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diff --git a/README.md b/README.md
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 ---
 title: Spiral
-emoji: 🦀
-colorFrom: pink
-colorTo: yellow
+app_file: human_test.py
 sdk: gradio
-sdk_version: 5.24.0
-app_file: app.py
-pinned: false
+sdk_version: 5.22.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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diff --git a/human_test.py b/human_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..85abbc865b33dd5ffd91519f698c33ae93e67b16
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+++ b/human_test.py
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+"""
+Human Maze Solving Experiment
+-----------------------------
+This application creates a web interface for human participants to solve maze puzzles, 
+mirroring the experiments conducted with AI systems. The program:
+
+1. Displays mazes from the same dataset used in AI experiments
+2. Allows participants to navigate through mazes of various sizes (5x5, 7x7) and shapes 
+   (square, cross, spiral, triangle, C, Z)
+3. Records performance data including:
+   - Time taken to solve each maze
+   - Complete movement history
+   - Success/failure rate
+4. Tests shape recognition by asking participants to identify maze shapes after solving
+5. Saves results in a structured format matching the AI experiment results for direct comparison
+
+Usage: Run with 'python human_test.py' to start the interface, which can be shared 
+with participants through the generated public URL.
+"""
+
+import os
+import time
+import numpy as np
+import gradio as gr
+from pathlib import Path
+import random
+import json
+from PIL import Image, ImageDraw, ImageFont
+import re
+from maze_generator import *
+from solution_verifier import get_valid_moves
+from collections import defaultdict
+import datetime
+
+# Constants for maze representation
+WALL = 0
+PATH = 1
+POS = 2
+END = 3
+# Additional constant for generation path
+GEN_PATH = 1  # Same as PATH but will use a different color in the generation grid
+
+# Constants for experiment parameters
+SIZES = [(5, 5), (7, 7)]
+SHAPES = ["square", "cross", "spiral", "triangle", "C", "Z"]
+MAZES_PER_COMBINATION = 10  # Number of mazes to complete for each size/shape combination
+
+def save_numpy_array_as_image(array: np.ndarray, cell_size: int = 0, target_size: int = 500,
+                             font_scale: float = 0.4, is_generation: bool = False) -> Image.Image:
+    """
+    Converts a 2D NumPy array to a PIL Image with:
+      - Distinct colors for each cell value with optimal contrast.
+      - Thick borders around each cell.
+      - Text showing each cell's coordinates in a dynamically sized font.
+      - Final image sized to approximately target_size (default 500px).
+
+    Args:
+        array: 2D NumPy array representing the maze
+        cell_size: Size of each cell in pixels (default: 0, which means calculate based on target_size)
+        target_size: Target size for the full image (default: 500px)
+        font_scale: Scale factor for the font size as a proportion of cell size (default: 0.4)
+        is_generation: If True, use a different color for PATH to make it more visible in generation mode
+
+    Returns:
+        PIL Image object
+    """
+    # 1) Map values to colors (improved for better contrast)
+    if is_generation:
+        # Colors for generation grid - make PATH light gray to show traveled path
+        color_map = {
+            0: (80, 80, 80),      # dark gray - wall (WALL=0)
+            1: (200, 200, 200),   # light gray - path/trail (PATH=1)
+            2: (0, 102, 204),     # blue - current position (POS=2)
+            3: (204, 0, 0),       # darker red - end (END=3)
+        }
+    else:
+        # Standard colors for maze solving
+        color_map = {
+            0: (80, 80, 80),      # dark gray - wall (WALL=0)
+            1: (255, 255, 255),   # white - path (PATH=1)
+            2: (0, 102, 204),     # blue - current position (POS=2)
+            3: (204, 0, 0),       # darker red - end (END=3)
+        }
+    
+    border_color = (0, 0, 0)  # black borders for maximum contrast
+    border_width = 2  # border thickness
+
+    height, width = array.shape
+
+    # 2) Calculate the appropriate cell size
+    if cell_size <= 0:
+        # Calculate cell size based on target_size
+        cell_size = min(target_size // width, target_size // height)
+
+    # 3) Create the image with calculated dimensions
+    img_width = width * cell_size
+    img_height = height * cell_size
+    image = Image.new("RGB", (img_width, img_height), (255, 255, 255))
+    draw = ImageDraw.Draw(image)
+
+    # 4) Calculate a dynamic font size based on font_scale (default 40% of cell size)
+    font_size = max(10, int(cell_size * font_scale))
+    
+    # Try to use a common font or default to load_default
+    try:
+        # Try common fonts that might be available
+        fonts_to_try = ["arial.ttf", "Arial.ttf", "DejaVuSans.ttf", "FreeSans.ttf",
+                         "LiberationSans-Regular.ttf", "Verdana.ttf"]
+        font = None
+
+        for font_name in fonts_to_try:
+            try:
+                font = ImageFont.truetype(font_name, font_size)
+                break
+            except IOError:
+                continue
+
+        if font is None:
+            # If specific fonts failed, try to use system font directory
+            system_font_dirs = [
+                "/usr/share/fonts",  # Linux
+                "/Library/Fonts",    # macOS
+                "C:/Windows/Fonts"   # Windows
+            ]
+
+            for font_dir in system_font_dirs:
+                if os.path.exists(font_dir):
+                    font_files = [f for f in os.listdir(font_dir)
+                                 if f.endswith(('.ttf', '.otf')) and 'bold' not in f.lower()]
+                    if font_files:
+                        try:
+                            font = ImageFont.truetype(os.path.join(font_dir, font_files[0]), font_size)
+                            break
+                        except:
+                            pass
+    except Exception:
+        pass
+
+    # Last resort: fall back to default font
+    if font is None:
+        font = ImageFont.load_default()
+
+    # 5) Function to get text dimensions that works with different PIL versions
+    def get_text_dimensions(text, font):
+        try:
+            # For newer PIL versions
+            return font.getbbox(text)[2:4]
+        except AttributeError:
+            try:
+                # For PIL 8.0.0+
+                return font.getsize(text)
+            except AttributeError:
+                # Fallback method for older PIL versions
+                return font.getmask(text).size
+
+    # 6) Draw each cell
+    for row in range(height):
+        for col in range(width):
+            x1 = col * cell_size
+            y1 = row * cell_size
+            x2 = x1 + cell_size
+            y2 = y1 + cell_size
+
+            cell_value = array[row, col]
+            fill_color = color_map.get(cell_value, (255, 255, 255))
+
+            # Draw the cell with border
+            draw.rectangle(
+                [x1, y1, x2, y2],
+                fill=fill_color,
+                outline=border_color,
+                width=border_width
+            )
+
+            # Write the cell coordinates in the center
+            text = f"({row},{col})"
+            text_width, text_height = get_text_dimensions(text, font)
+            text_x = x1 + (cell_size - text_width) // 2
+            text_y = y1 + (cell_size - text_height) // 2
+
+            # Text color based on background for better contrast
+            # Dark text on light backgrounds, light text on dark backgrounds
+            luminance = 0.299 * fill_color[0] + 0.587 * fill_color[1] + 0.114 * fill_color[2]
+            text_color = (0, 0, 0) if luminance > 128 else (255, 255, 255)
+
+            draw.text((text_x, text_y), text, fill=text_color, font=font)
+
+    return image
+
+def load_npy_files(folder_path):
+    """Load .npy maze files from the specified folder."""
+    all_file_data = []
+    idx = 0
+    # Traverse the folder and its subfolders
+    for root, dirs, files in os.walk(folder_path):
+        for filename in files:
+            # Check if the file is a .npy file
+            if filename.endswith(".npy"):
+                file_path = os.path.join(root, filename)
+                # Load the .npy file as a NumPy array
+                try:
+                    array_data = np.load(file_path)  # Keep as numpy array for easier processing later
+                    file_data = {
+                        'name': filename,
+                        'id': idx,
+                        'path': os.path.relpath(file_path, folder_path),  # Store relative path
+                        'data': array_data  # Store the array data
+                    }
+                    idx += 1
+                    all_file_data.append(file_data)
+                except Exception as e:
+                    print(f"Could not load {filename}: {e}")
+    return all_file_data
+
+class MazeExperiment:
+    """Class to manage the maze experiment."""
+    
+    def __init__(self, results_dir="results", maze_dir="experiment_mazes"):
+        """Initialize the maze experiment.
+        
+        Args:
+            results_dir: Directory to save experiment results
+            maze_dir: Directory containing maze files
+        """
+        self.results_dir = results_dir
+        self.maze_dir = maze_dir
+        self.current_maze = None
+        self.current_file_info = None
+        self.current_size = None
+        self.current_shape = None
+        self.start_time = None
+        self.maze_complete = False
+        self.experiment_complete = False
+        self.moves = []
+        self.current_phase = "solve"
+        
+        # Ensure results directory exists
+        os.makedirs(results_dir, exist_ok=True)
+        
+        # Initialize the participant ID
+        self.participant_id = f"p{datetime.datetime.now().strftime('%Y%m%d%H%M%S')}"
+        
+        # Initialize tracking of completed mazes for each size/shape combination
+        self.combination_counts = defaultdict(int)
+        
+        # Initialize the completed combinations list
+        self.completed_combinations = []
+        
+        # Calculate total mazes in the experiment
+        self.mazes_per_combination = MAZES_PER_COMBINATION
+        self.total_mazes = len(SIZES) * len(SHAPES) * self.mazes_per_combination
+        
+        # Load the first maze
+        self.load_next_combination()
+        
+    def load_random_maze(self, size, shape):
+        """Load a random maze of the specified size and shape.
