import os import gradio as gr from anthropic import Anthropic from datetime import datetime, timedelta from collections import deque import random import logging import tempfile from pathlib import Path from sympy import * # Set up logging logging.basicConfig( level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) # Initialize Anthropic client anthropic = Anthropic( api_key=os.environ.get('ANTHROPIC_API_KEY') ) # Request tracking MAX_REQUESTS_PER_DAY = 25 request_history = deque(maxlen=1000) import json from pathlib import Path def load_proof_repository(): """Load the proof repository from the repository file""" repo_path = Path("Lebl-theorems-all.json") try: with open(repo_path, "r") as f: return json.load(f) except Exception as e: logger.error(f"Error loading proof repository: {str(e)}") return None def get_relevant_proofs(topic): """Get relevant proofs from repository based on topic, randomly selecting examples""" repository = load_proof_repository() if not repository: return [] # Extract relevant proofs based on topic relevant_proofs = [] for theorem in repository.get("dataset", {}).get("theorems", []): # Add proofs that match the topic if any(topic.lower() in content.lower() for content in theorem.get("contents", [])): # Only include if it has both contents and proofs if theorem.get("contents") and theorem.get("proofs"): proof_content = { "title": theorem.get("title", ""), "contents": theorem.get("contents", []), "proofs": [p.get("contents", []) for p in theorem.get("proofs", [])] } relevant_proofs.append(proof_content) # Randomly select 3 proofs if we have more than 3 if len(relevant_proofs) > 3: return random.sample(relevant_proofs, 3) return relevant_proofs def enhance_prompt_with_proofs(system_prompt, subject, topic): """Enhance the system prompt with relevant proofs if subject is Real Analysis""" if subject != "Real Analysis": return system_prompt relevant_proofs = get_relevant_proofs(topic) if not relevant_proofs: return system_prompt # Add proof examples to the prompt proof_examples = "\n\nReference these proof examples for style and approach:\n" for proof in relevant_proofs[:3]: # Limit to 3 examples to manage token usage proof_examples += f"\nTheorem: {proof['title']}\n" proof_examples += "Statement: " + " ".join(proof['contents']) + "\n" if proof['proofs']: proof_examples += "Proof: " + " ".join(proof['proofs'][0]) + "\n" # Add specific instructions for using the examples enhanced_prompt = f""" {system_prompt} ADDITIONAL PROOF GUIDELINES: 1. Consider the following proof examples from established textbooks 2. Maintain similar level of rigor and detail 3. Use similar proof techniques where applicable 4. Follow similar notation and presentation style {proof_examples} """ return enhanced_prompt def get_difficulty_parameters(difficulty_level): """Return specific parameters and constraints based on difficulty level""" parameters = { 1: { # Very Easy "description": "very easy, suitable for beginners", "constraints": [ "Use only basic concepts and straightforward calculations", "Break complex problems into smaller, guided steps", "Provide hints within the question when needed", "Use simple numbers and avoid complex algebraic expressions" ], "example_style": "Similar to standard homework problems", "model": "claude-3-5-sonnet-20241022" }, 2: { # Easy "description": "easy, but requiring some thought", "constraints": [ "Use basic concepts with minor complications", "Include two-step problems", "Minimal guidance provided", "Use moderately complex numbers or expressions" ], "example_style": "Similar to quiz questions", "model": "claude-3-5-sonnet-20241022" }, 3: { # Intermediate "description": "intermediate difficulty, testing deeper understanding", "constraints": [ "Combine 2-3 related concepts", "Include some non-obvious solution paths", "Require multi-step reasoning", "Use moderate algebraic complexity" ], "example_style": "Similar to midterm exam questions", "model": "claude-3-5-sonnet-20241022" }, 4: { # Difficult "description": "challenging, requiring strong mathematical maturity", "constraints": [ "Combine multiple concepts creatively", "Require insight and deep understanding", "Include non-standard approaches", "Use sophisticated mathematical reasoning" ], "example_style": "Similar to final exam questions", "model": "claude-3-5-sonnet-20241022" }, 5: { # Very Difficult "description": "very challenging, testing mastery and creativity at a graduate level", "constraints": [ "Create novel applications of theoretical concepts", "Require graduate-level mathematical reasoning", "Combine multiple advanced topics in unexpected ways", "Demand creative problem-solving approaches", "Include rigorous proof construction", "Require synthesis across mathematical domains", "Test deep theoretical understanding" ], "example_style": "Similar