?search_query=python+programming+tutorials"}, {"title": "Coursera - Python Courses", "url": "https://www.coursera.org/courses?query=python"}, {"title": "edX - Learn Python", "url": "https://www.edx.org/learn/python"}, {"title": "Udemy - Python Video Courses", "url": "https://www.udemy.com/topic/python/"}, {"title": "Khan Academy - Computer Programming", "url": "https://www.khanacademy.org/computing/computer-programming"} ], "Interactive Exercises": [ {"title": "CheckiO Python Challenges", "url": "https://py.checkio.org/"}, {"title": "HackerRank Python", "url": "https://www.hackerrank.com/domains/python"}, {"title": "Codewars Python", "url": "https://www.codewars.com/?language=python"}, {"title": "LeetCode Python", "url": "https://leetcode.com/problemset/all/?difficulty=Easy&topicSlugs=python"} ], "Video Tutorials": [ {"title": "YouTube - Data Science Tutorials", "url": "https://www.youtube.com/results?search_query=data+science+tutorials"}, {"title": "Coursera - Data Science Courses", "url": "https://www.coursera.org/browse/data-science"}, {"title": "edX - Learn Data Science", "url": "https://www.edx.org/learn/data-science"}, {"title": "Udacity - Data Science Programs", "url": "https://www.udacity.com/school-of-data-science"}, {"title": "Udemy - Data Science Video Courses", "url": "https://www.udemy.com/topic/data-science/"} ] }, "data_science": { "Visual": [ {"title": "Seeing Theory - Visual Statistics", "url": "https://seeing-theory.brown.edu/"}, {"title": "Data Visualization Catalogue", "url": "https://datavizcatalogue.com/"}, {"title": "Visualizing Machine Learning", "url": "https://www.r2d3.us/visual-intro-to-machine-learning-part-1/"} ], "Reading/Writing": [ {"title": "Towards Data Science", "url": "https://towardsdatascience.com/"}, {"title": "Machine Learning Mastery", "url": "https://machinelearningmastery.com/"}, {"title": "Data Science Handbook", "url": "https://jakevdp.github.io/PythonDataScienceHandbook/"}, {"title": "Statistical Learning Book", "url": "https://www.statlearning.com/"} ], "Hands-on Projects": [ {"title": "Kaggle Competitions", "url": "https://www.kaggle.com/competitions"}, {"title": "Data Science Projects on GitHub", "url": "https://github.com/topics/data-science-projects"}, {"title": "Real-world Data Science Tasks", "url": "https://www.drivendata.org/"}, {"title": "UCI Machine Learning Repository", "url": "https://archive.ics.uci.edu/ml/index.php"} ], "Video Tutorials": [ {"title": "StatQuest with Josh Starmer", "url": "https://www.youtube.com/c/joshstarmer"}, {"title": "Data School", "url": "https://www.youtube.com/c/dataschool"}, {"title": "3Blue1Brown Statistics", "url": "https://www.youtube.com/c/3blue1brown"}, {"title": "Krish Naik Data Science", "url": "https://www.youtube.com/user/krishnaik06"} ], "Interactive Exercises": [ {"title": "Kaggle Learn", "url": "https://www.kaggle.com/learn"}, {"title": "DataQuest Interactive Lessons", "url": "https://www.dataquest.io/"}, {"title": "Mode Analytics SQL Tutorial", "url": "https://mode.com/sql-tutorial/"}, {"title": "IBM Data Science Exercise", "url": "https://cognitiveclass.ai/"} ], "Combination": [ {"title": "DataCamp", "url": "https://www.datacamp.com/"}, {"title": "Coursera Data Science Specialization", "url": "https://www.coursera.org/specializations/jhu-data-science"}, {"title": "edX Data Science MicroMasters", "url": "https://www.edx.org/micromasters/mitx-statistics-and-data-science"}, {"title": "Fast.ai Practical Data Science", "url": "https://www.fast.ai/"} ] }, "ai": { "Visual": [ {"title": "TensorFlow Playground", "url": "https://playground.tensorflow.org/"}, {"title": "Distill.pub Visualizations", "url": "https://distill.pub/"}, {"title": "AI Visuals & Explainers", "url": "https://pair.withgoogle.com/explorables/"} ], "Reading/Writing": [ {"title": "arXiv AI Papers", "url": "https://arxiv.org/list/cs.AI/recent"}, {"title": "AI Textbook", "url": "http://aima.cs.berkeley.edu/"}, {"title": "Deep Learning Book", "url": "https://www.deeplearningbook.org/"}, {"title": "Papers with Code", "url": "https://paperswithcode.com/"} ], "Hands-on Projects": [ {"title": "AI Projects on GitHub", "url": "https://github.com/topics/artificial-intelligence"}, {"title": "Hugging Face Model Hub", "url": "https://huggingface.co/models"}, {"title": "TensorFlow Model Garden", "url": "https://github.com/tensorflow/models"}, {"title": "PyTorch Examples", "url": "https://github.com/pytorch/examples"} ], "Video Tutorials": [ {"title": "DeepMind YouTube", "url": "https://www.youtube.com/c/DeepMind"}, {"title": "Yannic