+        
+        Args:
+            size: Tuple of (height, width)
+            shape: String representing the maze shape
+            
+        Returns:
+            Tuple of (maze_image, message, phase, progress)
+        """
+        # Update current size and shape
+        self.current_size = size
+        self.current_shape = shape
+        
+        # Reset maze state
+        self.maze_complete = False
+        self.moves = []
+        self.start_time = time.time()
+        
+        try:
+            # Get list of maze files for the specified size and shape
+            size_str = f"{size[0]}x{size[1]}"
+            
+            # Check the nested directory structure
+            shape_dir = os.path.join(self.maze_dir, size_str, shape)
+            if not os.path.exists(shape_dir):
+                return None, f"Directory not found: {shape_dir}", "error", self.get_progress()
+                
+            # Get all maze files in the shape directory
+            maze_files = []
+            for file in os.listdir(shape_dir):
+                if file.endswith(".npy"):
+                    maze_files.append(file)
+            
+            if not maze_files:
+                return None, f"No maze files found in {shape_dir}", "error", self.get_progress()
+            
+            # Select a random maze file
+            maze_file = random.choice(maze_files)
+            maze_path = os.path.join(shape_dir, maze_file)
+            
+            # Load the maze
+            self.current_maze = np.load(maze_path)
+            
+            # Extract file information for tracking completion
+            self.current_file_info = {
+                "file": maze_file,
+                "size": size_str,
+                "shape": shape
+            }
+            
+            # Calculate progress information
+            completed_mazes = self.combination_counts[(size_str, shape)]
+            total_combinations = len(SIZES) * len(SHAPES)
+            completed_combinations = len(self.completed_combinations)
+            
+            # Create progress message
+            progress_msg = f"Maze {completed_mazes + 1}/{MAZES_PER_COMBINATION} for {size_str} {shape} " \
+                          f"(Combination {completed_combinations + 1}/{total_combinations})"
+            
+            # Return the maze image, progress message, phase, and progress
+            return self.render_maze(), progress_msg, "solve", self.get_progress()
+        except Exception as e:
+            return None, f"Error loading maze: {str(e)}", "error", self.get_progress()
+
+    def load_next_combination(self):
+        """Load the next maze combination in the experiment sequence.
+        
+        If all combinations have been completed for all mazes, mark the experiment as complete.
+        
+        Returns:
+            Tuple of (maze_image, message, phase, progress)
+        """
+        # Check if any combinations need more mazes
+        available_combinations = []
+        
+        for size in SIZES:
+            size_str = f"{size[0]}x{size[1]}"
+            for shape in SHAPES:
+                combo_key = (size_str, shape)
+                if self.combination_counts[combo_key] < MAZES_PER_COMBINATION:
+                    if combo_key not in self.completed_combinations:
+                        available_combinations.append((size, shape))
+        
+        if not available_combinations:
+            self.experiment_complete = True
+            return None, "Experiment complete! Thank you for participating.", "complete", self.get_progress()
+        
+        # Choose the next combination
+        next_combination = available_combinations[0]  # Take the first available
+        
+        # Load a random maze for this combination
+        return self.load_random_maze(*next_combination)
+    
+    def process_move(self, direction):
+        """Process a move in the maze.
+        
+        Args:
+            direction (str): The direction to move in ('up', 'down', 'left', 'right').
+            
+        Returns:
+            Tuple of (maze_image, message, phase, progress)
+        """
+        if self.current_maze is None:
+            return self.load_random_maze(self.current_size, self.current_shape)
+        
+        # Get the current position
+        i, j = np.where(self.current_maze == 2)
+        if len(i) == 0:
+            return self.render_maze(), "Invalid maze state. No current position found.", self.current_phase, self.get_progress()
+        i, j = i[0], j[0]
+        
+        # Calculate the new position
+        new_i, new_j = i, j
+        if direction == 'up':
+            new_i -= 1
+        elif direction == 'down':
+            new_i += 1
+        elif direction == 'left':
+            new_j -= 1
+        elif direction == 'right':
+            new_j += 1
+            
+        # Check if the new position is valid
+        if new_i < 0 or new_i >= self.current_maze.shape[0] or new_j < 0 or new_j >= self.current_maze.shape[1]:
+            return self.render_maze(), "Can't move outside the maze!", self.current_phase, self.get_progress()
+            
+        # Check if the new position is a wall (0)
+        if self.current_maze[new_i, new_j] == WALL:
+            # Wall collision counts as a failure
+            current_time = time.time()
+            elapsed_time = current_time - self.start_time
+            
+            # Save the results with failed status
+            self.save_results(elapsed_time, failed=True)
+            
+            # Increment the count for the current combination
+            self.combination_counts[(f"{self.current_size[0]}x{self.current_size[1]}", self.current_shape)] += 1
+            
+            # Check if all mazes for this combination are completed
+            if self.combination_counts[(f"{self.current_size[0]}x{self.current_size[1]}", self.current_shape)] >= MAZES_PER_COMBINATION:
+                self.completed_combinations.append((f"{self.current_size[0]}x{self.current_size[1]}", self.current_shape))
+            
+            # Process the complete maze and get the next one
+            return self.process_complete_maze()
+            
+        # Check if the new position is the end (3)
+        if self.current_maze[new_i, new_j] == END:
+            # Move to the end
+            self.current_maze[i, j] = PATH  # Set old position to path
+            self.current_maze[new_i, new_j] = POS  # Set the new position to player
+            
+            # Calculate elapsed time
+            current_time = time.time()
+            elapsed_time = current_time - self.start_time
+            
+            # Save the results
+            self.save_results(elapsed_time)
+            
+            # Increment the count for the current combination
+            self.combination_counts[(f"{self.current_size[0]}x{self.current_size[1]}", self.current_shape)] += 1
+            
+            # Check if all mazes for this combination are completed
+            if self.combination_counts[(f"{self.current_size[0]}x{self.current_size[1]}", self.current_shape)] >= MAZES_PER_COMBINATION:
+                self.completed_combinations.append((f"{self.current_size[0]}x{self.current_size[1]}", self.current_shape))
+            
+            # Mark as complete and transition to recognition phase
+            self.maze_complete = True
+            self.current_phase = "recognize"
+            return self.render_maze(), "Maze complete! What shape do you think this maze represents?", "recognize", self.get_progress()
+                
+        # Move to the new position if it's a path (1)
+        self.current_maze[i, j] = PATH  # Clear the current position (set to path)
+        self.current_maze[new_i, new_j] = POS  # Set the new position to player
+        
+        # Record the move
+        self.moves.append(direction)
+        
+        return self.render_maze(), f"Moved {direction}. Keep going!", self.current_phase, self.get_progress()
+    
+    def process_complete_maze(self):
+        """Process a completed maze and load the next one.
+        
+        Returns:
+            Tuple of (maze_image, message, phase, progress)
+        """
+        # Display appropriate message based on if the maze was failed or completed
+        last_maze_failed = False
+        
+        # Check if we have result files to determine if the last maze was failed
+        result_files = []
+        for root, _, files in os.walk(self.results_dir):
+            for file in files:
+                if file.startswith(self.participant_id) and file.endswith(".json"):
+                    file_path = os.path.join(root, file)
+                    result_files.append(file_path)
+        
+        if result_files:
+            try:
+                file_path = sorted(result_files, key=os.path.getmtime)[-1]
+                with open(file_path, 'r') as f:
+                    data = json.load(f)
+                    last_maze_failed = data.get("failed", False)
+            except Exception as e:
+                print(f"Error checking failure status: {e}")
+                
+        # Load the next maze combination
+        img, msg, phase, progress = self.load_next_combination()
+        
+        # Update message if the last maze was failed
+        if last_maze_failed:
+            msg = f"Maze failed (wall collision). {msg}"
+            
+        return img, msg, phase, progress
+    
+    def is_experiment_complete(self):
+        """Check if all mazes in the experiment have been completed."""
+        for size in SIZES:
+            size_str = f"{size[0]}x{size[1]}"
+            for shape in SHAPES:
+                combo_key = (size_str, shape)
+                if self.combination_counts[combo_key] < MAZES_PER_COMBINATION:
+                    return False
+        return True
+        
+    def load_next_maze(self):
+        """Load the next maze based on current progress."""
