Add GPT-5-mini auto-solver for daily LeetCode problems

.github/workflows/auto_solver.yml

@@ -0,0 +1,41 @@
+name: Auto-solve Daily LeetCode Problem
+
+on:
+  schedule:
+    - cron: '1 0 * * *' # Runs daily at 00:01 UTC (1 minute after daily problems are released)
+  workflow_dispatch: # Allows for manual triggering
+
+jobs:
+  auto-solve:
+    runs-on: ubuntu-latest
+    if: github.repository_owner == 'ContextLab'  # only run if on the ContextLab (source) repository!
+
+    permissions:
+      contents: write  # Required to push commits back to the repository
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.11'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install requests openai
+
+      - name: Run auto-solver
+        env:
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        run: |
+          python auto_solver.py
+
+      - name: Commit and push changes
+        uses: stefanzweifel/git-auto-commit-action@v4
+        with:
+          commit_message: "Auto-solve daily LeetCode problem using GPT-5-mini"
+          branch: main
+          file_pattern: problems/*/gpt5-mini.md

README.md

@@ -6,6 +6,14 @@ Each day (ideally) we'll attempt the daily [leetcode](https://leetcode.com) prob
 
 _**Note:** Our group is currently taking a break from grinding leetcode. The table below will continue to update automatically, but newer problems likey won't have their solutions provided.  Pull requests will still be supported and reviewed if others would like to contribute solutions._
 
+## 🤖 Auto-Solver
+
+We've added an automatic solver that uses OpenAI's GPT-5-mini to generate solutions for each day's problem! Every day at 00:01 UTC, the system automatically:
+- Fetches the daily LeetCode problem
+- Generates a thoughtful solution with explanations
+- Commits the solution as `gpt5-mini.md` in the problem's folder
+
+This means you'll always find an AI-generated solution to compare with or learn from!
 