Kilcher AI Papers", "url": "https://www.youtube.com/c/YannicKilcher"}, {"title": "MIT Deep Learning Lectures", "url": "https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI"}, {"title": "Stanford CS231n Videos", "url": "https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv"} ], "Interactive Exercises": [ {"title": "Google AI Experiments", "url": "https://experiments.withgoogle.com/collection/ai"}, {"title": "OpenAI Gym", "url": "https://gym.openai.com/"}, {"title": "ML Playground", "url": "https://ml-playground.com/"}, {"title": "Colab Research AI Projects", "url": "https://colab.research.google.com/"} ], "Combination": [ {"title": "Fast.ai", "url": "https://www.fast.ai/"}, {"title": "DeepLearning.AI", "url": "https://www.deeplearning.ai/"}, {"title": "Coursera Machine Learning", "url": "https://www.coursera.org/learn/machine-learning"}, {"title": "edX AI Courses", "url": "https://www.edx.org/learn/artificial-intelligence"} ] } } # Practice project ideas PROJECT_IDEAS = { "python_beginner": [ "To-Do List Application", "Simple Calculator", "Password Generator", "Hangman Game", "Basic File Organizer" ], "python_intermediate": [ "Weather App with API", "Personal Blog with Flask", "Web Scraper for News Articles", "Data Visualization Dashboard", "Task Automation Scripts" ], "data_science": [ "Housing Price Prediction", "Customer Segmentation Analysis", "Sentiment Analysis of Reviews", "Stock Price Forecasting", "A/B Test Analysis Dashboard" ], "ai": [ "Image Classification System", "Chatbot with NLP", "Recommendation Engine", "Text Summarization Tool", "Object Detection Application" ] } # User session data store SESSION_DATA = {} def save_session(session_id, data): """Save session data to SESSION_DATA global dictionary""" if session_id in SESSION_DATA: SESSION_DATA[session_id].update(data) else: SESSION_DATA[session_id] = data # Add timestamp for session tracking SESSION_DATA[session_id]["last_activity"] = datetime.now().isoformat() def load_session(session_id): """Load session data from SESSION_DATA global dictionary""" return SESSION_DATA.get(session_id, {}) def recommend_learning_path(age, goals, knowledge_level, interests): """Recommend personalized learning paths based on user profile""" paths = [] # Simple recommendation logic based on profile if "beginner" in knowledge_level.lower(): if any(topic in interests.lower() for topic in ["python", "programming", "coding"]): paths.append("python_beginner") if any(topic in interests.lower() for topic in ["data", "analysis", "statistics"]): paths.append("data_science_beginner") elif "intermediate" in knowledge_level.lower() or "advanced" in knowledge_level.lower(): if any(topic in interests.lower() for topic in ["python", "programming", "coding"]): paths.append("python_intermediate") if any(topic in interests.lower() for topic in ["data", "analysis", "statistics"]): paths.append("data_science_advanced") if any(topic in interests.lower() for topic in ["ai", "machine learning", "deep learning"]): paths.append("ai_specialization") # Default path if no matches if not paths: paths = ["python_beginner"] return [LEARNING_PATHS[path] for path in paths if path in LEARNING_PATHS] def get_recommended_resources(interests): """Get recommended learning resources based on interests""" resources = [] if any(topic in interests.lower() for topic in ["python", "programming", "coding"]): resources.extend(LEARNING_RESOURCES["python"]) if any(topic in interests.lower() for topic in ["data", "analysis", "statistics"]): resources.extend(LEARNING_RESOURCES["data_science"]) if any(topic in interests.lower() for topic in ["ai", "machine learning", "deep learning"]): resources.extend(LEARNING_RESOURCES["ai"]) # If no specific interests match, provide general resources if not resources: for category in LEARNING_RESOURCES: resources.extend(LEARNING_RESOURCES[category][:1]) # Add first resource from each category return resources def get_project_ideas(learning_paths): """Get project ideas based on recommended learning paths""" ideas = [] for path in learning_paths: path_id = next((k for k, v in LEARNING_PATHS.items() if v["title"] == path["title"]), None) if path_id: if path_id.startswith("python"): category = "python_beginner" if "beginner" in path_id else "python_intermediate" ideas.extend(PROJECT_IDEAS[category]) elif path_id.startswith("data_science"): ideas.extend(PROJECT_IDEAS["data_science"]) elif path_id.startswith("ai"): ideas.extend(PROJECT_IDEAS["ai"]) # If no specific paths match, provide some general project ideas if not ideas: ideas = PROJECT_IDEAS["python_beginner"][:2] + PROJECT_IDEAS["data_science"][:2] return ideas[:5] # Return up to 5 project ideas def generate_quiz(topic, difficulty): """Generate a quiz based on the topic and difficulty""" # In a real application, you might use the LLM to generate quizzes # Here we're using a template approach for simplicity quiz_prompt = f""" Generate a {difficulty} level quiz on {topic} with 3 multiple-choice questions. For each question, provide 4 options and indicate the correct answer. Format the quiz nicely with clear question numbering and option lettering. """ # Use Groq to generate the quiz quiz_messages = [ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": quiz_prompt} ] quiz_response = client.chat.completions.create( messages=quiz_messages, model="llama-3.3-70b-versatile", stream=False ) return quiz_response.choices[0].message.content def create_study_plan(topic, time_available, goals): """Create a personalized study plan""" plan_prompt = f""" Create a structured study plan for learning {topic} with {time_available} hours per week available for study. The learner's goal is: {goals} Include: 1. Weekly breakdown of topics 2. Time allocation for theory vs practice 3. Recommended resources for each week 4. Milestone projects or assessments 5. Tips for effective learning """ # Use Groq to generate the study plan plan_messages = [ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": plan_prompt} ] plan_response = client.chat.completions.create( messages=plan_messages, model="llama-3.3-70b-versatile", stream=False ) return plan_response.choices[0].message.content def chat_with_groq(user_input, session_id): """Chat with Groq LLM using session context""" user_data = load_session(session_id) # Build context from session data if available context = "" if user_data: context = f""" User Profile: - Age: {user_data.get('age', 'Unknown')} - Knowledge Level: {user_data.get('knowledge_level', 'Unknown')} - Learning Goals: {user_data.get('goals', 'Unknown')} - Interests: {user_data.get('interests', 'Unknown')} - Available Study Time: {user_data.get('study_time', 'Unknown')} hours per week - Preferred Learning Style: {user_data.get('learning_style', 'Unknown')} Based on this profile, tailor your response appropriately. """ # Add chat history context if available chat_history = user_data.get('chat_history', []) if chat_history: context += "\n\nRecent conversation context (most recent first):\n" # Include up to 3 most recent exchanges for i, (q, a) in enumerate(reversed(chat_history[-3:])): context += f"User: {q}\nYou: {a}\n\n" # Combine everything for the LLM full_prompt = f"{context}\n\nUser's current question: {user_input}" messages = [ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": full_prompt} ] chat_completion = client.chat.completions.create( messages=messages, model="llama-3.3-70b-versatile", stream=False ) response = chat_completion.choices[0].message.content # Update chat history if 'chat_history' not in user_data: user_data['chat_history'] = [] user_data['chat_history'].append((user_input, response)) save_session(session_id, user_data) return response def format_learning_paths(paths): """Format learning paths for display""" if not paths: return "No specific learning paths recommended yet. Please complete your profile." result = "### Recommended Learning Paths\n\n" for i, path in enumerate(paths, 1): result += f"**{i}. {path['title']}**\n" result += f"{path['description']}\n\n" result += "**Modules:**\n" for module in path['modules']: result += f"- {module}\n" result += "\n" return result def format_resources(resources): """Format resources for display""" if not resources: return "No resources recommended yet. Please complete your profile." result = "### Recommended Learning Resources\n\n" for i, resource in enumerate(resources, 1): result += f"{i}. [{resource['title']}]({resource['url']})\n" return result def format_project_ideas(ideas): """Format project ideas for display""" if not ideas: return "No project ideas recommended yet. Please complete your profile." result = "### Recommended Practice Projects\n\n" for i, idea in enumerate(ideas, 1): result += f"{i}. {idea}\n" return result def user_onboarding(session_id, age, goals, knowledge_level, interests, study_time, learning_style): """Process user profile and provide initial recommendations""" # Save user profile data user_data = { 'age': age, 'goals': goals, 'knowledge_level': knowledge_level, 'interests': interests, 'study_time': study_time, 'learning_style': learning_style } save_session(session_id, user_data) # Generate