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import os
import gradio as gr
from anthropic import Anthropic
import wolframalpha
from datetime import datetime, timedelta
from collections import deque
import re
# Initialize clients
anthropic = Anthropic(api_key=os.environ.get('ANTHROPIC_API_KEY'))
wolfram_client = wolframalpha.Client(os.environ.get('WOLFRAM_APPID'))
def parse_questions(content):
"""Parse questions and their solutions from Claude's output"""
# Split content into questions
questions = []
parts = re.split(r'\n\s*(\d+)\)', content)
# Remove any empty first element
if parts[0].strip() == '':
parts = parts[1:]
# Process pairs of question numbers and content
for i in range(0, len(parts)-1, 2):
question_num = parts[i]
question_content = parts[i+1]
try:
# Split into problem and solution
if "Solution:" in question_content:
problem, solution = question_content.split("Solution:", 1)
else:
continue
# Clean up the problem and solution
problem = problem.strip()
solution = solution.strip()
# Extract the final answer (look for last equation or stated answer)
final_answer_match = re.search(r'(?:final answer|=)\s*[-+]?(?:\d*\.)?\d+', solution.lower())
if final_answer_match:
final_answer = re.findall(r'[-+]?(?:\d*\.)?\d+', final_answer_match.group())[-1]
questions.append({
'number': question_num,
'problem': problem,
'solution': solution,
'final_answer': final_answer
})
except Exception as e:
print(f"Error parsing question {question_num}: {e}")
continue
return questions
def verify_solution(problem, answer):
"""Verify a mathematical solution using Wolfram Alpha"""
try:
# Format the query for Wolfram Alpha
query = problem.replace('$$', '').replace('$', '') # Remove LaTeX delimiters
result = wolfram_client.query(query)
# Try to get the numerical result
for pod in result.pods:
if pod.title in ['Result', 'Solution', 'Numerical result']:
wolfram_answer = pod.text
# Extract numerical value
wolfram_nums = re.findall(r'[-+]?(?:\d*\.)?\d+', wolfram_answer)
if wolfram_nums:
return {
'verified': abs(float(wolfram_nums[0]) - float(answer)) < 0.001,
'wolfram_solution': wolfram_answer,
'error': None
}
return {
'verified': False,
'wolfram_solution': None,
'error': "Could not extract numerical solution from Wolfram Alpha"
}
except Exception as e:
return {
'verified': False,
'wolfram_solution': None,
'error': str(e)
}
def generate_test(subject):
"""Generate and verify a math test"""
try:
system_prompt = """Generate 3 university-level math questions that can be verified numerically.
For each question:
1. Number the question as 1), 2), 3)
2. State the problem clearly using simple $$ for displayed math
3. Include "Solution:" before the solution
4. Show step-by-step work
5. End each solution with "Final answer = [number]"
6. Keep problems relatively simple (basic calculus, algebra, etc.)"""
message = anthropic.messages.create(
model="claude-3-opus-20240229",
max_tokens=1500,
temperature=0.7,
messages=[{
"role": "user",
"content": f"{system_prompt}\n\nWrite an exam for {subject} with simple numerical answers."
}]
)
# Get the content and parse questions
content = message.content[0].text
questions = parse_questions(content)
# Add verification results
verification_note = "\n\n---\n## Solution Verification:\n"
verification_results = []
for q in questions:
result = verify_solution(q['problem'], q['final_answer'])
verification_results.append(result)
verification_note += f"\nQuestion {q['number']}:\n"
if result['verified']:
verification_note += "✅ Solution verified by Wolfram Alpha\n"
else:
verification_note += "⚠️ Solution needs verification\n"
if result['wolfram_solution']:
verification_note += f"Wolfram Alpha result: {result['wolfram_solution']}\n"
if result['error']:
verification_note += f"Note: {result['error']}\n"
# Add usage statistics
usage_stats = f"""
\n---\nUsage Statistics:
• Input Tokens: {message.usage.input_tokens:,}
• Output Tokens: {message.usage.output_tokens:,}
• Wolfram Alpha calls: {len(verification_results)}
Cost Breakdown:
• Claude Cost: ${((message.usage.input_tokens / 1000) * 0.015) + ((message.usage.output_tokens / 1000) * 0.075):.4f}
• Wolfram API calls: {len(verification_results)}
"""
# Combine everything with proper spacing
final_output = content + "\n\n" + verification_note + usage_stats
return final_output
except Exception as e:
return f"Error: {str(e)}"
subjects = [
"Single Variable Calculus",
"Multivariable Calculus",
"Linear Algebra",
"Differential Equations",
"Real Analysis",
"Complex Analysis",
"Abstract Algebra",
"Probability Theory",
"Numerical Analysis",
"Topology"
]
# Create Gradio interface
interface = gr.Interface(
fn=generate_test,
inputs=gr.Dropdown(
choices=subjects,
label="Select Mathematics Subject",
info="Choose a subject for the exam questions"
),
outputs=gr.Markdown(
label="Generated Test",
latex_delimiters=[
{"left": "$$", "right": "$$", "display": True},
{"left": "$", "right": "$", "display": False}
]
),
title="Advanced Mathematics Test Generator",
description="""Generates university-level mathematics exam questions with solutions using Claude 3 Opus.
Limited to 25 requests per day. Please use responsibly.""",
theme="default",
allow_flagging="never"
)
# Launch the interface
if __name__ == "__main__":
interface.launch() |