 # Problems we've attempted so far:
 

auto_solver.py

@@ -0,0 +1,201 @@
+#!/usr/bin/env python3
+"""
+Auto-solver for daily LeetCode problems using OpenAI GPT-5-mini.
+This script fetches the daily problem, uses AI to solve it, and creates a solution file.
+"""
+
+import os
+import sys
+import json
+import requests
+from datetime import datetime
+from openai import OpenAI
+
+
+def fetch_daily_problem():
+    """
+    Fetch the daily LeetCode problem details using the GraphQL API.
+
+    Returns:
+        dict: Problem details including ID, title, description, etc.
+    """
+    leetcode_api_url = "https://leetcode.com/graphql"
+    daily_challenge_query = {
+        "query": """query questionOfToday {
+            activeDailyCodingChallengeQuestion {
+                date
+                link
+                question {
+                    questionFrontendId
+                    title
+                    titleSlug
+                    difficulty
+                    content
+                    exampleTestcases
+                }
+            }
+        }""",
+        "operationName": "questionOfToday"
+    }
+
+    try:
+        response = requests.post(leetcode_api_url, json=daily_challenge_query)
+        response.raise_for_status()
+        data = response.json()
+
+        if 'data' in data and 'activeDailyCodingChallengeQuestion' in data['data']:
+            problem_data = data['data']['activeDailyCodingChallengeQuestion']
+            question = problem_data['question']
+
+            return {
+                'problem_id': question['questionFrontendId'],
+                'title': question['title'],
+                'title_slug': question['titleSlug'],
+                'difficulty': question['difficulty'],
+                'content': question['content'],
+                'link': f"https://leetcode.com/problems/{question['titleSlug']}/description/?envType=daily-question",
+                'example_testcases': question.get('exampleTestcases', '')
+            }
+        else:
+            print("Error: Unexpected response structure from LeetCode API")
+            print(json.dumps(data, indent=2))
+            return None
+
+    except Exception as e:
+        print(f"Error fetching daily problem: {e}")
+        return None
+
+
+def generate_solution_with_ai(problem_info, api_key):
+    """
+    Use OpenAI GPT-5-mini to generate a solution for the problem.
+
+    Args:
+        problem_info (dict): Problem details from LeetCode
+        api_key (str): OpenAI API key
+
+    Returns:
+        str: Generated solution in markdown format
+    """
+    try:
+        client = OpenAI(api_key=api_key)
+
+        # Create a detailed prompt for the AI
+        prompt = f"""You are solving a LeetCode problem. Generate a complete solution following this exact format:
+
+# [Problem {problem_info['problem_id']}: {problem_info['title']}]({problem_info['link']})
+
+## Initial thoughts (stream-of-consciousness)
+[Provide your initial thoughts about the problem, what approach comes to mind first, any observations about the problem structure]
+
+## Refining the problem, round 2 thoughts
+[Discuss any refinements to your approach, edge cases to consider, alternative solutions, time/space complexity considerations]
+
+## Attempted solution(s)
+```python
+[Provide a complete, working Python solution]
+```
+- [Add brief notes about the solution approach, complexity analysis, and any important implementation details]
+
+Here is the problem:
+
+**Title:** {problem_info['title']}
+**Difficulty:** {problem_info['difficulty']}
+**Link:** {problem_info['link']}
+
+**Problem Description:**
+{problem_info['content']}
+
+Please provide a thoughtful, well-explained solution that demonstrates clear problem-solving skills. The solution should be efficient and include proper complexity analysis."""
+
+        response = client.chat.completions.create(
+            model="gpt-5-mini",
+            messages=[
+                {"role": "system", "content": "You are an expert software engineer solving LeetCode problems. Provide clear explanations and efficient solutions."},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=0.7,
+            max_tokens=2000
+        )
+
+        return response.choices[0].message.content
+
+    except Exception as e:
+        print(f"Error generating solution with AI: {e}")
+        return None
+
+
+def save_solution(problem_id, solution_content):
+    """
+    Save the generated solution to the appropriate file.
+
+    Args:
+        problem_id (str): The LeetCode problem ID
+        solution_content (str): The markdown content to save
+
+    Returns:
+        str: Path to the created file
+    """
+    try:
+        problem_dir = f"problems/{problem_id}"
+        os.makedirs(problem_dir, exist_ok=True)
+
+        solution_file = f"{problem_dir}/gpt5-mini.md"
+
+        with open(solution_file, 'w', encoding='utf-8') as f:
+            f.write(solution_content)
+
+        print(f"Solution saved to: {solution_file}")
+        return solution_file
+
+    except Exception as e:
+        print(f"Error saving solution: {e}")
+        return None
+
+
+def main():
+    """Main execution function."""
+    print("=" * 60)
+    print("LeetCode Auto-Solver - Starting...")
+    print("=" * 60)
+
+    # Get OpenAI API key from environment
+    api_key = os.environ.get('OPENAI_API_KEY')
+    if not api_key:
+        print("Error: OPENAI_API_KEY environment variable not set")
+        sys.exit(1)
+
+    # Fetch today's problem
+    print("\n1. Fetching daily LeetCode problem...")
+    problem_info = fetch_daily_problem()
+    if not problem_info:
+        print("Failed to fetch daily problem")
+        sys.exit(1)
+
+    print(f"   Problem ID: {problem_info['problem_id']}")
+    print(f"   Title: {problem_info['title']}")
+    print(f"   Difficulty: {problem_info['difficulty']}")
+
+    # Generate solution using AI
+    print("\n2. Generating solution with GPT-5-mini...")
+    solution = generate_solution_with_ai(problem_info, api_key)
+    if not solution:
+        print("Failed to generate solution")
+        sys.exit(1)
+
+    print("   Solution generated successfully!")
+
+    # Save the solution
+    print("\n3. Saving solution to file...")
+    saved_path = save_solution(problem_info['problem_id'], solution)
+    if not saved_path:
+        print("Failed to save solution")
+        sys.exit(1)
+
+    print("\n" + "=" * 60)
+    print("Auto-solver completed successfully!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()