recommendations learning_paths = recommend_learning_path(age, goals, knowledge_level, interests) resources = get_recommended_resources(interests) project_ideas = get_project_ideas(learning_paths) # Save recommendations to session user_data.update({ 'recommended_paths': learning_paths, 'recommended_resources': resources, 'recommended_projects': project_ideas }) save_session(session_id, user_data) # Format welcome message with personalized recommendations welcome_message = f""" # Welcome to Your Personalized Learning Journey! Thank you for providing your profile. Based on your information, I've prepared some tailored recommendations to start your learning journey. ## Your Profile Summary: - **Age:** {age} - **Knowledge Level:** {knowledge_level} - **Learning Goals:** {goals} - **Interests:** {interests} - **Available Study Time:** {study_time} hours per week - **Preferred Learning Style:** {learning_style} {format_learning_paths(learning_paths)} {format_resources(resources)} {format_project_ideas(project_ideas)} ## Next Steps: 1. Browse through the recommended learning paths and resources 2. Ask me any questions about the topics you're interested in 3. Request exercises, explanations, or code samples 4. Try one of the project ideas to apply your knowledge I'm here to help you every step of the way! What would you like to explore first? """ return welcome_message def chatbot_interface(session_id, user_message): """Main chatbot interface function""" user_data = load_session(session_id) if not user_data or not user_data.get('age'): return "Please complete your profile first by going to the Profile tab." response = chat_with_groq(user_message, session_id) return response def generate_recommendations(session_id): """Generate or refresh recommendations based on current profile""" user_data = load_session(session_id) if not user_data or not user_data.get('age'): return "Please complete your profile first by going to the Profile tab." # Generate fresh recommendations learning_paths = recommend_learning_path( user_data.get('age', ''), user_data.get('goals', ''), user_data.get('knowledge_level', ''), user_data.get('interests', '') ) resources = get_recommended_resources(user_data.get('interests', '')) project_ideas = get_project_ideas(learning_paths) # Save recommendations to session user_data.update({ 'recommended_paths': learning_paths, 'recommended_resources': resources, 'recommended_projects': project_ideas }) save_session(session_id, user_data) # Format recommendations recommendations = f""" # Your Personalized Learning Recommendations {format_learning_paths(learning_paths)} {format_resources(resources)} {format_project_ideas(project_ideas)} """ return recommendations def handle_quiz_request(session_id, topic, difficulty): """Handle quiz generation request""" user_data = load_session(session_id) if not user_data or not user_data.get('age'): return "Please complete your profile first by going to the Profile tab." quiz = generate_quiz(topic, difficulty) return quiz def handle_study_plan_request(session_id, topic, time_available): """Handle study plan generation request""" user_data = load_session(session_id) if not user_data or not user_data.get('age'): return "Please complete your profile first by going to the Profile tab." goals = user_data.get('goals', 'improving skills') study_plan = create_study_plan(topic, time_available, goals) return study_plan def create_chatbot(): """Create the Gradio interface for the chatbot""" # Generate a random session ID for the user session_id = str(uuid.uuid4()) # Define theme colors and styling primary_color = "#4a6fa5" secondary_color = "#6c757d" success_color = "#28a745" light_color = "#f8f9fa" dark_color = "#343a40" custom_css = f""" :root {{ --primary-color: {primary_color}; --secondary-color: {secondary_color}; --success-color: {success_color}; --light-color: {light_color}; --dark-color: {dark_color}; }} .gradio-container {{ background-color: var(--light-color); font-family: 'Inter', 'Segoe UI', sans-serif; }} #title {{ font-size: 32px; font-weight: bold; text-align: center; padding-top: 20px; color: var(--primary-color); margin-bottom: 0; }} #subtitle {{ font-size: 18px; text-align: center; margin-bottom: 20px; color: var(--secondary-color); }} .card {{ background-color: white; padding: 20px; border-radius: 12px; box-shadow: 0 4px 10px rgba(0,0,0,0.08); margin-bottom: 20px; }} .tabs {{ margin-top: 20px; }} .gr-button-primary {{ background-color: var(--primary-color) !important; }} .gr-button-secondary {{ background-color: var(--secondary-color) !important; }} .gr-button-success {{ background-color: var(--success-color) !important; }} .footer {{ text-align: center; margin-top: 30px; padding: 10px; font-size: 14px; color: var(--secondary-color); }} .progress-module {{ padding: 10px; margin: 5px 0; border-radius: 5px; background-color: #e9ecef; }} .progress-module.completed {{ background-color: #d4edda; }} """ with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="blue")) as demo: gr.HTML("
