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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
# 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)
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-sonnet-20240229"
},
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-sonnet-20240229"
},
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-sonnet-20240229"
},
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-opus-20240229"
},
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-opus-20240229"
}
}
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 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"],
}
selected_topic = random.choice(topics.get(subject, ["general"]))
logger.debug(f"Selected topic: {selected_topic}")
difficulty_params = get_difficulty_parameters(difficulty)
[Previous code remains the same until the system_prompt section...]
if question_type == "application":
application_addition = """
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)
"""
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 application 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 to complex real-world scenarios
- Graduate-level mathematical reasoning in practical contexts
- Sophisticated modeling of real situations
- Creative problem-solving approaches
- Multiple stages of mathematical modeling and analysis
Follow these specific constraints:
{chr(10).join(f' - {c}' for c in difficulty_params['constraints'])}
{application_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 $$ on separate lines for equations and solutions
- Put each solution step on its own line in $$ $$
- DO NOT use \\begin{{aligned}} or similar environments
6. Include a detailed solution with thorough explanations of:
- How to model the situation
- Why specific mathematical techniques were chosen
- How to interpret the results
7. Maintain clear, precise formatting"""
else:
system_prompt = f"""You are an expert mathematics professor creating a {difficulty_params['description']} exam question.
STRICT REQUIREMENTS:
1. Write exactly 1 application question on {subject} covering {selected_topic}.
2. Difficulty Level Guidelines:
{difficulty_params['description'].upper()}
Follow these specific constraints:
{chr(10).join(f' - {c}' for c in difficulty_params['constraints'])}
{application_addition}
3. Style Reference:
Question should be {difficulty_params['example_style']}
4. For LaTeX formatting:
- Use $ for inline math
- Use $$ 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 that explains:
- How to model the situation
- Why specific mathematical techniques were chosen
- How to interpret the results
6. Maintain clear formatting"""
else:
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")
# 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, None
# 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()