+        # Find the next combination that needs more completions
+        next_combination = None
+        for size in SIZES:
+            size_str = f"{size[0]}x{size[1]}"
+            for shape in SHAPES:
+                if self.combination_counts[(size_str, shape)] < MAZES_PER_COMBINATION:
+                    next_combination = (size, shape)
+                    break
+            if next_combination:
+                break
+                
+        if next_combination:
+            size, shape = next_combination
+            self.load_random_maze(size, shape)
+        else:
+            # All combinations completed
+            self.current_maze = None
+    
+    def save_results(self, elapsed_time, failed=False):
+        """Save the results to a file.
+        
+        Args:
+            elapsed_time: Time elapsed during maze solving
+            failed: Boolean indicating if the maze was failed
+        """
+        if not self.current_file_info:
+            return
+            
+        # Create a unique filename
+        timestamp = int(time.time())
+        filename = f"{self.results_dir}/{self.participant_id}_{self.current_file_info['size']}_{self.current_file_info['shape']}_{timestamp}.json"
+        
+        # Prepare data to save
+        data = {
+            "participant_id": self.participant_id,
+            "maze_file": self.current_file_info['file'],
+            "maze_type": {
+                "size": self.current_file_info['size'],
+                "shape": self.current_file_info['shape']
+            },
+            "moves": self.moves,
+            "total_moves": len(self.moves),
+            "completion_time": elapsed_time,
+            "timestamp": timestamp,
+            "maze_complete": self.maze_complete,
+            "failed": failed
+        }
+        
+        # Save to file
+        with open(filename, 'w') as f:
+            json.dump(data, f, indent=2)
+            
+        print(f"Results saved to {filename}")
+    
+    def skip_maze(self):
+        """Skip the current maze and save as not completed."""
+        if self.current_maze is not None:
+            self.save_results(0)
+            self.maze_complete = True
+            return save_numpy_array_as_image(self.current_maze), "Maze skipped. Load a new maze.", "solve", self.get_progress()
+        return None, "No maze loaded to skip.", "solve", self.get_progress()
+    
+    def ask_shape_recognition(self):
+        """Ask the participant to recognize the shape of the maze."""
+        if self.maze_complete and self.current_maze is not None:
+            return "What shape do you think this maze was designed to look like? (square, cross, spiral, triangle, C, Z)"
+        return ""
+    
+    def submit_shape_recognition(self, recognized_shape):
+        """Submit the participant's shape recognition answer."""
+        if not recognized_shape or not self.maze_complete:
+            return save_numpy_array_as_image(self.current_maze), "Please solve the maze first and enter a shape before submitting.", "recognize", self.get_progress()
+            
+        # Check if recognized_shape is valid
+        recognized_shape = recognized_shape.strip().lower()
+        valid_shapes = [s.lower() for s in SHAPES]
+        
+        if recognized_shape not in valid_shapes:
+            return save_numpy_array_as_image(self.current_maze), f"Invalid shape. Please enter one of: {', '.join(SHAPES)}", "recognize", self.get_progress()
+        
+        try:
+            # Update the saved results file with the recognized shape
+            result_files = []
+            for root, _, files in os.walk(self.results_dir):
+                for file in files:
+                    if file.startswith(self.participant_id) and file.endswith(".json"):
+                        file_path = os.path.join(root, file)
+                        try:
+                            # Verify the file is valid JSON before adding it
+                            with open(file_path, 'r') as f:
+                                json.load(f)
+                            result_files.append(file_path)
+                        except json.JSONDecodeError:
+                            print(f"Skipping invalid JSON file: {file_path}")
+                            continue
+            
+            # Sort by creation time and get the most recent
+            if result_files:
+                file_path = sorted(result_files, key=os.path.getmtime)[-1]
+                
+                try:
+                    with open(file_path, 'r') as f:
+                        data = json.load(f)
+                    
+                    # Add the recognized shape
+                    data["recognized_shape"] = recognized_shape
+                    data["recognition_correct"] = (recognized_shape.lower() == self.current_shape.lower())
+                    
+                    with open(file_path, 'w') as f:
+                        json.dump(data, f, indent=2)
+                    
+                    # Update phase and create the generation grid immediately
+                    self.current_phase = "generate"
+                    self.create_generation_grid()
+                    
+                    # Get the generation grid image
+                    generation_img = self.get_generation_grid_image()
+                    
+                    # Prepare feedback message
+                    if data["recognition_correct"]:
+                        feedback = f"Correct! This is a {self.current_shape} maze."
+                    else:
+                        feedback = f"Not quite. This is a {self.current_shape} maze."
+                    
+                    message = f"{feedback} Now draw a maze of shape '{self.current_shape}' using the movement buttons."
+                    return generation_img, message, "generate", self.get_progress()
+                except Exception as e:
+                    print(f"Error processing JSON file {file_path}: {e}")
+                    # Create a new result file if the existing one is corrupted
+                    self.save_results(0)
+                    # Try again with the newly created file
+                    return self.submit_shape_recognition(recognized_shape)
+            else:
+                # If no valid result files found, create a new one
+                self.save_results(0)
+                # Add the recognized shape directly
+                self.current_phase = "generate"
+                self.create_generation_grid()
+                generation_img = self.get_generation_grid_image()
+                return generation_img, f"Now draw a maze of shape '{self.current_shape}' using the movement buttons.", "generate", self.get_progress()
+        except Exception as e:
+            print(f"Error in shape recognition: {e}")
+            return save_numpy_array_as_image(self.current_maze), "An error occurred. Please try again.", "recognize", self.get_progress()
+    
+    def create_generation_grid(self):
+        """Create a blank grid for maze generation."""
+        if self.current_size is None:
+            return None
+            
+        # Create a blank grid with the same dimensions as the current maze
+        height, width = self.current_maze.shape
+        grid = np.zeros((height, width), dtype=np.int8)  # Start with all walls (0)
+        
+        # Set the starting position
+        center_row = height // 2
+        center_col = width // 2
+        grid[center_row, center_col] = POS  # Current position (2)
+        
+        self.generation_grid = grid
+        self.generation_trail = [(center_row, center_col)]
+        
+        return save_numpy_array_as_image(self.generation_grid, is_generation=True)
+    
+    def move_in_generation(self, direction):
+        """Move in the specified direction on the generation grid."""
+        if not hasattr(self, 'generation_grid') or self.generation_grid is None:
+            # Create the generation grid if it doesn't exist
+            img = self.create_generation_grid()
+            return img, "Generation grid created. Draw a path in the shape of a " + self.current_shape, "generate", self.get_progress()
+        
+        # Find current position in generation grid
+        player_pos = np.argwhere(self.generation_grid == POS)[0]
+        player_pos = (int(player_pos[0]), int(player_pos[1]))
+        
+        # Calculate new position based on direction
+        new_pos = None
+        height, width = self.generation_grid.shape
+        
+        if direction == "up" and player_pos[0] > 0:
+            new_pos = (player_pos[0] - 1, player_pos[1])
+        elif direction == "down" and player_pos[0] < height - 1:
+            new_pos = (player_pos[0] + 1, player_pos[1])
+        elif direction == "left" and player_pos[1] > 0:
+            new_pos = (player_pos[0], player_pos[1] - 1)
+        elif direction == "right" and player_pos[1] < width - 1:
+            new_pos = (player_pos[0], player_pos[1] + 1)
+        # Add diagonal movements
+        elif direction == "up-left" and player_pos[0] > 0 and player_pos[1] > 0:
+            new_pos = (player_pos[0] - 1, player_pos[1] - 1)
+        elif direction == "up-right" and player_pos[0] > 0 and player_pos[1] < width - 1:
+            new_pos = (player_pos[0] - 1, player_pos[1] + 1)
+        elif direction == "down-left" and player_pos[0] < height - 1 and player_pos[1] > 0:
+            new_pos = (player_pos[0] + 1, player_pos[1] - 1)
+        elif direction == "down-right" and player_pos[0] < height - 1 and player_pos[1] < width - 1:
+            new_pos = (player_pos[0] + 1, player_pos[1] + 1)
+        
+        if new_pos:
+            # Check if new position is already a path (don't overwrite)
+            if self.generation_grid[new_pos[0], new_pos[1]] == PATH:
+                return save_numpy_array_as_image(self.generation_grid, is_generation=True), "You've already drawn here. Try a different direction."
+            
+            # Update the grid
+            # Current position becomes path
+            self.generation_grid[player_pos[0], player_pos[1]] = PATH  # Set to PATH (1) - will be light gray in generation image
+            # New position becomes current position
+            self.generation_grid[new_pos[0], new_pos[1]] = POS  # Set to POS (2) - will be blue in image
+            
+            # Add to trail if not already there
+            if new_pos not in self.generation_trail:
+                self.generation_trail.append(new_pos)
+            
+            return save_numpy_array_as_image(self.generation_grid, is_generation=True), f"Drawing {self.current_shape} shape. Use movement buttons to draw the path."
+        
+        return save_numpy_array_as_image(self.generation_grid, is_generation=True), "Cannot move in that direction."
+    
+    def reset_generation(self):
+        """Reset the generation grid."""