🎓 AI Teaching Assistant
") gr.HTML("
Your personalized learning companion for Python, Data Science & AI
") # Tabs for different sections with gr.Tabs(elem_classes=["tabs"]) as tabs: # Profile Tab with gr.Tab("Profile & Goals"): with gr.Column(elem_classes=["card"]): gr.HTML("

Complete Your Learning Profile

") with gr.Row(): with gr.Column(scale=1): age_input = gr.Textbox( label="Age", placeholder="e.g. 20", lines=1 ) with gr.Column(scale=2): knowledge_level_input = gr.Dropdown( choices=["Beginner", "Intermediate", "Advanced", "Expert"], label="Knowledge Level", value="Beginner" ) goals_input = gr.Textbox( label="Learning Goals", placeholder="e.g. I want to become a data scientist and work with machine learning models", lines=2 ) interests_input = gr.Textbox( label="Specific Interests", placeholder="e.g. Python, data visualization, neural networks", lines=2 ) with gr.Row(): with gr.Column(scale=1): study_time_input = gr.Dropdown( choices=["1-3", "4-6", "7-10", "10+"], label="Hours Available Weekly", value="4-6" ) with gr.Column(scale=2): learning_style_input = gr.Dropdown( choices=["Visual", "Reading/Writing", "Hands-on Projects", "Video Tutorials", "Interactive Exercises", "Combination"], label="Preferred Learning Style", value="Combination" ) profile_submit_btn = gr.Button("Save Profile & Generate Recommendations", variant="primary") profile_output = gr.Markdown(label="Personalized Recommendations") # Chat Tab with gr.Tab("Learning Assistant"): with gr.Row(): with gr.Column(elem_classes=["card"]): chat_input = gr.Textbox( label="Ask a Question", placeholder="Ask anything about Python, Data Science, AI...", lines=3 ) with gr.Row(): chat_submit_btn = gr.Button("Send Message", variant="primary") chat_clear_btn = gr.Button("Clear Chat", variant="secondary") chat_output = gr.Markdown(label="Assistant Response") # Resources Tab with gr.Tab("Resources & Recommendations"): with gr.Column(elem_classes=["card"]): gr.HTML("

Your Learning Resources

") refresh_recommendations_btn = gr.Button("Refresh Recommendations", variant="primary") recommendations_output = gr.Markdown(label="Personalized Recommendations") # Practice Tab with gr.Tab("Practice & Assessment"): with gr.Column(elem_classes=["card"]): gr.HTML("

Generate a Quiz

") with gr.Row(): quiz_topic_input = gr.Textbox( label="Quiz Topic", placeholder="e.g. Python Lists", lines=1 ) quiz_difficulty_input = gr.Dropdown( choices=["Beginner", "Intermediate", "Advanced"], label="Difficulty Level", value="Beginner" ) generate_quiz_btn = gr.Button("Generate Quiz", variant="primary") quiz_output = gr.Markdown(label="Quiz") # Study Plan Tab with gr.Tab("Study Plan"): with gr.Column(elem_classes=["card"]): gr.HTML("

Generate a Personalized Study Plan

") with gr.Row(): plan_topic_input = gr.Textbox( label="Study Topic", placeholder="e.g. Data Science", lines=1 ) plan_time_input = gr.Dropdown( choices=["1-3", "4-6", "7-10", "10+"], label="Hours Available Weekly", value="4-6" ) generate_plan_btn = gr.Button("Generate Study Plan", variant="primary") plan_output = gr.Markdown(label="Personalized Study Plan") gr.HTML("""
AI Teaching Assistant Pro | Version 2.0 | © 2025 | Powered by Groq AI
""") # Event handlers profile_submit_btn.click( user_onboarding, inputs=[ gr.State(session_id), age_input, goals_input, knowledge_level_input, interests_input, study_time_input, learning_style_input ], outputs=profile_output ) chat_submit_btn.click( chatbot_interface, inputs=[gr.State(session_id), chat_input], outputs=chat_output ) chat_clear_btn.click( lambda: "", inputs=[], outputs=[chat_output, chat_input] ) refresh_recommendations_btn.click( generate_recommendations, inputs=[gr.State(session_id)], outputs=recommendations_output ) generate_quiz_btn.click( handle_quiz_request, inputs=[gr.State(session_id), quiz_topic_input, quiz_difficulty_input], outputs=quiz_output ) generate_plan_btn.click( handle_study_plan_request, inputs=[gr.State(session_id), plan_topic_input, plan_time_input], outputs=plan_output ) return demo # Run the chatbot if __name__ == "__main__": app = create_chatbot() app.launch()