diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..b74b8e0cd7687ff5c70132e875c9811412af0e7b --- /dev/null +++ b/.gitignore @@ -0,0 +1,27 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +.venv/ +venv/ + +# Node +node_modules/ +.npm/ + +# Build +dist/ +build/ + +# IDE +.vscode/ +.idea/ + +# OS +.DS_Store + +# Logs +*.log + +# Environment +.env diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..f9ed618905ff8cfc51867a031012fb24adba7689 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,43 @@ +# Simple Hugging Face Spaces Dockerfile +FROM node:20-slim + +ENV DEBIAN_FRONTEND=noninteractive + +# Install Python +RUN apt-get update && apt-get install -y --no-install-recommends \ + python3 \ + python3-pip \ + python3-venv \ + && rm -rf /var/lib/apt/lists/* + +# Create user with UID 1000 (required by HF Spaces) +RUN useradd -m -u 1000 user + +WORKDIR /app + +# Copy and build frontend +COPY --chown=user frontend/package*.json ./frontend/ +RUN cd frontend && npm ci +COPY --chown=user frontend/ ./frontend/ +RUN cd frontend && npm run build + +# Copy and install backend +COPY --chown=user backend/ ./backend/ +RUN pip3 install --no-cache-dir --break-system-packages \ + fastapi uvicorn pydantic pydantic-settings \ + httpx psutil python-dotenv websockets + +# Switch to non-root user +USER user + +ENV HOME=/home/user \ + PYTHONPATH=/app/backend \ + PYTHONUNBUFFERED=1 + +EXPOSE 7860 + +# Simple startup script +COPY --chown=user start.sh /app/ +RUN chmod +x /app/start.sh + +CMD ["/app/start.sh"] diff --git a/README.md b/README.md index 3bdc0b41bfe753fa97684b546f745f81114d517b..d7a3a4ce78703ba86a53f001aed8a62486d1247c 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,26 @@ --- -title: SearchAlgorithms -emoji: ⚡ -colorFrom: gray -colorTo: gray +title: Grid Search Pathfinding +emoji: 🗺️ +colorFrom: blue +colorTo: green sdk: docker +app_port: 7860 pinned: false --- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# Grid Search - Pathfinding Algorithms Visualization + +An interactive web application to visualize and compare various pathfinding algorithms on a grid-based map. + +## Features + +- **Multiple Algorithms**: A*, BFS, DFS, Greedy Best-First, UCS, IDS +- **Real-time Visualization**: Watch algorithms explore the grid step by step +- **Algorithm Comparison**: Compare performance metrics across different algorithms +- **Customizable Grid**: Generate grids with obstacles, tunnels, and delivery points + +## Tech Stack + +- **Frontend**: React + TypeScript + Vite + TailwindCSS +- **Backend**: FastAPI + Python +- **Visualization**: Real-time WebSocket updates diff --git a/backend/.dockerignore b/backend/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..92e68940d24bc52f92b42701d40133fe3ea022f9 --- /dev/null +++ b/backend/.dockerignore @@ -0,0 +1,46 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +.venv/ +venv/ +ENV/ +env/ + +# Testing +.pytest_cache/ +.coverage +htmlcov/ +.tox/ +.nox/ + +# IDE +.vscode/ +.idea/ +*.swp +*.swo + +# Build +*.egg-info/ +dist/ +build/ +eggs/ +.eggs/ + +# OS +.DS_Store +Thumbs.db + +# Logs +*.log + +# Environment +.env +.env.* +!.env.example + +# UV +.uv/ +uv.lock diff --git a/backend/.python-version b/backend/.python-version new file mode 100644 index 0000000000000000000000000000000000000000..c8cfe3959183f8e9a50f83f54cd723f2dc9c252d --- /dev/null +++ b/backend/.python-version @@ -0,0 +1 @@ +3.10 diff --git a/backend/app/__init__.py b/backend/app/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/backend/app/algorithms/__init__.py b/backend/app/algorithms/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..87da80414f1ccff549e65b84679eb7cb6c622eef --- /dev/null +++ b/backend/app/algorithms/__init__.py @@ -0,0 +1,22 @@ +"""Search algorithms package.""" +from .bfs import bfs_search, bfs_search_generator +from .dfs import dfs_search, dfs_search_generator +from .ids import ids_search, ids_search_generator +from .ucs import ucs_search, ucs_search_generator +from .greedy import greedy_search, greedy_search_generator +from .astar import astar_search, astar_search_generator + +__all__ = [ + "bfs_search", + "bfs_search_generator", + "dfs_search", + "dfs_search_generator", + "ids_search", + "ids_search_generator", + "ucs_search", + "ucs_search_generator", + "greedy_search", + "greedy_search_generator", + "astar_search", + "astar_search_generator", +] diff --git a/backend/app/algorithms/astar.py b/backend/app/algorithms/astar.py new file mode 100644 index 0000000000000000000000000000000000000000..b78be450646b7cc9564193aef8a9ca1678945fdc --- /dev/null +++ b/backend/app/algorithms/astar.py @@ -0,0 +1,186 @@ +"""A* Search algorithm.""" +from typing import Tuple, Optional, List, Generator, Callable, TYPE_CHECKING + +if TYPE_CHECKING: + from ..core.generic_search import GenericSearch + +from ..core.node import SearchNode +from ..core.frontier import PriorityQueueFrontier +from ..models.state import PathResult, SearchStep + + +def astar_search( + problem: 'GenericSearch', + heuristic: Callable[[Tuple[int, int], Tuple[int, int]], float], + visualize: bool = False +) -> Tuple[PathResult, Optional[List[SearchStep]]]: + """ + A* search using f(n) = g(n) + h(n). + + Optimal if heuristic is admissible (never overestimates). + Complete if step costs are positive. + + Args: + problem: The search problem to solve + heuristic: Function(state, goal) -> estimated cost to goal + visualize: If True, collect visualization steps + + Returns: + Tuple of (PathResult, Optional[List[SearchStep]]) + """ + frontier = PriorityQueueFrontier() + start = problem.initial_state() + + # Get goal for heuristic calculation + goal = getattr(problem, 'goal', None) + + h_value = heuristic(start, goal) if goal else 0 + f_value = 0 + h_value # g(n) + h(n) + start_node = SearchNode(state=start, path_cost=0, depth=0, priority=f_value) + frontier.push(start_node) + + explored: set = set() + nodes_expanded = 0 + steps: List[SearchStep] = [] if visualize else None + + while not frontier.is_empty(): + node = frontier.pop() + + # Record step for visualization + if visualize: + steps.append(SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + )) + + # Goal test + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ), steps + + # Skip if already explored + if node.state in explored: + continue + + explored.add(node.state) + nodes_expanded += 1 + + # Expand node + for action in problem.actions(node.state): + child_state = problem.result(node.state, action) + + if child_state not in explored: + step_cost = problem.step_cost(node.state, action, child_state) + g_value = node.path_cost + step_cost + h_value = heuristic(child_state, goal) if goal else 0 + f_value = g_value + h_value + + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=g_value, + depth=node.depth + 1, + priority=f_value # Priority = f(n) = g(n) + h(n) + ) + frontier.push(child) + + # No solution found + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ), steps + + +def astar_search_generator( + problem: 'GenericSearch', + heuristic: Callable[[Tuple[int, int], Tuple[int, int]], float] +) -> Generator[SearchStep, None, PathResult]: + """ + Generator version of A* search that yields steps during execution. + + Args: + problem: The search problem to