+        grid = self.create_generation_grid()
+        return grid, "Generation grid reset. Start drawing the shape again."
+    
+    def validate_generation(self):
+        """Validate the user's generated maze shape against the current shape.
+        
+        Returns:
+            Dict with validation results
+        """
+        if not hasattr(self, 'generation_grid') or self.generation_grid is None:
+            return {
+                'valid': False,
+                'error': 'No maze has been generated'
+            }
+            
+        # Extract the path from the generation grid (values of PATH=1 or POS=2)
+        path_cells = []
+        for i in range(len(self.generation_grid)):
+            for j in range(len(self.generation_grid[i])):
+                if self.generation_grid[i][j] == PATH or self.generation_grid[i][j] == POS:
+                    path_cells.append((i, j))
+                    
+        # Compute a simple shape score - can be expanded with more sophisticated metric
+        total_cells = self.generation_grid.shape[0] * self.generation_grid.shape[1]
+        path_ratio = len(path_cells) / total_cells
+        
+        # Validate based on current shape 
+        # (simple validation - could be enhanced with shape recognition)
+        valid = True
+        feedback = "Shape accepted. Great work!"
+        
+        # Basic validation to avoid trivial mazes
+        if len(path_cells) < 5:
+            valid = False
+            feedback = "Your drawing is too small. Please create a more complex shape."
+            
+        return {
+            'valid': valid,
+            'shape': self.current_shape,
+            'generated_shape': path_cells,
+            'feedback': feedback,
+            'path_ratio': path_ratio,
+        }
+    
+    def submit_generation_drawing(self):
+        """Process the generated maze and move to the next."""
+        result = self.validate_generation()
+        
+        # Save the results
+        self.save_generation_results(result)
+        
+        # Get feedback message
+        feedback = result['feedback']
+        
+        if not result['valid']:
+            # If not valid, let the user try again
+            return save_numpy_array_as_image(self.generation_grid, is_generation=True), feedback, "generate", self.get_progress()
+        
+        # Mark this maze as completed with all tasks (solve, recognize, generate)
+        self.complete_current_maze()
+        
+        # Check if we should move to a new combination
+        has_next = self.select_next_combination()
+        
+        if not has_next:
+            # All combinations completed
+            return save_numpy_array_as_image(np.zeros((5, 5))), "Congratulations! You've completed all mazes in the experiment. Thank you for participating!", "complete", self.get_progress()
+            
+        # Load the next maze
+        img, msg, phase, progress = self.load_random_maze(self.current_size, self.current_shape)
+        progress_msg = f"Shape accepted! {feedback} Loading next maze..."
+        return img, progress_msg, "solve", progress
+    
+    def get_progress(self):
+        """Get the current progress information for the experiment.
+        
+        Returns:
+            A string with progress information.
+        """
+        total_completed = sum(self.combination_counts.values())
+        total_mazes = self.total_mazes
+        
+        completed_combinations = len(self.completed_combinations)
+        total_combinations = len(SIZES) * len(SHAPES)
+        
+        current_size = "None" if self.current_size is None else f"{self.current_size[0]}x{self.current_size[1]}"
+        current_shape = "None" if self.current_shape is None else self.current_shape
+        
+        if self.current_file_info:
+            combo_key = (self.current_file_info["size"], self.current_file_info["shape"])
+            completed_in_combo = self.combination_counts[combo_key]
+        else:
+            completed_in_combo = 0
+        
+        return f"Progress: {total_completed}/{total_mazes} mazes completed ({completed_combinations}/{total_combinations} combinations)"
+
+    def select_next_combination(self):
+        """Select the next size/shape combination that needs mazes."""
+        # If current combination is complete, find a new one
+        if self.current_size and self.current_shape:
+            key = f"{self.current_size[0]}x{self.current_size[1]}_{self.current_shape}"
+            if self.combination_counts.get(key, 0) >= self.mazes_per_combination:
+                # Current combination is complete, find a new one
+                for size in SIZES:
+                    for shape in SHAPES:
+                        check_key = f"{size[0]}x{size[1]}_{shape}"
+                        if self.combination_counts.get(check_key, 0) < self.mazes_per_combination:
+                            self.current_size = size
+                            self.current_shape = shape
+                            return True  # Found a new combination
+                
+                # All combinations are complete
+                return False
+        
+        # Current combination still has mazes to complete
+        return True
+
+    def complete_current_maze(self):
+        """Mark the current maze as completed."""
+        if self.current_size and self.current_shape:
+            key = f"{self.current_size[0]}x{self.current_size[1]}_{self.current_shape}"
+            self.combination_counts[key] = self.combination_counts.get(key, 0) + 1
+
+    def initial_load(self, size, shape):
+        """Initial load of a maze with the specified size and shape.
+        
+        Args:
+            size: Tuple of (height, width)
+            shape: String representing the maze shape
+            
+        Returns:
+            Tuple of (maze_image, message, phase, progress)
+        """
+        # Reset experiment state for a new start
+        self.maze_complete = False
+        self.moves = []
+        
+        # Load a random maze for the specified size and shape
+        return self.load_random_maze(size, shape)
+
+    def render_maze(self):
+        """Render the current maze as an image.
+        
+        Returns:
+            PIL Image of the current maze
+        """
+        if self.current_maze is None:
+            # Create a blank maze image
+            blank_maze = np.ones((5, 5), dtype=np.int8)
+            return save_numpy_array_as_image(blank_maze)
+            
+        return save_numpy_array_as_image(self.current_maze)
+
+    def save_generation_results(self, result):
+        """Save the generation results to a file.
+        
+        Args:
+            result: Dict with generation validation results
+        """
+        if not self.current_file_info:
+            return
+            
+        # Find the most recent result file for this maze
+        result_files = []
+        for root, _, files in os.walk(self.results_dir):
+            for file in files:
+                if file.startswith(self.participant_id) and file.endswith(".json"):
+                    file_path = os.path.join(root, file)
+                    result_files.append(file_path)
+        
+        # Sort by creation time and get the most recent
+        if result_files:
+            file_path = sorted(result_files, key=os.path.getmtime)[-1]
+            
+            try:
+                with open(file_path, 'r') as f:
+                    data = json.load(f)
+                
+                # Add the generation results
+                data["generation_result"] = result
+                
+                with open(file_path, 'w') as f:
+                    json.dump(data, f, indent=2)
+                    
+                print(f"Generation results saved to {file_path}")
+                
+            except Exception as e:
+                print(f"Error saving generation results: {e}")
+        else:
+            timestamp = int(time.time())
+            filename = f"{self.results_dir}/{self.participant_id}_{self.current_file_info['size']}_{self.current_shape}_generation_{timestamp}.json"
+            
+            # Prepare data to save
+            data = {
+                "participant_id": self.participant_id,
+                "maze_file": self.current_file_info['file'],
+                "maze_type": {
+                    "size": self.current_file_info['size'],
+                    "shape": self.current_shape
+                },
+                "generation_result": result,
+                "timestamp": timestamp
+            }
+            
+            # Save to file
+            with open(filename, 'w') as f:
+                json.dump(data, f, indent=2)
+                
+            print(f"Generation results saved to {filename}")
+
+    def get_generation_grid_image(self):
+        """Get an image of the current generation grid.
+        
+        Returns:
+            Image: PIL Image of the generation grid
+        """
+        if not hasattr(self, 'generation_grid') or self.generation_grid is None:
+            # Create the generation grid if it doesn't exist
+            return self.create_generation_grid()
+        
+        return save_numpy_array_as_image(self.generation_grid, is_generation=True)
+
+def create_interface(experiment):
+    """
+    Create the Gradio interface for the maze experiment.
+    
+    Args:
+        experiment: MazeExperiment instance.
+        
+    Returns:
+        gr.Interface: The Gradio interface for the experiment.
+    """
+    # Create the interface components
+    with gr.Blocks() as interface:
+        with gr.Row():
+            maze_display = gr.Image(label="Maze")
+            
+        with gr.Row():
+            message = gr.Textbox(label="Message", value="Loading maze...")
+            
+        with gr.Row():
+            phase_info = gr.Textbox(label="Current Phase", value="solve", visible=False)
+            progress_info = gr.Textbox(label="Progress", value="Progress: 0/120 mazes completed (0/12 combinations)")
+         
+        # Movement buttons - cardinal directions   
+        with gr.Row(visible=True) as movement_controls:
+            up_button = gr.Button("Up")
+            down_button = gr.Button("Down")
+            left_button = gr.Button("Left")
+            right_button = gr.Button("Right")
+        
+        # Movement buttons - diagonal directions
+        with gr.Row(visible=True) as diagonal_controls:
+            up_left_button = gr.Button("↖ Up-Left")
+            up_right_button = gr.Button("↗ Up-Right")
+            down_left_button = gr.Button("↙ Down-Left")
+            down_right_button = gr.Button("↘ Down-Right")
+            
+        # Shape recognition controls
+        with gr.Row(visible=False) as recognition_controls:
+            shape_input = gr.Dropdown(
+                choices=SHAPES,
+                label="Recognize Shape",
+                info="What shape does this maze represent?"