to graduate qualifying exams or advanced competition problems", "model": "claude-3-5-sonnet-20241022" } } return parameters.get(difficulty_level) def create_latex_document(content, questions_only=False): """Create a complete LaTeX document""" try: latex_header = r"""\documentclass{article} \usepackage{amsmath,amssymb} \usepackage[margin=1in]{geometry} \begin{document} \title{Mathematics Question} \maketitle """ latex_footer = r"\end{document}" if questions_only: # Modified to handle single question processed_content = content.split('Solution:')[0] content = processed_content full_document = f"{latex_header}\n{content}\n{latex_footer}" logger.debug(f"Created {'questions-only' if questions_only else 'full'} LaTeX document") return full_document except Exception as e: logger.error(f"Error creating LaTeX document: {str(e)}") raise def save_to_temp_file(content, filename): """Save content to a temporary file and return the path""" try: temp_dir = Path(tempfile.gettempdir()) / "math_test_files" temp_dir.mkdir(exist_ok=True) file_path = temp_dir / filename file_path.write_text(content, encoding='utf-8') logger.debug(f"Saved content to temporary file: {file_path}") return str(file_path) except Exception as e: logger.error(f"Error saving temporary file: {str(e)}") raise def get_problem_type_addition(question_type): """Return specific requirements based on problem type""" problem_type_additions = { "application": """ The application question MUST: - Present a real-world scenario or practical problem - Require modeling the situation mathematically - Connect abstract mathematical concepts to concrete situations - Include realistic context and data - Require students to: 1. Identify relevant mathematical concepts 2. Translate the practical problem into mathematical terms 3. Solve using appropriate mathematical techniques 4. Interpret the results in the context of the original problem Example contexts might include: - Physics applications (motion, forces, work) - Engineering scenarios (optimization, rates of change) - Economics problems (cost optimization, growth models) - Biological systems (population growth, reaction rates) - Business applications (profit maximization, inventory management) - Social science applications (demographic models, social network analysis) - Data science applications (regression, statistical analysis) """, "proof": """ The proof question MUST: - Require a formal mathematical proof - Focus on demonstrating logical reasoning - Require justification for each step - Emphasize theoretical understanding The proof question MAY NOT: - Include Real-world applications or scenarios - Include Pure computation problems - Ask only for numerical answers """, "computation": """ The computation question MUST: - Require specific algebraic calculations - Focus on mathematical techniques - Have concrete answers in the form of algebraic expressions (about half of questions) or numbers (about half of questions) - Test procedural knowledge The computation question MAY NOT: - Include extended real-world applications or scenarios - Ask for a proof """ } return problem_type_additions.get(question_type, "") def generate_question(subject, difficulty, question_type): """Generate a single math question""" try: if not os.environ.get('ANTHROPIC_API_KEY'): logger.error("Anthropic API key not found") return "Error: Anthropic API key not configured", None, None logger.debug(f"Generating {question_type} question for subject: {subject} at difficulty level: {difficulty}") # Check rate limit now = datetime.now() while request_history and (now - request_history[0]) > timedelta(days=1): request_history.popleft() if len(request_history) >= MAX_REQUESTS_PER_DAY: return "Daily request limit reached. Please try again tomorrow.", None, None request_history.append(now) topics = { "Single Variable Calculus": ["limits", "derivatives", "integrals", "series", "applications"], "Multivariable Calculus": ["partial derivatives", "multiple integrals", "vector fields", "optimization"], "Linear Algebra": ["matrices", "vector spaces", "eigenvalues", "linear transformations"], "Differential Equations": ["first order equations", "second order equations", "systems", "stability analysis"], "Real Analysis": ["sequences", "series", "continuity", "differentiation", "integration"], "Complex Analysis": ["complex functions", "analyticity", "contour integration", "residues"], "Abstract Algebra": ["groups", "rings", "fields", "homomorphisms"], "Probability Theory": ["probability spaces", "random variables", "distributions", "limit theorems"], "Numerical Analysis": ["approximation", "interpolation", "numerical integration", "error analysis"], "Topology": ["metric spaces", "continuity", "compactness", "connectedness"] } selected_topic = random.choice(topics.get(subject, ["general"])) logger.debug(f"Selected topic: {selected_topic}") difficulty_params = get_difficulty_parameters(difficulty) problem_type_addition = get_problem_type_addition(question_type) if difficulty == 5: system_prompt = f"""You are an expert mathematics professor creating a graduate-level exam question. STRICT REQUIREMENTS: 1. Write exactly 1 graduate-level {question_type} question on {subject} covering {selected_topic}. 