solve + heuristic: Heuristic function + + Yields: + SearchStep objects + + Returns: + Final PathResult + """ + frontier = PriorityQueueFrontier() + start = problem.initial_state() + goal = getattr(problem, 'goal', None) + + h_value = heuristic(start, goal) if goal else 0 + f_value = 0 + h_value + start_node = SearchNode(state=start, path_cost=0, depth=0, priority=f_value) + frontier.push(start_node) + + explored: set = set() + nodes_expanded = 0 + + while not frontier.is_empty(): + node = frontier.pop() + + yield SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + ) + + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ) + + if node.state in explored: + continue + + explored.add(node.state) + nodes_expanded += 1 + + for action in problem.actions(node.state): + child_state = problem.result(node.state, action) + + if child_state not in explored: + step_cost = problem.step_cost(node.state, action, child_state) + g_value = node.path_cost + step_cost + h_value = heuristic(child_state, goal) if goal else 0 + f_value = g_value + h_value + + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=g_value, + depth=node.depth + 1, + priority=f_value + ) + frontier.push(child) + + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ) diff --git a/backend/app/algorithms/bfs.py b/backend/app/algorithms/bfs.py new file mode 100644 index 0000000000000000000000000000000000000000..91600f6cebd0aa1d3b847f6f0aa75b052d256e43 --- /dev/null +++ b/backend/app/algorithms/bfs.py @@ -0,0 +1,160 @@ +"""Breadth-First Search algorithm.""" +from typing import Tuple, Optional, List, Generator, TYPE_CHECKING + +if TYPE_CHECKING: + from ..core.generic_search import GenericSearch + +from ..core.node import SearchNode +from ..core.frontier import QueueFrontier +from ..models.state import PathResult, SearchStep + + +def bfs_search( + problem: 'GenericSearch', + visualize: bool = False +) -> Tuple[PathResult, Optional[List[SearchStep]]]: + """ + Breadth-first search using FIFO queue. + + Finds path with minimum number of steps (not minimum cost). + Complete and optimal for unweighted graphs. + + Args: + problem: The search problem to solve + visualize: If True, collect visualization steps + + Returns: + Tuple of (PathResult, Optional[List[SearchStep]]) + """ + frontier = QueueFrontier() + start = problem.initial_state() + start_node = SearchNode(state=start, path_cost=0, depth=0) + frontier.push(start_node) + + explored: set = set() + nodes_expanded = 0 + steps: List[SearchStep] = [] if visualize else None + + while not frontier.is_empty(): + node = frontier.pop() + + # Record step for visualization + if visualize: + steps.append(SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + )) + + # Goal test after pop (standard BFS) + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ), steps + + # Skip if already explored + if node.state in explored: + continue + + explored.add(node.state) + nodes_expanded += 1 + + # Expand node + for action in problem.actions(node.state): + child_state = problem.result(node.state, action) + if child_state not in explored and not frontier.contains_state(child_state): + step_cost = problem.step_cost(node.state, action, child_state) + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=node.path_cost + step_cost, + depth=node.depth + 1 + ) + frontier.push(child) + + # No solution found + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ), steps + + +def bfs_search_generator( + problem: 'GenericSearch' +) -> Generator[SearchStep, None, PathResult]: + """ + Generator version of BFS that yields steps during execution. + + Args: + problem: The search problem to solve + + Yields: + SearchStep objects + + Returns: + Final PathResult + """ + frontier = QueueFrontier() + start = problem.initial_state() + start_node = SearchNode(state=start, path_cost=0, depth=0) + frontier.push(start_node) + + explored: set = set() + nodes_expanded = 0 + + while not frontier.is_empty(): + node = frontier.pop() + + yield SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + ) + + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ) + + if node.state in explored: + continue + + explored.add(node.state) + nodes_expanded += 1 + + for action in problem.actions(node.state): + child_state = problem.result(node.state, action) + if child_state not in explored and not frontier.contains_state(child_state): + step_cost = problem.step_cost(node.state, action, child_state) + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=node.path_cost + step_cost, + depth=node.depth + 1 + ) + frontier.push(child) + + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ) diff --git a/backend/app/algorithms/dfs.py b/backend/app/algorithms/dfs.py new file mode 100644 index 0000000000000000000000000000000000000000..5d79e705f5cc73c19b944e586cc19925ab1aaf50 --- /dev/null +++ b/backend/app/algorithms/dfs.py @@ -0,0 +1,162 @@ +"""Depth-First Search algorithm.""" +from typing import Tuple, Optional, List, Generator, TYPE_CHECKING + +if TYPE_CHECKING: + from ..core.generic_search import GenericSearch + +from ..core.node import SearchNode +from ..core.frontier import StackFrontier +from ..models.state import PathResult, SearchStep + + +def dfs_search( + problem: 'GenericSearch', + visualize: bool = False +) -> Tuple[PathResult, Optional[List[SearchStep]]]: + """ + Depth-first search using LIFO stack. + + Not guaranteed to find optimal solution. + Complete in finite state spaces with cycle detection. + + Args: + problem: The search problem to solve + visualize: If True, collect visualization steps + + Returns: + Tuple of (PathResult, Optional[List[SearchStep]]) + """ + frontier = StackFrontier() + start = problem.initial_state() + start_node = SearchNode(state=start, path_cost=0, depth=0) + frontier.push(start_node) + + explored: set = set() + nodes_expanded = 0 + steps: List[SearchStep] = [] if visualize else None + + while not frontier.is_empty(): + node = frontier.pop() + + # Record step for visualization + if visualize: + steps.append(SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + )) + + # Goal test + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ), steps + + # Skip if already explored + if node.state in explored: + continue + + explored.add(node.state) + nodes_expanded += 1 + + # Expand node (reverse order so first action is processed last -> depth-first) + actions = problem.actions(node.state) + for action in reversed(actions): + child_state = problem.result(node.state, action) + if child_state not in explored and not frontier.contains_state(child_state): + step_cost = problem.step_cost(node.state, action, child_state) + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=node.path_cost + step_cost, + depth=node.depth + 1 + ) + frontier.push(child) + + # No solution found + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ), steps + + +def dfs_search_generator( + problem: 'GenericSearch' +) -> Generator[SearchStep, None, PathResult]: + """ + Generator version of DFS that yields steps during execution. + + Args: + problem: The search problem to solve + + Yields: + SearchStep objects + + Returns: + Final PathResult + """ + frontier = StackFrontier() + start = problem.initial_state() + start_node = SearchNode(state=start, path_cost=0, depth=0) + frontier.push(start_node) + + explored: set = set() + nodes_expanded = 0 + + while not frontier.is_empty(): + node = frontier.pop() + + yield SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + ) + + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ) + + if node.state in explored: + continue + + explored.add(node.state) + nodes_expanded += 1 + + actions = problem.actions(node.state) + for action in reversed(actions): + child_state = problem.result(node.state, action) + if child_state not in explored and not frontier.contains_state(child_state): + step_cost = problem.step_cost(node.state, action, child_state) + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=node.path_cost + step_cost, + depth=node.depth + 1 + ) + frontier.push(child) + + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ) diff --git a/backend/app/algorithms/greedy.py b/backend/app/algorithms/greedy.py new file mode 100644 index 0000000000000000000000000000000000000000..6b4bbc50e295b8a45a7721dd80ea36a917746e48 --- /dev/null +++ b/backend/app/algorithms/greedy.py @@ -0,0 +1,181 @@ +"""Greedy Best-First Search algorithm.""" +from typing import Tuple, Optional, List, Generator, Callable, TYPE_CHECKING + +if TYPE_CHECKING: + from ..core.generic_search import GenericSearch + +from ..core.node import SearchNode +from ..core.frontier import PriorityQueueFrontier +from ..models.state import PathResult, SearchStep + + +def greedy_search( + problem: 'GenericSearch', + heuristic: Callable[[Tuple[int, int], Tuple[int, int]], float], + visualize: bool = False +) -> Tuple[PathResult, Optional[List[SearchStep]]]: + """ + Greedy best-first search using heuristic only. + + Expands node that appears closest to goal. + Not guaranteed to find optimal solution. + Not complete in general. + + Args: + problem: The search problem to solve + heuristic: Function(state, goal) -> estimated cost to goal + visualize: If True, collect visualization steps + + Returns: + Tuple of (PathResult, Optional[List[SearchStep]]) + """ + frontier = PriorityQueueFrontier() + start = problem.initial_state() + + # Get goal for heuristic calculation (assume single goal) + goal = getattr(problem, 'goal', None) + + h_value = heuristic(start, goal) if goal else 0 + start_node = SearchNode(state=start, path_cost=0, depth=0, priority=h_value) + frontier.push(start_node) + + explored: set = set() + nodes_expanded = 0 + steps: List[SearchStep] = [] if visualize else None + + while not frontier.is_empty(): + node = frontier.pop() + + # Record step for visualization + if visualize: + steps.append(SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + )) + + # Goal test + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ), steps + + # Skip if already explored + if node.state in explored: + continue + + explored.add(node.state) + nodes_expanded += 1 + + # Expand node + for action in problem.actions(node.state): + child_state = problem.result(node.state, action) + + if child_state not in explored: + step_cost = problem.step_cost(node.state, action, child_state) + h_value = heuristic(child_state, goal) if goal else 0 + + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=node.path_cost + step_cost, + depth=node.depth + 1, + priority=h_value # Priority = h(n) only for Greedy + ) + frontier.push(child) + + # No solution found + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ), steps + + +def greedy_search_generator( + problem: 'GenericSearch', + heuristic: Callable[[Tuple[int, int], Tuple[int, int]], float] +) -> Generator[SearchStep, None, PathResult]: + """ + Generator version of Greedy search that yields steps during execution. + + Args: + problem: The search problem to solve + heuristic: Heuristic function + + Yields: + SearchStep objects + + Returns: + Final PathResult + """ + frontier = PriorityQueueFrontier() + start = problem.initial_state() + goal = getattr(problem, 'goal', None) + + h_value = heuristic(start, goal) if goal else 0 + start_node = SearchNode(state=start, path_cost=0, depth=0, priority=h_value) + frontier.push(start_node) + + explored: set = set() + nodes_expanded = 0 + + while not frontier.is_empty(): + node = frontier.pop() + + yield SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + ) + + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ) + + if node.state in explored: + continue + + explored.add(node.state) + nodes_expanded += 1 + + for action in problem.actions(node.state): + child_state = problem.result(node.state, action) + + if child_state not in explored: + step_cost = problem.step_cost(node.state, action, child_state) + h_value = heuristic(child_state, goal) if goal else 0 + + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=node.path_cost + step_cost, + depth=node.depth + 1, + priority=h_value + ) + frontier.push(child) + + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ) diff --git a/backend/app/algorithms/ids.py b/backend/app/algorithms/ids.py new file mode 100644 index 0000000000000000000000000000000000000000..fd4eb7d75581c4a5e08138cdb0c1766f06475480 --- /dev/null +++ b/backend/app/algorithms/ids.py @@ -0,0 +1,255 @@ +"""Iterative Deepening Search algorithm.""" +from typing import Tuple, Optional, List, Generator, TYPE_CHECKING + +if TYPE_CHECKING: + from ..core.generic_search import GenericSearch + +from ..core.node import SearchNode +from ..models.state import PathResult, SearchStep + + +# Sentinel values for DLS results +CUTOFF = "cutoff" +FAILURE = "failure" + + +def depth_limited_search( + problem: 'GenericSearch', + limit: int, + visualize: bool = False, + steps: Optional[List[SearchStep]] = None, + base_expanded: int = 0 +) -> Tuple[Optional[PathResult], str, int, Optional[List[SearchStep]]]: + """ + Depth-limited search - DFS with depth limit. + + Args: + problem: The search problem + limit: Maximum depth to search + visualize: If True, collect visualization steps + steps: Existing steps list to append to + base_expanded: Starting count for nodes expanded + + Returns: + Tuple of (PathResult or None, status, nodes_expanded, steps) + status is CUTOFF if limit reached, FAILURE if no solution exists + """ + start = problem.initial_state() + start_node = SearchNode(state=start, path_cost=0, depth=0) + + return _recursive_dls( + problem, start_node, limit, set(), visualize, + steps if steps is not None else ([] if visualize else None), + base_expanded + ) + + +def _recursive_dls( + problem: 'GenericSearch', + node: SearchNode, + limit: int, + explored: set, + visualize: bool, + steps: Optional[List[SearchStep]], + nodes_expanded: int +) -> Tuple[Optional[PathResult], str, int, Optional[List[SearchStep]]]: + """Recursive helper for depth-limited search.""" + + # Record step for visualization + if visualize and steps is not None: + steps.append(SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=[], # DLS doesn't maintain explicit frontier + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + )) + + # Goal test + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ), "success", nodes_expanded, steps + + # Depth limit reached + if node.depth >= limit: + return None, CUTOFF, nodes_expanded, steps + + # Mark as explored for this path + explored.add(node.state) + nodes_expanded += 1 + + cutoff_occurred = False + + # Expand node + for action in problem.actions(node.state): + child_state = problem.result(node.state, action) + + if child_state not in explored: + step_cost = problem.step_cost(node.state, action, child_state) + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=node.path_cost + step_cost, + depth=node.depth + 1 + ) + + result, status, nodes_expanded, steps = _recursive_dls( + problem, child, limit, explored.copy(), visualize, steps, nodes_expanded + ) + + if status == "success": + return result, status, nodes_expanded, steps + elif status == CUTOFF: + cutoff_occurred = True + + if cutoff_occurred: + return None, CUTOFF, nodes_expanded, steps + else: + return None, FAILURE, nodes_expanded, steps + + +def ids_search( + problem: 'GenericSearch', + visualize: bool = False, + max_depth: int = 1000 +) -> Tuple[PathResult, Optional[List[SearchStep]]]: + """ + Iterative deepening search - repeated DLS with increasing depth. + + Combines BFS's completeness and optimality (for unweighted) + with DFS's space efficiency. + + Args: + problem: The search problem to solve + visualize: If True, collect visualization steps + max_depth: Maximum depth to search (prevents infinite loops) + + Returns: + Tuple of (PathResult, Optional[List[SearchStep]]) + """ + total_expanded = 0 + all_steps: List[SearchStep] = [] if visualize else None + + for depth in range(max_depth): + result, status, expanded, steps = depth_limited_search( + problem, depth, visualize, all_steps, total_expanded + ) + total_expanded = expanded + + if visualize and steps: + all_steps = steps + + if status == "success" and result is not None: + result.nodes_expanded = total_expanded + return result, all_steps + elif status == FAILURE: + # No solution exists + break + + # No solution found within max_depth + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=total_expanded, + path=[] + ), all_steps + + +def ids_search_generator( + problem: 'GenericSearch', + max_depth: int = 1000 +) -> Generator[SearchStep, None, PathResult]: + """ + Generator version of IDS that yields steps during execution. + + Args: + problem: The search problem to solve + max_depth: Maximum depth to search + + Yields: + SearchStep objects + + Returns: + Final PathResult + """ + total_expanded = 0 + + for depth in range(max_depth): + # Run DLS and yield steps + for step in _dls_generator(problem, depth, total_expanded): + yield step + total_expanded = step.step_number + + # Check if solution was found at this depth + result, status, expanded, _ = depth_limited_search( + problem, depth, False, None, total_expanded + ) + total_expanded = expanded + + if status == "success" and result is not None: + result.nodes_expanded = total_expanded + return result + elif status == FAILURE: + break + + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=total_expanded, + path=[] + ) + + +def _dls_generator( + problem: 'GenericSearch', + limit: int, + base_expanded: int +) -> Generator[SearchStep, None, None]: + """Generator helper for DLS.""" + start = problem.initial_state() + start_node = SearchNode(state=start, path_cost=0, depth=0) + + stack = [(start_node, set())] + nodes_expanded = base_expanded + + while stack: + node, explored = stack.pop() + + yield SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=[n.state for n, _ in stack], + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + ) + + if problem.goal_test(node.state): + return + + if node.depth >= limit: + continue + + explored = explored | {node.state} + nodes_expanded += 1 + + for action in reversed(problem.actions(node.state)): + child_state = problem.result(node.state, action) + if child_state not in explored: + step_cost = problem.step_cost(node.state, action, child_state) + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=node.path_cost + step_cost, + depth=node.depth + 1 + ) + stack.append((child, explored)) diff --git a/backend/app/algorithms/ucs.py b/backend/app/algorithms/ucs.py new file mode 100644 index 0000000000000000000000000000000000000000..5b183b79b05c28360af98d96de642288dfcf6f54 --- /dev/null +++ b/backend/app/algorithms/ucs.py @@ -0,0 +1,166 @@ +"""Uniform Cost Search algorithm.""" +from typing import Tuple, Optional, List, Generator, TYPE_CHECKING + +if TYPE_CHECKING: + from ..core.generic_search import GenericSearch + +from ..core.node import SearchNode +from ..core.frontier import PriorityQueueFrontier +from ..models.state import PathResult, SearchStep + + +def ucs_search( + problem: 'GenericSearch', + visualize: bool = False +) -> Tuple[PathResult, Optional[List[SearchStep]]]: + """ + Uniform Cost Search using priority queue ordered by path cost. + + Always finds the optimal (minimum cost) solution. + Complete if step costs are positive. + + Args: + problem: The search problem to solve + visualize: If True, collect visualization steps + + Returns: + Tuple of (PathResult, Optional[List[SearchStep]]) + """ + frontier = PriorityQueueFrontier() + start = problem.initial_state() + start_node = SearchNode(state=start, path_cost=0, depth=0, priority=0) + frontier.push(start_node) + + explored: set = set() + nodes_expanded = 0 + steps: List[SearchStep] = [] if visualize else None + + while not frontier.is_empty(): + node = frontier.pop() + + # Record step for visualization + if visualize: + steps.append(SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + )) + + # Goal test (after pop for UCS) + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ), steps + + # Skip if already explored + if node.state in explored: + continue + + explored.add(node.state) + nodes_expanded += 1 + + # Expand node + for action in problem.actions(node.state): + child_state = problem.result(node.state, action) + step_cost = problem.step_cost(node.state, action, child_state) + new_cost = node.path_cost + step_cost + + if child_state not in explored: + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=new_cost, + depth=node.depth + 1, + priority=new_cost # Priority = path cost for UCS + ) + frontier.push(child) + + # No solution found + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ), steps + + +def ucs_search_generator( + problem: 'GenericSearch' +) -> Generator[SearchStep, None, PathResult]: + """ + Generator version of UCS that yields steps during execution. + + Args: + problem: The search problem to solve + + Yields: + SearchStep objects + + Returns: + Final PathResult + """ + frontier = PriorityQueueFrontier() + start = problem.initial_state() + start_node = SearchNode(state=start, path_cost=0, depth=0, priority=0) + frontier.push(start_node) + + explored: set = set() + nodes_expanded = 0 + + while not frontier.is_empty(): + node = frontier.pop() + + yield SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + ) + + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ) + + if node.state in explored: + continue + + explored.add(node.state) + nodes_expanded += 1 + + for action in problem.actions(node.state): + child_state = problem.result(node.state, action) + step_cost = problem.step_cost(node.state, action, child_state) + new_cost = node.path_cost + step_cost + + if child_state not in explored: + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=new_cost, + depth=node.depth + 1, + priority=new_cost + ) + frontier.push(child) + + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ) diff --git a/backend/app/api/__init__.py b/backend/app/api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1ba4038003af96b16ba233758fd7e2a265015a2a --- /dev/null +++ b/backend/app/api/__init__.py @@ -0,0 +1,5 @@ +"""API package.""" +from .routes import router +from .websocket import handle_visualization, manager + +__all__ = ["router", "handle_visualization", "manager"] diff --git a/backend/app/api/routes.py b/backend/app/api/routes.py new file mode 100644 index 0000000000000000000000000000000000000000..8ce15aba18ba4b49413da4ab3235bd94957cd652 --- /dev/null +++ b/backend/app/api/routes.py @@ -0,0 +1,270 @@ +"""API routes for the delivery search application.""" +from fastapi import APIRouter, HTTPException +from typing import List + +from ..models.requests import ( + GridConfig, + SearchRequest, + PathRequest, + CompareRequest, + Position, + GenerateResponse, + SearchResponse, + PlanResponse, + ComparisonResult, + CompareResponse, + AlgorithmInfo, + AlgorithmsResponse, + GridData, + StoreData, + DestinationData, + TunnelData, + SegmentData, +) +from ..services import gen_grid, parse_full_state, measure_performance +from ..core import DeliverySearch, DeliveryPlanner + + +router = APIRouter() + + +# Algorithm metadata +ALGORITHMS = [ + AlgorithmInfo( + code="BF", + name="Breadth-First Search", + description="Explores all nodes at current depth before moving deeper. Finds shortest path in terms of steps." + ), + AlgorithmInfo( + code="DF", + name="Depth-First Search", + description="Explores as far as possible along each branch. Memory efficient but may not find optimal path." + ), + AlgorithmInfo( + code="ID", + name="Iterative Deepening", + description="Combines BFS completeness with DFS space efficiency. Good for unknown depth goals." + ), + AlgorithmInfo( + code="UC", + name="Uniform Cost Search", + description="Expands lowest-cost node first. Always finds the optimal (minimum cost) solution." + ), + AlgorithmInfo( + code="GR1", + name="Greedy (Manhattan)", + description="Uses Manhattan distance heuristic. Fast but may not find optimal path." + ), + AlgorithmInfo( + code="GR2", + name="Greedy (Euclidean)", + description="Uses Euclidean distance heuristic. Fast but may not find optimal path." + ), + AlgorithmInfo( + code="AS1", + name="A* (Manhattan)", + description="A* with Manhattan distance. Optimal and complete with admissible heuristic." + ), + AlgorithmInfo( + code="AS2", + name="A* (Tunnel-Aware)", + description="A* considering tunnel shortcuts. More informed for grids with tunnels." + ), +] + + +@router.get("/api/health") +async def health_check(): + """Health check endpoint.""" + return {"status": "ok"} + + +@router.get("/api/algorithms", response_model=AlgorithmsResponse) +async def list_algorithms(): + """List available search algorithms.""" + return AlgorithmsResponse(algorithms=ALGORITHMS) + + +@router.post("/api/grid/generate", response_model=GenerateResponse) +async def generate_grid(config: GridConfig): + """Generate a random grid configuration.""" + try: + initial_state, traffic, state = gen_grid( + width=config.width, + height=config.height, + num_stores=config.num_stores, + num_destinations=config.num_destinations, + num_tunnels=config.num_tunnels, + obstacle_density=config.obstacle_density + ) + + # Convert to GridData for frontend + parsed = GridData( + width=state.grid.width, + height=state.grid.height, + stores=[ + StoreData(id=s.id, position=Position(x=s.position[0], y=s.position[1])) + for s in state.stores + ], + destinations=[ + DestinationData(id=d.id, position=Position(x=d.position[0], y=d.position[1])) + for d in state.destinations + ], + tunnels=[ + TunnelData( + entrance1=Position(x=t.entrance1[0], y=t.entrance1[1]), + entrance2=Position(x=t.entrance2[0], y=t.entrance2[1]), + cost=t.cost + ) + for t in state.tunnels + ], + segments=[ + SegmentData( + src=Position(x=seg.src[0], y=seg.src[1]), + dst=Position(x=seg.dst[0], y=seg.dst[1]), + traffic=seg.traffic + ) + for seg in state.grid.segments.values() + ] + ) + + return GenerateResponse( + initial_state=initial_state, + traffic=traffic, + parsed=parsed + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@router.post("/api/search/path", response_model=SearchResponse) +async def find_path(request: PathRequest): + """Find path from start to goal using specified strategy.""" + try: + from ..models.grid import Grid + from ..models.entities import Tunnel + + # Build grid from request + grid = Grid(width=request.grid_width, height=request.grid_height) + for seg in request.segments: + grid.add_segment( + (seg.src.x, seg.src.y), + (seg.dst.x, seg.dst.y), + seg.traffic + ) + + # Build tunnels + tunnels = [ + Tunnel( + entrance1=(t.entrance1.x, t.entrance1.y), + entrance2=(t.entrance2.x, t.entrance2.y) + ) + for t in request.tunnels + ] + + # Run search with metrics + with measure_performance() as metrics: + result, steps = DeliverySearch.path( + grid, + (request.start.x, request.start.y), + (request.goal.x, request.goal.y), + tunnels, + request.strategy.value, + visualize=True + ) + metrics.sample() + + return SearchResponse( + plan=result.plan, + cost=result.cost, + nodes_expanded=result.nodes_expanded, + runtime_ms=metrics.runtime_ms, + memory_mb=max(0, metrics.memory_mb), + cpu_percent=metrics.cpu_percent, + path=[Position(x=p[0], y=p[1]) for p in result.path], + steps=[s.to_dict() for s in steps] if steps else None + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@router.post("/api/search/plan", response_model=PlanResponse) +async def create_plan(request: SearchRequest): + """Create full delivery plan for all trucks and destinations.""" + try: + # Parse state + state = parse_full_state(request.initial_state, request.traffic) + + # Run planner with metrics + with measure_performance() as metrics: + plan_result, viz_data = DeliveryPlanner.plan_from_state( + state.grid, + state.stores, + state.destinations, + state.tunnels, + request.strategy.value, + request.visualize + ) + metrics.sample() + + return PlanResponse( + output=plan_result.to_string(), + assignments=[a.to_dict() for a in plan_result.assignments], + total_cost=plan_result.total_cost, + total_nodes_expanded=plan_result.total_nodes_expanded, + runtime_ms=metrics.runtime_ms, + memory_mb=max(0, metrics.memory_mb), + cpu_percent=metrics.cpu_percent + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@router.post("/api/search/compare", response_model=CompareResponse) +async def compare_algorithms(request: CompareRequest): + """Run all algorithms on same problem and return comparison.""" + try: + state = parse_full_state(request.initial_state, request.traffic) + + results: List[ComparisonResult] = [] + optimal_cost = float('inf') + + # Run each algorithm + for algo_info in ALGORITHMS: + with measure_performance() as metrics: + plan_result, _ = DeliveryPlanner.plan_from_state( + state.grid, + state.stores, + state.destinations, + state.tunnels, + algo_info.code, + visualize=False + ) + metrics.sample() + + # Track optimal cost (from UCS or A*) + if algo_info.code in ["UC", "AS1", "AS2"]: + optimal_cost = min(optimal_cost, plan_result.total_cost) + + results.append(ComparisonResult( + algorithm=algo_info.code, + name=algo_info.name, + plan=plan_result.to_string(), + cost=plan_result.total_cost, + nodes_expanded=plan_result.total_nodes_expanded, + runtime_ms=metrics.runtime_ms, + memory_mb=max(0, metrics.memory_mb), + cpu_percent=metrics.cpu_percent, + is_optimal=False # Will be set below + )) + + # Mark optimal solutions + for result in results: + result.is_optimal = (result.cost == optimal_cost) + + return CompareResponse( + comparisons=results, + optimal_cost=optimal_cost + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) diff --git a/backend/app/core/__init__.py b/backend/app/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..67189d621666f0dc9b5e73435ca8a66f31a490c0 --- /dev/null +++ b/backend/app/core/__init__.py @@ -0,0 +1,19 @@ +"""Core search module.""" +from .node import SearchNode +from .frontier import Frontier, QueueFrontier, StackFrontier, PriorityQueueFrontier +from .generic_search import GenericSearch, graph_search, graph_search_generator +from .delivery_search import DeliverySearch +from .delivery_planner import DeliveryPlanner + +__all__ = [ + "SearchNode", + "Frontier", + "QueueFrontier", + "StackFrontier", + "PriorityQueueFrontier", + "GenericSearch", + "graph_search", + "graph_search_generator", + "DeliverySearch", + "DeliveryPlanner", +] diff --git a/backend/app/core/delivery_planner.py b/backend/app/core/delivery_planner.py new file mode 100644 index 0000000000000000000000000000000000000000..b13b85fa8185945c17afd50ca01b5fc4d2283dde --- /dev/null +++ b/backend/app/core/delivery_planner.py @@ -0,0 +1,197 @@ +"""DeliveryPlanner - Plans which trucks deliver which packages.""" +from typing import List, Dict, Tuple, Optional +from .delivery_search import DeliverySearch +from ..models.grid import Grid +from ..models.entities import Store, Destination, Tunnel +from ..models.state import PathResult, PlanResult, DeliveryAssignment, SearchStep + + +class DeliveryPlanner: + """ + Plans the assignment of destinations to stores/trucks. + + For each destination, finds the optimal store to deliver from and computes the delivery path. + """ + + def __init__( + self, + grid: Grid, + stores: List[Store], + destinations: List[Destination], + tunnels: Optional[List[Tunnel]] = None + ): + """ + Initialize the delivery planner. + + Args: + grid: The city grid with traffic information + stores: List of store locations (each has a truck) + destinations: List of customer destinations + tunnels: Available tunnels + """ + self.grid = grid + self.stores = stores + self.destinations = destinations + self.tunnels = tunnels or [] + + def plan( + self, + strategy: str, + visualize: bool = False + ) -> Tuple[PlanResult, Optional[Dict[int, List[SearchStep]]]]: + """ + Create delivery plan assigning destinations to stores. + + Algorithm: + 1. For each destination, compute path cost from each store + 2. Assign each destination to the store with minimum path cost + 3. Compile results + + Args: + strategy: Search strategy to use + visualize: If True, collect visualization steps + + Returns: + Tuple of (PlanResult, Optional visualization data) + """ + assignments: List[DeliveryAssignment] = [] + all_steps: Dict[int, List[SearchStep]] = {} if visualize else None + total_cost = 0.0 + total_nodes = 0 + + # For each destination, find best store + for dest in self.destinations: + best_store: Optional[Store] = None + best_result: Optional[PathResult] = None + best_steps: Optional[List[SearchStep]] = None + best_cost = float('inf') + + # Try each store + for store in self.stores: + result, steps = DeliverySearch.path( + self.grid, + store.position, + dest.position, + self.tunnels, + strategy, + visualize + ) + + # Track nodes expanded + total_nodes += result.nodes_expanded + + # Check if this is better + if result.cost < best_cost: + best_cost = result.cost + best_store = store + best_result = result + best_steps = steps + + # Create assignment + if best_store and best_result: + assignment = DeliveryAssignment( + store_id=best_store.id, + destination_id=dest.id, + path_result=best_result + ) + assignments.append(assignment) + total_cost += best_result.cost + + if visualize and best_steps: + all_steps[dest.id] = best_steps + + return PlanResult( + assignments=assignments, + total_cost=total_cost, + total_nodes_expanded=total_nodes + ), all_steps + + def plan_all_from_store( + self, + store: Store, + strategy: str, + visualize: bool = False + ) -> List[Tuple[Destination, PathResult, Optional[List[SearchStep]]]]: + """ + Plan all deliveries from a single store. + + This variant finds paths from one store to all destinations, + useful for comparing which destinations are closest. + + Args: + store: The store to deliver from + strategy: Search strategy to use + visualize: If True, collect visualization steps + + Returns: + List of (destination, path_result, steps) tuples + """ + results = [] + + for dest in self.destinations: + result, steps = DeliverySearch.path( + self.grid, + store.position, + dest.position, + self.tunnels, + strategy, + visualize + ) + results.append((dest, result, steps)) + + # Sort by cost (closest first) + results.sort(key=lambda x: x[1].cost) + return results + + def plan_sequential( + self, + strategy: str, + visualize: bool = False + ) -> Tuple[PlanResult, Optional[Dict]]: + """ + Plan deliveries where trucks return to store after each delivery. + + For each destination: + 1. Find best store (minimum round-trip or just delivery cost) + 2. Assign to that store + + This is the simplified version as per project spec where + "once a delivery has been made, the truck immediately returns + to the store and can now make a new delivery." + + Args: + strategy: Search strategy to use + visualize: If True, collect visualization steps + + Returns: + Tuple of (PlanResult, Optional visualization data) + """ + # For this simplified version, we use the same logic as plan() + # since each delivery is independent (truck returns to store) + return self.plan(strategy, visualize) + + @staticmethod + def plan_from_state( + grid: Grid, + stores: List[Store], + destinations: List[Destination], + tunnels: List[Tunnel], + strategy: str, + visualize: bool = False + ) -> Tuple[PlanResult, Optional[Dict]]: + """ + Static method to create and run planner. + + Args: + grid: The city grid + stores: List of stores + destinations: List of destinations + tunnels: Available tunnels + strategy: Search strategy + visualize: If True, collect visualization steps + + Returns: + Tuple of (PlanResult, Optional visualization data) + """ + planner = DeliveryPlanner(grid, stores, destinations, tunnels) + return planner.plan(strategy, visualize) diff --git a/backend/app/core/delivery_search.py b/backend/app/core/delivery_search.py new file mode 100644 index 0000000000000000000000000000000000000000..d2f87af3eb51cc238aa27c369e9228871ee1108f --- /dev/null +++ b/backend/app/core/delivery_search.py @@ -0,0 +1,211 @@ +"""DeliverySearch - Search problem for package delivery.""" +from typing import List, Tuple, Optional, Dict +from .generic_search import GenericSearch +from ..models.grid import Grid +from ..models.entities import Tunnel +from ..models.state import PathResult, SearchStep +from ..heuristics import