+            )
+            submit_recognition_button = gr.Button("Submit Recognition")
+            
+        # Generation controls
+        with gr.Row(visible=False) as generation_controls:
+            reset_gen_button = gr.Button("Reset")
+            submit_gen_button = gr.Button("Submit Generated Shape")
+            
+        # Define helper functions to route actions based on current phase
+        def handle_move(direction, phase):
+            if phase == "solve":
+                return experiment.process_move(direction)
+            elif phase == "generate":
+                img, msg = experiment.move_in_generation(direction)
+                return img, msg, phase, progress_info.value
+            else:
+                # For other phases, do nothing
+                return maze_display.value, message.value, phase, progress_info.value
+            
+        # Button click events - cardinal directions
+        up_button.click(
+            fn=handle_move,
+            inputs=[gr.Textbox(value="up", visible=False), phase_info],
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+        down_button.click(
+            fn=handle_move,
+            inputs=[gr.Textbox(value="down", visible=False), phase_info],
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+        left_button.click(
+            fn=handle_move,
+            inputs=[gr.Textbox(value="left", visible=False), phase_info],
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+        right_button.click(
+            fn=handle_move,
+            inputs=[gr.Textbox(value="right", visible=False), phase_info],
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+        
+        # Button click events - diagonal directions
+        up_left_button.click(
+            fn=handle_move,
+            inputs=[gr.Textbox(value="up-left", visible=False), phase_info],
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+        up_right_button.click(
+            fn=handle_move,
+            inputs=[gr.Textbox(value="up-right", visible=False), phase_info],
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+        down_left_button.click(
+            fn=handle_move,
+            inputs=[gr.Textbox(value="down-left", visible=False), phase_info],
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+        down_right_button.click(
+            fn=handle_move,
+            inputs=[gr.Textbox(value="down-right", visible=False), phase_info],
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+        
+        # Shape recognition event
+        submit_recognition_button.click(
+            fn=experiment.submit_shape_recognition,
+            inputs=shape_input,
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+        
+        # Generation events
+        def handle_reset_generation():
+            img, msg = experiment.reset_generation()
+            return img, msg, "generate", progress_info.value
+            
+        reset_gen_button.click(
+            fn=handle_reset_generation,
+            inputs=[],
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+            
+        submit_gen_button.click(
+            fn=experiment.submit_generation_drawing,
+            inputs=[],
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+        
+        # Phase change event handler
+        def handle_phase_change(phase):
+            if phase == "solve":
+                return {
+                    movement_controls: gr.update(visible=True),
+                    diagonal_controls: gr.update(visible=True),
+                    recognition_controls: gr.update(visible=False),
+                    generation_controls: gr.update(visible=False)
+                }
+            elif phase == "recognize":
+                return {
+                    movement_controls: gr.update(visible=False),
+                    diagonal_controls: gr.update(visible=False),
+                    recognition_controls: gr.update(visible=True),
+                    generation_controls: gr.update(visible=False)
+                }
+            elif phase == "generate":
+                return {
+                    movement_controls: gr.update(visible=True),
+                    diagonal_controls: gr.update(visible=True),
+                    recognition_controls: gr.update(visible=False),
+                    generation_controls: gr.update(visible=True)
+                }
+            else:
+                return {
+                    movement_controls: gr.update(visible=False),
+                    diagonal_controls: gr.update(visible=False),
+                    recognition_controls: gr.update(visible=False),
+                    generation_controls: gr.update(visible=False)
+                }
+                
+        phase_info.change(
+            fn=handle_phase_change,
+            inputs=phase_info,
+            outputs=[movement_controls, diagonal_controls, recognition_controls, generation_controls]
+        )
+
+        # Automatically start the experiment with the first maze
+        def auto_start():
+            # Default to first size and shape in the list
+            size = SIZES[0]
+            shape = SHAPES[0]
+            return experiment.initial_load(size, shape)
+        
+        # Start the experiment automatically when the interface loads
+        interface.load(
+            fn=auto_start,
+            inputs=None,
+            outputs=[maze_display, message, phase_info, progress_info]
+        )
+        
+    return interface
+
+if __name__ == "__main__":
+    experiment = MazeExperiment()
+    interface = create_interface(experiment)
+    interface.launch(share=True)
\ No newline at end of file
diff --git a/maze_generator.py b/maze_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..aae4390ec48cdfe0046662b07e64dd5e4ddb627f
--- /dev/null
+++ b/maze_generator.py
@@ -0,0 +1,347 @@
+import os
+import random
+import numpy as np
+from scipy.ndimage import zoom
+
+# IMPORTANT: paths are 1, walls are 0
+# Get all mazes of some size with paths of a given shape with:
+# `all_{shape}_path_mazes(size)`
+
+### SQUARES ###
+
+# Return a list of all minimal bounding box squares up to a certain size.
+def all_mbbox_squares(max_size):
+    all_squares = []
+    # Start with smallest possible square.
+    curr_square = np.array([[1, 1, 1], [1, 0, 1], [1, 1, 1]]) 
+    curr_size = curr_square.shape[0]
+    while curr_size <= max_size[0] and curr_size <= max_size[1]:
+        all_squares.append(curr_square)
+        curr_square = zoom(
+            curr_square,
+            ((curr_size + 1) / curr_size, (curr_size + 1) / curr_size),
+            order=0
+        )
+        curr_size = curr_square.shape[0]
+    return all_squares
+
+# Generate all square paths in a maze 
+def all_square_path_mazes(maze_size):
+    paths = all_mbbox_squares(maze_size)
+    return all_mazes_with_implanted_paths(maze_size, paths)
+
+### CROSSES ###
+
+def create_cross(size):
+    diag_1 = np.eye(size, dtype=int)
+    diag_2 = np.fliplr(np.eye(size, dtype=int))
+    return diag_1 | diag_2
+
+# Only allowing crosses with an uneven size to avoid a 2x2 block in the middle.
+def all_mbbox_crosses(max_size):
+    all_crosses = []
+    # Start with smallest possible cross.
+    curr_size = 3
+    curr_cross = create_cross(curr_size)
+    while curr_size <= max_size[0] and curr_size <= max_size[1]:
+        all_crosses.append(curr_cross)
+        curr_size += 2
+        curr_cross = create_cross(curr_size)
+    return all_crosses
+
+def all_cross_path_mazes(maze_size):
+    paths = all_mbbox_crosses(maze_size)
+    return all_mazes_with_implanted_paths(maze_size, paths)
+
+
+### SPIRAL ###
+
+# Fill a square grid with clockwise spiral from top left.
+def create_base_spiral(size):
+    spiral = np.zeros((size, size), dtype=int)
+
+    top, bottom = 0, size - 1
+    left, right = 0, size - 1
+    
+    while top <= bottom and left <= right:
+        # Fill the top row (left to right)
+        for i in range(max(left - 1, 0), right + 1):
+            spiral[top][i] = 1
+        top += 2
+        
+        # Fill the right column (top to bottom)
+        for i in range(top - 1, bottom + 1):
+            spiral[i][right] = 1
+        right -= 2
+        
+        if top <= bottom:
+            # Fill the bottom row (right to left)
+            for i in range(right + 1, left - 1, -1):
+                spiral[bottom][i] = 1
+            bottom -= 2
+        
+        if left <= right:
+            # Fill the left column (bottom to top)
+            for i in range(bottom + 1, top - 1, -1):
+                spiral[i][left] = 1
+            left += 2
+    
+    return spiral
+ 
+
+# Creates all square spirals going both clockwise and anticlockwise in all
+# four orientations.
+def all_mbbox_spirals(max_size):
+    all_spirals = []
+    curr_size = 5
+    while curr_size <= max_size[0] and curr_size <= max_size[1]:
+        cw_base_spiral = create_base_spiral(curr_size)
+        acw_base_spiral = np.fliplr(cw_base_spiral)
+        for k in range(4):
+            all_spirals.append(np.rot90(cw_base_spiral, k=k))
+            all_spirals.append(np.rot90(acw_base_spiral, k=k))
+        curr_size += 1
+    return all_spirals
+
+def all_spiral_path_mazes(maze_size):
+    paths = all_mbbox_spirals(maze_size)
+    return all_mazes_with_implanted_paths(maze_size, paths)
+
+### TRIANGLE ###
+
+# Create all triangles with base at bottom, pointing up.
+def all_mbbox_bottom_base_triangles(max_size):
+    all_triangles = []
+    # Smallest base bottom triangle.
+    curr_size = (3, 5)
+    while curr_size[0] <= max_size[0] and curr_size[1] <= max_size[1]:
+        triangle = np.zeros(curr_size, dtype=int)
+        # Top row has single 1 in the middle.
+        mid_point = curr_size[1] // 2
+        triangle[0, mid_point] = 1
+        # Bottom row is all 1's.
+        triangle[-1, :] = 1
+        # Intermediate rows have two 1's expanding out from top to bottom.