2. Advanced Difficulty Requirements: This question must be suitable for PhD qualifying exams or advanced competitions. MUST include: - Novel applications of theoretical concepts - Graduate-level mathematical reasoning - Unexpected connections between different areas of {subject} - Creative problem-solving approaches - Rigorous proof requirements where applicable Follow these specific constraints: {chr(10).join(f' - {c}' for c in difficulty_params['constraints'])} {problem_type_addition} 3. Style Reference: Question should be {difficulty_params['example_style']} 4. The question MUST: - Bridge multiple mathematical domains - Require deep theoretical understanding - Test mastery of advanced concepts - Demand innovative solution approaches 5. For LaTeX formatting: - Use $ for inline math - Use $$ on separate lines for equations and solutions - Put each solution step on its own line in $$ $$ - DO NOT use \\begin{{aligned}} or similar environments 6. Include a detailed solution with thorough explanations of advanced concepts used 7. Maintain clear, precise formatting 8. At the end of the LaTeX solution output, print SymPy code that you would use to solve or verify the main equations in the question. 9. When writing SymPy code to verify solutions: - Use ONLY SymPy functions - do not use NumPy, SciPy, or other libraries - For numerical integration, use SymPy's integrate() instead of scipy.integrate.quad() - For any numerical computations, use SymPy's native functions - Always define symbolic variables using Symbol() before using them - Include print statements for ALL calculations and results - Print intermediate steps and final answers - Print variable values after they are computed - Format numerical results using evalf() for decimal""" else: system_prompt = f"""You are an expert mathematics professor creating a {difficulty_params['description']} exam question. STRICT REQUIREMENTS: 1. Write exactly 1 {question_type} question on {subject} covering {selected_topic}. 2. Difficulty Level Guidelines: {difficulty_params['description'].upper()} Follow these specific constraints: {chr(10).join(f' - {c}' for c in difficulty_params['constraints'])} {problem_type_addition} 3. Style Reference: Question should be {difficulty_params['example_style']} 4. For LaTeX formatting: - Use $ for inline math - Use $$ on separate lines for equations and solutions - Put each solution step on its own line in $$ $$ - DO NOT use \\begin{{aligned}} or similar environments 5. Include a detailed solution 6. Maintain clear formatting 7. At the end of the LaTeX solution output, print SymPy code that you would use to solve or verify the main equations in the question. 8. When writing SymPy code to verify solutions: - Use ONLY SymPy functions - do not use NumPy, SciPy, or other libraries - For numerical integration, use SymPy's integrate() instead of scipy.integrate.quad() - For any numerical computations, use SymPy's native functions - Always define symbolic variables using Symbol() before using them - Include print statements for ALL calculations and results - Print intermediate steps and final answers - Print variable values after they are computed - Format numerical results using evalf() for decimal""" #Consider #When writing SymPy code: #- Use FiniteSet(1, 2, 3) instead of Set([1, 2, 3]) for finite sets #- Import specific functions instead of using 'from sympy import *' #- Print results of each calculation step # Enhance the prompt with proof examples if applicable if subject == "Real Analysis" and question_type == "proof": system_prompt = enhance_prompt_with_proofs(system_prompt, subject, selected_topic) logger.debug("Sending request to Anthropic API") message = anthropic.messages.create( model=difficulty_params['model'], max_tokens=4096, temperature=0.7, messages=[{ "role": "user", "content": f"{system_prompt}\n\nWrite a question for {subject}." }] ) if not hasattr(message, 'content') or not message.content: logger.error("No content received from Anthropic API") return "Error: No content received from API", None, None response_text = message.content[0].text logger.debug("Successfully received response from Anthropic API") # Execute SymPy code and append results sympy_output = extract_and_run_sympy_code_simple(response_text) if sympy_output: # Check if SymPy ran successfully if "Error" not in sympy_output: resolution = check_and_resolve_discrepancy(response_text, sympy_output) response_text = f"{response_text}\n\nSymPy Verification Results:\n```\n{sympy_output}\n```\n\nVerification Analysis:\n{resolution}" else: # Just append SymPy results if there was an error response_text += f"\n\nSymPy Verification Results:\n```\n{sympy_output}\n```" # Create LaTeX content questions_latex = create_latex_document(response_text, questions_only=True) full_latex = create_latex_document(response_text, questions_only=False) # Save to temporary files questions_path = save_to_temp_file(questions_latex, "question.tex") full_path = save_to_temp_file(full_latex, "full_question.tex") logger.debug("Successfully created temporary files") return response_text, questions_path, full_path except Exception as e: logger.error(f"Error generating question: {str(e)}") return f"Error: {str(e)}", None, Non def extract_and_run_sympy_code_simple(response_text): """ Extract SymPy code from the response and execute it. Returns the output exactly as SymPy would produce it. """ try: # Extract code sympy_start = response_text.find('```python') if sympy_start == -1: return "No SymPy code found in the response." code_start = response_text.find('\n', sympy_start) + 1 code_end = response_text.find('```', code_start) if code_end == -1: return "Malformed SymPy code block." sympy_code = response_text[code_start:code_end].strip() # Set up basic SymPy environment import io import sympy from contextlib import redirect_stdout # Capture output output_buffer = io.StringIO() with redirect_stdout(output_buffer): exec(sympy_code, {"sympy": sympy, "print": print}) return output_buffer.getvalue().strip() or "No output produced" except Exception as e: return f"Error executing SymPy code: {str(e)}" def check_and_resolve_discrepancy(initial_response, sympy_output): """ Compare the SymPy output with the initial response and resolve any discrepancies by making another API call to Claude. """ try: resolution_prompt = f"""Here is a mathematics question with two answers. The first, called Original solution, is a complete solution. The second, called SymPy Verification, will only provide the final answer. If the SymPy Verification answer is consistent with the final answer Original solution, then please say that they are consistent and briefly explain why. If the two answers are inconsistent with each other then please: 1. Identify which solution is correct 2. Explain the error in the incorrect solution 3. Provide a revised complete solution that fixes any errors Original solution: {initial_response} SymPy Verification Results: {sympy_output} Please maintain the same LaTeX formatting as the original solution.""" # Make API call for resolution message = anthropic.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=4096, temperature=0.2, messages=[{ "role": "user", "content": resolution_prompt }] ) resolution_text = message.content[0].text # Check if resolution contains new SymPy code if "```python" in resolution_text: new_sympy_output = extract_and_run_sympy_code(resolution_text) resolution_text += "\n\nNew SymPy Verification Results:\n```\n" + new_sympy_output + "\n```" return resolution_text except Exception as e: logger.error(f"Error in discrepancy resolution: {str(e)}") return initial_response # Create Gradio interface with gr.Blocks() as interface: gr.Markdown("# Advanced Mathematics Question Generator") gr.Markdown("""Generates a unique university-level mathematics question with solution using Claude 3. Each question features different topics and difficulty levels. Limited to 25 requests per day.""") with gr.Row(): with gr.Column(): subject_dropdown = gr.Dropdown( choices=[ "Single Variable Calculus", "Multivariable Calculus", "Linear Algebra", "Differential Equations", "Real Analysis", "Complex Analysis", "Abstract Algebra", "Probability Theory", "Numerical Analysis", "Topology" ], label="Select Mathematics Subject", info="Choose a subject for the question" ) difficulty_slider = gr.Slider( minimum=1, maximum=5, step=1, value=3, label="Difficulty Level", info="1: Very Easy, 2: Easy, 3: Moderate, 4: Difficult, 5: Very Difficult" ) question_type = gr.Radio( choices=["computation", "proof", "application"], label="Question Type", info="Select the type of question you want", value="computation" ) generate_btn = gr.Button("Generate Question") output_text = gr.Markdown( label="Generated Question Preview", latex_delimiters=[ {"left": "$$", "right": "$$", "display": True}, {"left": "$", "right": "$", "display": False} ] ) with gr.Row(): questions_file = gr.File(label="Question Only (LaTeX)") full_file = gr.File(label="Question with Solution (LaTeX)") generate_btn.click( generate_question, inputs=[ subject_dropdown, difficulty_slider, question_type ], outputs=[output_text, questions_file, full_file] ) if __name__ == "__main__": logger.info("Starting application") interface.launch()