create_tunnel_aware_heuristic + + +class DeliverySearch(GenericSearch): + """ + Search problem for finding path from a store to a destination. + + Implements the GenericSearch interface for the package delivery problem. + Supports movement in 4 directions (up/down/left/right) and tunnel travel. + """ + + def __init__( + self, + grid: Grid, + start: Tuple[int, int], + goal: Tuple[int, int], + tunnels: Optional[List[Tunnel]] = None + ): + """ + Initialize the delivery search problem. + + Args: + grid: The city grid with traffic information + start: Starting position (store location) + goal: Goal position (destination location) + tunnels: List of available tunnels + """ + self.grid = grid + self.start = start + self.goal = goal + self.tunnels = tunnels or [] + + # Create tunnel lookup by entrance position + self._tunnel_entrances: Dict[Tuple[int, int], Tunnel] = {} + for tunnel in self.tunnels: + self._tunnel_entrances[tunnel.entrance1] = tunnel + self._tunnel_entrances[tunnel.entrance2] = tunnel + + # Create tunnel-aware heuristic + self._tunnel_heuristic = create_tunnel_aware_heuristic(self.tunnels) + + def initial_state(self) -> Tuple[int, int]: + """Return the starting position.""" + return self.start + + def goal_test(self, state: Tuple[int, int]) -> bool: + """Check if current state is the goal.""" + return state == self.goal + + def actions(self, state: Tuple[int, int]) -> List[str]: + """ + Return list of valid actions from current state. + + Actions: + - up: Move up (y+1) + - down: Move down (y-1) + - left: Move left (x-1) + - right: Move right (x+1) + - tunnel: Use tunnel if at entrance + + Returns: + List of valid action strings + """ + x, y = state + valid_actions = [] + + # Check each direction + directions = [ + ("up", (x, y + 1)), + ("down", (x, y - 1)), + ("left", (x - 1, y)), + ("right", (x + 1, y)), + ] + + for action, new_pos in directions: + if self.grid.is_valid_position(new_pos): + if not self.grid.is_blocked(state, new_pos): + valid_actions.append(action) + + # Check for tunnel + if state in self._tunnel_entrances: + valid_actions.append("tunnel") + + return valid_actions + + def result(self, state: Tuple[int, int], action: str) -> Tuple[int, int]: + """ + Apply action to state and return new state. + + Args: + state: Current position + action: Action to take + + Returns: + New position after action + """ + x, y = state + + if action == "up": + return (x, y + 1) + elif action == "down": + return (x, y - 1) + elif action == "left": + return (x - 1, y) + elif action == "right": + return (x + 1, y) + elif action == "tunnel": + if state in self._tunnel_entrances: + tunnel = self._tunnel_entrances[state] + return tunnel.get_other_entrance(state) + else: + raise ValueError(f"No tunnel entrance at {state}") + else: + raise ValueError(f"Unknown action: {action}") + + def step_cost( + self, + state: Tuple[int, int], + action: str, + next_state: Tuple[int, int] + ) -> float: + """ + Return the cost of taking an action. + + For regular moves: cost = traffic level of the segment + For tunnels: cost = Manhattan distance between entrances + + Args: + state: Current position + action: Action taken + next_state: Resulting position + + Returns: + Cost of the action + """ + if action == "tunnel": + tunnel = self._tunnel_entrances.get(state) + if tunnel: + return tunnel.cost + return 0.0 + else: + # Regular movement - cost is traffic level + return self.grid.get_traffic(state, next_state) + + def heuristic(self, state: Tuple[int, int]) -> float: + """ + Tunnel-aware heuristic for A* search. + + Args: + state: Current position + + Returns: + Estimated cost to goal + """ + return self._tunnel_heuristic(state, self.goal) + + @staticmethod + def path( + grid: Grid, + start: Tuple[int, int], + goal: Tuple[int, int], + tunnels: List[Tunnel], + strategy: str, + visualize: bool = False + ) -> Tuple[PathResult, Optional[List[SearchStep]]]: + """ + Find path from start to goal using specified strategy. + + Args: + grid: The city grid + start: Starting position + goal: Goal position + tunnels: Available tunnels + strategy: Search strategy (BF, DF, ID, UC, GR1, GR2, AS1, AS2) + visualize: If True, collect visualization steps + + Returns: + Tuple of (PathResult, Optional[List[SearchStep]]) + """ + search = DeliverySearch(grid, start, goal, tunnels) + return search.solve(strategy, visualize) + + @staticmethod + def path_string( + grid: Grid, + start: Tuple[int, int], + goal: Tuple[int, int], + tunnels: List[Tunnel], + strategy: str + ) -> str: + """ + Find path and return formatted string. + + Args: + grid: The city grid + start: Starting position + goal: Goal position + tunnels: Available tunnels + strategy: Search strategy + + Returns: + String in format "plan;cost;nodesExpanded" + """ + result, _ = DeliverySearch.path(grid, start, goal, tunnels, strategy) + return result.to_string() diff --git a/backend/app/core/frontier.py b/backend/app/core/frontier.py new file mode 100644 index 0000000000000000000000000000000000000000..d0d6637e19823f0926c68b4b66297d0da256cfd5 --- /dev/null +++ b/backend/app/core/frontier.py @@ -0,0 +1,182 @@ +"""Frontier data structures for search algorithms.""" +from abc import ABC, abstractmethod +from collections import deque +import heapq +from typing import List, Optional, Set, Dict +from .node import SearchNode + + +class Frontier(ABC): + """Abstract base class for frontier data structures.""" + + @abstractmethod + def push(self, node: SearchNode) -> None: + """Add a node to the frontier.""" + pass + + @abstractmethod + def pop(self) -> SearchNode: + """Remove and return the next node from the frontier.""" + pass + + @abstractmethod + def is_empty(self) -> bool: + """Check if the frontier is empty.""" + pass + + @abstractmethod + def __len__(self) -> int: + """Return the number of nodes in the frontier.""" + pass + + @abstractmethod + def contains_state(self, state) -> bool: + """Check if a state is in the frontier.""" + pass + + def get_states(self) -> List: + """Get all states in the frontier (for visualization).""" + return [] + + +class QueueFrontier(Frontier): + """FIFO queue frontier for Breadth-First Search.""" + + def __init__(self): + self._queue: deque[SearchNode] = deque() + self._states: Set = set() + + def push(self, node: SearchNode) -> None: + self._queue.append(node) + self._states.add(node.state) + + def pop(self) -> SearchNode: + node = self._queue.popleft() + self._states.discard(node.state) + return node + + def is_empty(self) -> bool: + return len(self._queue) == 0 + + def __len__(self) -> int: + return len(self._queue) + + def contains_state(self, state) -> bool: + return state in self._states + + def get_states(self) -> List: + return [node.state for node in self._queue] + + +class StackFrontier(Frontier): + """LIFO stack frontier for Depth-First Search.""" + + def __init__(self): + self._stack: List[SearchNode] = [] + self._states: Set = set() + + def push(self, node: SearchNode) -> None: + self._stack.append(node) + self._states.add(node.state) + + def pop(self) -> SearchNode: + node = self._stack.pop() + self._states.discard(node.state) + return node + + def is_empty(self) -> bool: + return len(self._stack) == 0 + + def __len__(self) -> int: + return