+        for i in range(1, curr_size[0] - 1):
+            triangle[i, mid_point - i] = 1
+            triangle[i, mid_point + i] = 1
+        all_triangles.append(triangle)
+        curr_size = (curr_size[0] + 1, curr_size[1] + 2)
+    return all_triangles
+
+# Create all triangles with diagonal bases for all 4 orientations.
+def all_mbbox_diag_base_traingles(max_size):
+    all_triangles = []
+    curr_size = 4
+    while curr_size <= max_size[0] and curr_size <= max_size[1]:
+        triangle = np.eye(curr_size, dtype=int)
+        triangle[:, 0] = 1
+        triangle[-1, :] = 1
+        for k in range(4):
+            all_triangles.append(np.rot90(triangle, k=k))
+        curr_size += 1
+    return all_triangles
+
+def all_triangle_path_mazes(maze_size):
+    paths = all_mbbox_bottom_base_triangles(maze_size)
+    paths.extend(all_mbbox_diag_base_traingles(maze_size))
+    return all_mazes_with_implanted_paths(maze_size, paths)
+
+
+### C ###
+
+def all_mbbox_Cs(max_size):
+    all_cs = []
+    # C where length = width.
+    curr_c = np.array([[1, 1, 1], [1, 0, 0], [1, 1, 1]]) 
+    curr_size = curr_c.shape[0]
+    while curr_size <= max_size[0] and curr_size <= max_size[1]:
+        all_cs.append(curr_c)
+        curr_c = zoom(
+            curr_c,
+            ((curr_size + 1) / curr_size, (curr_size + 1) / curr_size),
+            order=0
+        )
+        curr_size = curr_c.shape[0]
+    
+    # C where length > width.
+    curr_c = np.array([[1, 1, 1], [1, 0, 0], [1, 0, 0], [1, 1, 1]]) 
+    while curr_c.shape[0] <= max_size[0] and curr_c.shape[1] <= max_size[1]:
+        all_cs.append(curr_c)
+        curr_c = zoom(
+            curr_c,
+            ((curr_c.shape[0] + 1) / curr_c.shape[0], (curr_c.shape[1] + 1) / curr_c.shape[1]),
+            order=0
+        )
+
+    return all_cs
+
+def all_C_path_mazes(maze_size):
+    paths = all_mbbox_Cs(maze_size)
+    return all_mazes_with_implanted_paths(maze_size, paths)
+
+### U ###
+
+def all_mbbox_Us(max_size):
+    all_us = []
+    # U where length = width.
+    curr_u = np.array([[1, 0, 1], [1, 0, 1], [1, 1, 1]]) 
+    curr_size = curr_u.shape[0]
+    while curr_size <= max_size[0] and curr_size <= max_size[1]:
+        all_us.append(curr_u)
+        curr_u = zoom(
+            curr_u,
+            ((curr_size + 1) / curr_size, (curr_size + 1) / curr_size),
+            order=0
+        )
+        curr_size = curr_u.shape[0]
+    
+    # U where length > width.
+    curr_u = np.array([[1, 0, 1], [1, 0, 1], [1, 0, 1], [1, 1, 1]]) 
+    while curr_u.shape[0] <= max_size[0] and curr_u.shape[1] <= max_size[1]:
+        all_us.append(curr_u)
+        curr_u = zoom(
+            curr_u,
+            ((curr_u.shape[0] + 1) / curr_u.shape[0], (curr_u.shape[1] + 1) / curr_u.shape[1]),
+            order=0
+        )
+
+    return all_us
+
+def all_U_path_mazes(maze_size):
+    paths = all_mbbox_Us(maze_size)
+    return all_mazes_with_implanted_paths(maze_size, paths)
+
+### Z ###
+
+def create_Z(size):
+    z = np.fliplr(np.eye(size, dtype=int))
+    z[0, :] = 1
+    z[-1, :] = 1
+    return z
+
+def all_mbbox_Zs(max_size):
+    all_Zs = []
+    # Start with smallest possible Z.
+    curr_size = 4
+    curr_cross = create_Z(curr_size)
+    while curr_size <= max_size[0] and curr_size <= max_size[1]:
+        all_Zs.append(curr_cross)
+        curr_size += 1
+        curr_cross = create_Z(curr_size)
+    return all_Zs
+
+def all_Z_path_mazes(maze_size):
+    paths = all_mbbox_Zs(maze_size)
+    return all_mazes_with_implanted_paths(maze_size, paths)
+
+### N ###
+
+def all_mbbox_Ns(max_size):
+    return [np.rot90(z) for z in all_mbbox_Zs(max_size)]
+
+def all_N_path_mazes(maze_size):
+    paths = all_mbbox_Ns(maze_size)
+    return all_mazes_with_implanted_paths(maze_size, paths)
+
+### MAZES ###
+
+WALL = 0
+PATH = 1
+POS = 2
+END = 3
+
+MIN_DISTANCE = 2
+
+# Generate all mazes of a given size with each provided path placed into each
+# possible position of the maze.
+def all_mazes_with_implanted_paths(maze_size, paths):
+    all_mazes = []
+    for path in paths:
+        assert path.shape[0] <= maze_size[0] and path.shape[1] <= maze_size[1]
+        for i in range(0, maze_size[0] - path.shape[0] + 1):
+            for j in range(0, maze_size[1] - path.shape[1] + 1):
+                maze = np.zeros(maze_size, dtype=int)
+                maze[i: i + path.shape[0], j: j + path.shape[1]] = path
+                all_mazes.append(maze)
+    return all_mazes
+
+def distance(p1, p2):
+    return np.sqrt((p2[0] - p1[0]) ** 2 + (p2[1] - p1[1]) ** 2)
+
+def init_random_start_end(maze, k=1):
+    path_indices = np.argwhere(maze == PATH)
+    valid_pairs = []
+    for p1 in path_indices:
+        for p2 in path_indices:
+            if distance(tuple(p1), tuple(p2)) >= MIN_DISTANCE:
+                valid_pairs.append((tuple(p1), tuple(p2)))
+
+    random_start_ends = random.sample(valid_pairs, k=k)
+    random_mazes = []
+    for start, end in random_start_ends:
+        random_maze = np.copy(maze)
+        random_maze[start] = POS
+        random_maze[end] = END
+        random_mazes.append(random_maze)
+
+    if k == 1:
+        return random_mazes[0]
+
+    return random_mazes
+
+def init_all_start_end(maze):
+    path_indices = np.argwhere(maze == PATH)
+    valid_pairs = []
+    for p1 in path_indices:
+        for p2 in path_indices:
+            if tuple(p1) != tuple(p2):
+                valid_pairs.append((tuple(p1), tuple(p2)))
+    mazes = []
+    for start, end in valid_pairs:
+        path_maze = np.copy(maze)
+        path_maze[start] = POS
+        path_maze[end] = END
+        mazes.append(path_maze)
+
+    return mazes
+
+SHAPES = ["square", "cross", "spiral", "triangle", "C", "Z"]
+
+def get_sample_mazes(maze_size, shape, k):
+    if shape not in SHAPES:
+        raise NotImplemented("Shape not supported.")
+    all_mazes = globals()[f"all_{shape}_path_mazes"](maze_size)
+    sample_mazes = random.sample(all_mazes, k=k)
+    mazes_with_path = []
+    for maze in sample_mazes:
+        mazes_with_path.append(init_random_start_end(maze))
+    return mazes_with_path
+
+def generate_maze_experiments(size, shape):
+    size_shape_path = f"experiment_mazes/{str(size[0])}x{str(size[1])}/{shape}/"
+    if not os.path.isdir(size_shape_path):
+        os.makedirs(size_shape_path)
+    if size == (5, 5):
+        # Generate 5 samples of shape
+        sample_mazes = get_sample_mazes(size, shape, k=5)
+        for i, maze in enumerate(sample_mazes):
+            np.save(size_shape_path + f"{str(size[0])}x{str(size[1])}_{shape}_{str(i)}.npy", maze)
+    elif size == (7, 7):
+        # Generate 30 samples of shape
+        sample_mazes = get_sample_mazes(size, shape, k=30)
+        for i, maze in enumerate(sample_mazes):
+            np.save(size_shape_path + f"{str(size[0])}x{str(size[1])}_{shape}_{str(i)}.npy", maze)
+    elif size == (9, 9):
+        # Generate 50 samples of shape
+        sample_mazes = get_sample_mazes(size, shape, k=50)
+        for i, maze in enumerate(sample_mazes):
+            np.save(size_shape_path + f"{str(size[0])}x{str(size[1])}_{shape}_{str(i)}.npy", maze)
+
+
+'''
+if __name__ == "__main__":
+    for size in [(5, 5), (7, 7), (9, 9)]:
+        for shape in SHAPES:
+            generate_maze_experiments(size, shape)
+'''
\ No newline at end of file
diff --git a/prompt_builder.py b/prompt_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..e96c47de3dba94fb09498399694b27dd96765659
--- /dev/null
+++ b/prompt_builder.py
@@ -0,0 +1,173 @@
+import random
+import os
+
+import numpy as np
+
+from maze_generator import * 
+
+# Turn 2D maze array into a string representation as follows:
+# 011P 
+# 0100 
+# 011G 
+def encode_standard_matrix_maze(maze):
+
+    def symbol(x):
+        if x == WALL:
+            return "1"
+        elif x == PATH:
+            return "0"
+        elif x == POS:
+            return "P"
+        elif x == END:
+            return "G"
+
+    return "\n".join(" ".join(map(symbol, row)) for row in maze)
+
+# Turn 2D maze array into a string representation as follows:
+# @**S 
+# @*@@ 
+# @**E 
+def encode_unusual_matrix_maze(maze):
+
+    def symbol(x):
+        if x == WALL:
+            return "@"
+        elif x == PATH:
+            return "*"
+        elif x == POS:
+            return "P"
+        elif x == END:
+            return "G"
+
+    return "\n".join(" ".join(map(symbol, row)) for row in maze)
+
+def str_of_coordinates(indices):
+    return ", ".join(f"({p[0]}, {p[1]})" for p in indices)
+
+# Turn 2D maze array into a list of coordinates for the walls,
+# path, start, and end coordinates.
+def encode_coordinate_list_maze(maze):
+    walls_s = "Walls: " + str_of_coordinates(np.argwhere(maze == WALL))
+    paths_s = "Empty: "  + str_of_coordinates(np.argwhere(maze == PATH))
+    player_coord = np.argwhere(maze == POS)[0]
+    start_s = "Player position: " + f"({player_coord[0]}, {player_coord[1]})"
+    end_coord = np.argwhere(maze == END)[0]
+    end_s = "Goal: " + f"({end_coord[0]}, {end_coord[1]})"
+    return "\n".join((walls_s, paths_s, start_s, end_s))
+
+# Get stats on number of paths present for each shape in a given maze size.
+def print_num_paths(maze_size):
+    print("For a maze size of " + str(maze_size) + " there are:")
+
+    print(len(all_square_path_mazes(maze_size)), "squres")
+    print(len(all_cross_path_mazes(maze_size)), "crosses")
+    print(len(all_spiral_path_mazes(maze_size)), "spirals")
+    print(len(all_triangle_path_mazes(maze_size)), "triangles")
+    print(len(all_C_path_mazes(maze_size)), "Cs")
+    print(len(all_Z_path_mazes(maze_size)), "Zs")
+
+SHAPES = ["square", "cross", "spiral", "triangle", "C", "U", "Z", "N"]
+
+def get_sample_mazes(maze_size, shape, k):
+    if shape not in SHAPES:
+        raise NotImplemented("Shape not supported.")
+    all_mazes = globals()[f"all_{shape}_path_mazes"](maze_size)
+    sample_mazes = random.sample(all_mazes, k=k)
+    for maze in sample_mazes:
+        init_random_start_end(maze)
+    return sample_mazes
+
+STANDARD_PROMPT_INSTRUCTION = """You are tasked with solving a maze. The maze is a 2D grid with walls, empty spaces, a start point and an end point. The maze has the following encoding: 
+* Walls are represented by a '1'
+* Empty spaces are represented by a '0' 
+* The start point is represented by 'S'
+* The end point is represented by 'E' 
+
+Your task is to provide a step-by-step solution to move from the start point (S) to the end point (E), navigating only through empty spaces while avoiding walls. For reference, the coordinates of the top left corner of the maze are (0,0). You can move to any empty space that is adjacent to the current position: either up, down, left, right, or any diagonal.
+
+Please provide a step-by-step solution from the start point (S) to the end point (E) using coordinates to specify each location visited. Your solution should:
+1. Start at the coordinates of S
+2. End at the coordinates of E
+3. Only move through valid paths (empty spaces)
+
+Present your solution as a series of coordinates, showing each step of the path from start to end. Do not use any code execution. Check each time if there is a wall. If there is a wall, then don't proceed. Plan using language. Explain your reasoning. You are allowed to take up to 50 steps.
+
+Here is the maze you should solve:
+
+"""
+
+UNUSUAL_PROMPT_INSTRUCTION = """You are tasked with solving a maze. The maze is a 2D grid with walls, empty spaces, a start point and an end point. The maze has the following encoding: 
+* Walls are represented by a '@'
+* Empty spaces are represented by a '*' 
+* The start point is represented by 'S'
+* The end point is represented by 'E' 
+
+Your task is to provide a step-by-step solution to move from the start point (S) to the end point (E), navigating only through empty spaces while avoiding walls. For reference, the coordinates of the top left corner of the maze are (0,0). You can move to any empty space that is adjacent to the current position: either up, down, left, right, or any diagonal.
+
+Please provide a step-by-step solution from the start point (S) to the end point (E) using coordinates to specify each location visited. Your solution should:
+1. Start at the coordinates of S
+2. End at the coordinates of E
+3. Only move through valid paths (empty spaces)
+
+Present your solution as a series of coordinates, showing each step of the path from start to end. Do not use any code execution. Check each time if there is a wall. If there is a wall, then don't proceed. Plan using language. Explain your reasoning. You are allowed to take up to 50 steps.
+
+Here is the maze you should solve:
+
+"""
+
+COORDINATE_PROMPT_INSTRUCTION = """You are tasked with solving a maze. The maze is a 2D grid with walls, empty spaces, a start point and an end point. The maze will be provided as a list of coordinates denoting the walls, a list of coordinates denoting the empty spaces, and coordinates for the start point and end point. 
+
+Your task is to provide a step-by-step solution to move from the start point (S) to the end point (E), navigating only through empty spaces while avoiding walls. For reference, the coordinates of the top left corner of the maze are (0,0). You can move to any empty space that is adjacent to the current position: either up, down, left, right, or any diagonal.
+
+Please provide a step-by-step solution from the start point (S) to the end point (E) using coordinates to specify each location visited. Your solution should:
+1. Start at the coordinates of S
+2. End at the coordinates of E
+3. Only move through valid paths (empty spaces)
+
+Present your solution as a series of coordinates, showing each step of the path from start to end. Do not use any code execution. Check each time if there is a wall. If there is a wall, then don't proceed. Plan using language. Explain your reasoning. You are allowed to take up to 50 steps.
+
+Here is the maze you should solve:
+
+"""
+
+def generate_prompts(version, maze_size):
+    random.seed(10)
+    np.random.seed(10)
+
+    parent_dir = f"prompts/{version}/{maze_size[0]}x{maze_size[1]}/"
+    if not os.path.isdir(parent_dir):
+        os.makedirs(parent_dir)
+
+    for shape in SHAPES:
+        all_mazes = globals()[f"all_{shape}_path_mazes"](maze_size)
+        for i, maze in enumerate(all_mazes):
+            random_mazes = init_random_start_end(maze, k=3)
+            for j, random_maze in enumerate(random_mazes):
+                maze_name = f"{shape}_{i}_{j}.txt"
+
+                # Standard encoding.
+                maze_path = parent_dir + f"standard/{shape}/"
+                if not os.path.isdir(maze_path):
+                    os.makedirs(maze_path)
+                s_encoding = encode_standard_matrix_maze(random_maze)
+                s_prompt = STANDARD_PROMPT_INSTRUCTION + s_encoding
+                with open(maze_path + maze_name, "w") as file:
+                    file.write(s_prompt)
+
+                # Unusual encoding.
+                maze_path = parent_dir + f"unusual/{shape}/"
+                if not os.path.isdir(maze_path):
+                    os.makedirs(maze_path)
+                u_encoding = encode_unusual_matrix_maze(random_maze)
+                u_prompt = UNUSUAL_PROMPT_INSTRUCTION + u_encoding
+                with open(maze_path + maze_name, "w") as file:
+                    file.write(u_prompt)
+
+                # Coordinate list encoding. 
+                maze_path = parent_dir + f"coordinate/{shape}/"
+                if not os.path.isdir(maze_path):
+                    os.makedirs(maze_path)
+                c_encoding = encode_coordinate_list_maze(random_maze)
+                c_prompt = COORDINATE_PROMPT_INSTRUCTION + c_encoding
+                with open(maze_path + maze_name, "w") as file:
+                    file.write(c_prompt)
\ No newline at end of file
diff --git a/solution_verifier.py b/solution_verifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..8eda72c4237d361ad3a8a45b1be5afb181ae674f
--- /dev/null
+++ b/solution_verifier.py
@@ -0,0 +1,222 @@
+import re
+from pathlib import Path
+
+import numpy as np
+
+from maze_generator import *
+from prompt_builder import *
+
+def encode_maze(maze, encoding_type):
+    if encoding_type == "matrix":
+        return encode_standard_matrix_maze(maze)
+    elif encoding_type == "coord_list":
+        return encode_coordinate_list_maze(maze)
+    elif encoding_type == "vision":
+        return encode_standard_matrix_maze(maze)
+    else:
+        raise NotImplementedError
+
+# RECOGNIZE 
+def contains_word_case_insensitive(s, word_list):
+    word_list = [re.escape(word) for word in word_list]
+    pattern = r'\b(?:' + '|'.join(word_list) + r')\b'
+    return re.search(pattern, s, re.IGNORECASE) is not None
+
+def is_correct_recognize(response, shape):
+    if shape == 'square':
+        word_list = ['square', 'box', 'cube', 'symmetrical rectangle', 'right-angled quadrilateral', 'symmetric quadrilateral', "four-sided polygon"]
+    elif shape == 'triangle':
+        word_list = ['triangle', 'pyramid', 'trilateral', 'three-sided polygon', 'isosceles']
+    elif shape == 'spiral':
+        word_list = ['spiral', 'helix', 'coil', 'whorl', 'swirl', 'vortex']
+    elif shape == 'cross':
+        word_list = ['cross', 'times', 'multiplication', 'X', 'X-shape', 'crossed lines', 'crossing']
+    elif shape == 'C':
+        word_list = ['C', 'crescent', 'half-circle', 'curved line', 'semi-circle', 'semi circle', 'open curve']
+    elif shape == 'Z':
+        word_list = ['Z', 'zigzag', 'lightning bolt']
+
+    return contains_word_case_insensitive(response, word_list)
+
+# GENERATE
+
+def is_correct_generate(response, encoding_type, size, shape, orig_maze):
+    all_mazes = globals()[f"all_{shape}_path_mazes"](size)
+    all_mazes_and_paths = []
+    for maze in all_mazes:
+        all_mazes_and_paths.extend(init_all_start_end(maze))
+    all_maze_encodings = set()
+    for maze in all_mazes_and_paths:
+        all_maze_encodings.add(encode_maze(maze, encoding_type))
+    
+    all_maze_encodings.remove(encode_maze(orig_maze, encoding_type))
+
+    for maze_encoding in all_maze_encodings:
+        if maze_encoding in response:
+            return True
+    return False
+
+def is_correct_solution(response, maze):
+    # Find all coordinates that appear in the response.
+    pattern = r'\(\s*(-?\d+)\s*,\s*(-?\d+)\s*\)'
+    coordinates = re.findall(pattern, response)
+    coordinates = [(int(x), int(y)) for x, y in coordinates]
+
+    # As responses will usually talk about the solution before presenting
+    # the final form, we will start from the last provided end point 
+    # of the maze and work backwards until we arrive at the solution.
+    start = tuple(np.argwhere(maze == POS)[0])
+    end = tuple(np.argwhere(maze == END)[0])
+
+    try:
+        i = find_last_index(coordinates, end)
+    except ValueError:
+        return False
+
+    while i > 0:
+        curr_pos = coordinates[i]
+        if curr_pos == start:
+            return True
+        next_pos = coordinates[i - 1]
+        if next_pos in get_valid_moves(maze, curr_pos):
+            i = i - 1
+        else:
+            break
+    return False
+
+def find_last_index(lst, element):
+    return len(lst) - 1 - lst[::-1].index(element)
+
+def get_valid_moves(maze, pos):
+    x, y = pos
+    rows = len(maze)
+    cols = len(maze[0])
+    
+    # All 8 possible directions (up, down, left, right and diagonals)
+    directions = [
+        (-1, -1), (-1, 0), (-1, 1),
+        (0, -1),         (0, 1),
+        (1, -1), (1, 0), (1, 1) 
+    ]
+
+    valid_moves = []
+    for dx, dy in directions:
+        new_x, new_y = x + dx, y + dy
+        
+        # Check if the new coordinates are within bounds
+        if 0 <= new_x < rows and 0 <= new_y < cols:
+            if maze[new_x, new_y] != WALL:
+                valid_moves.append((new_x, new_y))
+    
+    return valid_moves
+
+def parse_coordinates(text):
+    walls = re.findall(r"Walls: ([\d, ()]+)", text)
+    empty = re.findall(r"Empty: ([\d, ()]+)", text)
+    start = re.findall(r"Start: \((\d+, \d+)\)", text)
+    end = re.findall(r"End: \((\d+, \d+)\)", text)
+
+    walls = [tuple(map(int, coord.split(', '))) for coord in re.findall(r"\((\d+, \d+)\)", walls[0])]
+    empty = [tuple(map(int, coord.split(', '))) for coord in re.findall(r"\((\d+, \d+)\)", empty[0])]
+    start = tuple(map(int, start[0].split(', ')))
+    end = tuple(map(int, end[0].split(', ')))
+
+    return walls, empty, start, end
+
+def parse_coordinate_maze(text, size):
+    walls, empty, start, end = parse_coordinates(text)
+    maze = np.full(size, -1)
+    for x, y in walls:
+        maze[x, y] = WALL
+    for x, y in empty:
+        maze[x, y] = PATH
+    maze[start[0], start[1]] = POS
+    maze[end[0], end[1]] = END
+    return maze
+
+def grade_prompts(version):
+    parent_dir = Path(f"results/{version}/")
+    for model in parent_dir.iterdir():
+        grade_info = ""
+        total = 0
+        total_correct = 0
+        total_size = {size_t.name: 0 for size_t in (parent_dir / model.name).iterdir() if not size_t.is_file()}
+        total_size_correct = {size_t.name: 0 for size_t in (parent_dir / model.name).iterdir() if not size_t.is_file()}
+        total_encoding = {"standard": 0, "unusual": 0, "coordinate": 0}
+        total_encoding_correct = {"standard": 0, "unusual": 0, "coordinate": 0}
+        total_shape = {shape: 0 for shape in SHAPES}
+        total_shape_correct = {shape: 0 for shape in SHAPES}
+        for size_t in (parent_dir / model.name).iterdir():
+            if size_t.is_file():
+                continue
+            size = (int(size_t.name[0]), int(size_t.name[2]))
+            for encoding in (parent_dir / model.name / size_t.name).iterdir():
+                for shape in (parent_dir / model.name / size_t.name / encoding.name).iterdir():
+                    for response_file in (parent_dir / model.name / size_t.name / encoding.name / shape.name).iterdir():
+                        response_text = response_file.read_text()
+                        # Get coordinate version of maze given in prompt so we can decode.
+                        coord_prompt_text = Path(f"prompts/v1/diag/{size_t.name}/coordinate/{shape.name}/{response_file.name}").read_text()
+                        maze = parse_coordinate_maze(coord_prompt_text, size)
+                        is_correct = is_correct_solution(response_text, maze)
+
+                        grade_info += f"{size_t.name}-{encoding.name}: {response_file.name}: {is_correct}\n"
+
+                        total += 1
+                        total_correct += is_correct
+                        total_size[size_t.name] += 1
+                        total_size_correct[size_t.name] += is_correct
+                        total_encoding[encoding.name] += 1
+                        total_encoding_correct[encoding.name] += is_correct
+                        total_shape[shape.name] += 1
+                        total_shape_correct[shape.name] += is_correct
+        
+        grade_info += "\n\n### SUMMARY ###\n"
+        grade_info += f"Total: {total_correct}/{total}\n\n"
+        grade_info += "Following show total correct for one variable across all others:\n"
+        grade_info += "# SIZES #\n"
+        for size, correct in total_size_correct.items():
+            grade_info += f"{size}: {correct}/{total_size[size]}\n"
+        grade_info += "# ENCODING #\n"
+        for encoding, correct in total_encoding_correct.items():
+            grade_info += f"{encoding}: {correct}/{total_encoding[encoding]}\n"
+        grade_info += "# SHAPE #\n"
+        for shape, correct in total_shape_correct.items():
+            grade_info += f"{shape}: {correct}/{total_shape[shape]}\n"
+
+        (parent_dir / model.name / "grade_info.txt").write_text(grade_info)
+
+        print(f"{model.name} - total: {total_correct}/{total}")
+        import csv
+
+        # Define the path to the CSV file
+        csv_path = parent_dir / model.name / "grade_info.csv"
+
+        # Prepare the data for the CSV
+        csv_data = [
+            [model.name, "Total Correct", "Total"],
+            ["Summary", total_correct, total],
+        ]
+
+        # Add size data to csv_data
+        csv_data.append(["SIZES", "Correct", "Total"])
+        for size, correct in total_size_correct.items():
+            csv_data.append([size, correct, total_size[size]])
+
+        # Add encoding data to csv_data
+        csv_data.append(["ENCODING", "Correct", "Total"])
+        for encoding, correct in total_encoding_correct.items():
+            csv_data.append([encoding, correct, total_encoding[encoding]])
+
+        # Add shape data to csv_data
+        csv_data.append(["SHAPE", "Correct", "Total"])
+        for shape, correct in total_shape_correct.items():
+            csv_data.append([shape, correct, total_shape[shape]])
+
+        # Write the csv_data to the CSV file
+        with open(csv_path, mode='w', newline='') as file:
+            writer = csv.writer(file)
+            writer.writerows(csv_data)
+        print(f"CSV file saved at {csv_path}")
+
+if __name__ == "__main__":
+    grade_prompts("only_one_per_folder")
\ No newline at end of file