len(self._stack) + + def contains_state(self, state) -> bool: + return state in self._states + + def get_states(self) -> List: + return [node.state for node in self._stack] + + +class PriorityQueueFrontier(Frontier): + """ + Priority queue frontier for UCS, Greedy, and A* search. + + Uses heapq with a counter to break ties and ensure FIFO ordering for nodes with equal priority. + """ + + def __init__(self): + self._heap: List[tuple] = [] # (priority, counter, node) + self._counter: int = 0 + self._states: Dict = {} # state -> (priority, node) for updates + self._removed: Set = set() # Track removed entries + + def push(self, node: SearchNode) -> None: + """ + Add a node to the priority queue. + + If a node with the same state already exists with higher priority, it will be updated. + """ + state = node.state + priority = node.priority + + # If state exists with higher or equal priority, skip + if state in self._states: + existing_priority, _ = self._states[state] + if existing_priority <= priority: + return + # Mark old entry as removed + self._removed.add((existing_priority, state)) + + # Add new entry + entry = (priority, self._counter, node) + heapq.heappush(self._heap, entry) + self._states[state] = (priority, node) + self._counter += 1 + + def pop(self) -> SearchNode: + """Remove and return the node with lowest priority.""" + while self._heap: + priority, _, node = heapq.heappop(self._heap) + + # Skip removed entries + if (priority, node.state) in self._removed: + self._removed.discard((priority, node.state)) + continue + + # Skip if this is not the current entry for this state + if node.state in self._states: + current_priority, current_node = self._states[node.state] + if current_priority != priority: + continue + del self._states[node.state] + + return node + + raise IndexError("Pop from empty frontier") + + def is_empty(self) -> bool: + # Account for lazy deletions + return len(self._states) == 0 + + def __len__(self) -> int: + return len(self._states) + + def contains_state(self, state) -> bool: + return state in self._states + + def get_node(self, state) -> Optional[SearchNode]: + """Get the node for a given state if it exists.""" + if state in self._states: + _, node = self._states[state] + return node + return None + + def get_states(self) -> List: + return list(self._states.keys()) + + def get_priority(self, state) -> Optional[float]: + """Get the priority of a state if it exists.""" + if state in self._states: + priority, _ = self._states[state] + return priority + return None diff --git a/backend/app/core/generic_search.py b/backend/app/core/generic_search.py new file mode 100644 index 0000000000000000000000000000000000000000..f2cc1f3fb56652d5d5f4ee09bf52fb5d1beed4e8 --- /dev/null +++ b/backend/app/core/generic_search.py @@ -0,0 +1,303 @@ +"""Generic search problem abstract base class.""" +from abc import ABC, abstractmethod +from typing import List, Tuple, Optional, Generator +from .node import SearchNode +from .frontier import Frontier +from ..models.state import PathResult, SearchStep + + +class GenericSearch(ABC): + """ + Abstract base class for search problems. + + Subclasses must implement: + - initial_state(): Return the starting state + - goal_test(state): Return True if state is a goal + - actions(state): Return list of valid actions from state + - result(state, action): Return new state after action + - step_cost(state, action, next_state): Return cost of action + """ + + @abstractmethod + def initial_state(self) -> Tuple[int, int]: + """Return the initial state.""" + pass + + @abstractmethod + def goal_test(self, state: Tuple[int, int]) -> bool: + """Return True if state is a goal state.""" + pass + + @abstractmethod + def actions(self, state: Tuple[int, int]) -> List[str]: + """Return list of valid actions from given state.""" + pass + + @abstractmethod + def result(self, state: Tuple[int, int], action: str) -> Tuple[int, int]: + """Return the state resulting from taking action in given state.""" + pass + + @abstractmethod + def step_cost( + self, state: Tuple[int, int], action: str, next_state: Tuple[int, int] + ) -> float: + """Return the cost of taking action from state to next_state.""" + pass + + def heuristic(self, state: Tuple[int, int]) -> float: + """ + Heuristic function h(n) estimating cost from state to goal. + Override in subclass for informed search. + """ + return 0.0 + + def solve( + self, + strategy: str, + visualize: bool = False + ) -> Tuple[PathResult, Optional[List[SearchStep]]]: + """ + Solve the search problem using the specified strategy. + + Args: + strategy: One of 'BF', 'DF', 'ID', 'UC', 'GR1', 'GR2', 'AS1', 'AS2' + visualize: If True, collect visualization steps + + Returns: + Tuple of (PathResult, Optional[List[SearchStep]]) + """ + from ..algorithms import ( + bfs_search, + dfs_search, + ids_search, + ucs_search, + greedy_search, + astar_search, + ) + from ..heuristics import ( + manhattan_heuristic, + euclidean_heuristic, + ) + + # Wrap instance heuristic to match expected signature (state, goal) -> float + def tunnel_aware_wrapper(state, goal): + return self.heuristic(state) + + # Map strategy codes to search functions + strategy_map = { + 'BF': lambda: bfs_search(self, visualize), + 'DF': lambda: dfs_search(self, visualize), + 'ID': lambda: ids_search(self, visualize), + 'UC': lambda: ucs_search(self, visualize), + 'GR1': lambda: greedy_search(self, manhattan_heuristic, visualize), + 'GR2': lambda: greedy_search(self, euclidean_heuristic, visualize), + 'AS1': lambda: astar_search(self, manhattan_heuristic, visualize), + 'AS2': lambda: astar_search(self, tunnel_aware_wrapper, visualize), # Tunnel-aware + } + + if strategy not in strategy_map: + raise ValueError(f"Unknown strategy: {strategy}") + + return strategy_map[strategy]() + + def solve_with_steps( + self, strategy: str + ) -> Generator[SearchStep, None, PathResult]: + """ + Generator version of solve that yields steps for real-time visualization. + + Args: + strategy: Search strategy code + + Yields: + SearchStep objects during search + + Returns: + Final PathResult + """ + from ..algorithms import ( + bfs_search_generator, + dfs_search_generator, + ids_search_generator, + ucs_search_generator, + greedy_search_generator, + astar_search_generator, + ) + from ..heuristics import ( + manhattan_heuristic, + euclidean_heuristic, + ) + + # Wrap instance heuristic to match expected signature (state, goal) -> float + def tunnel_aware_wrapper(state, goal): + return self.heuristic(state) + + strategy_map = { + 'BF': lambda: bfs_search_generator(self), + 'DF': lambda: dfs_search_generator(self), + 'ID': lambda: ids_search_generator(self), + 'UC': lambda: ucs_search_generator(self), + 'GR1': lambda: greedy_search_generator(self, manhattan_heuristic), + 'GR2': lambda: greedy_search_generator(self, euclidean_heuristic), + 'AS1': lambda: astar_search_generator(self, manhattan_heuristic), + 'AS2': lambda: astar_search_generator(self, tunnel_aware_wrapper), + } + + if strategy not in strategy_map: + raise ValueError(f"Unknown strategy: {strategy}") + + return strategy_map[strategy]() + + +def graph_search( + problem: GenericSearch, + frontier: Frontier, + visualize: bool = False +) -> Tuple[PathResult, Optional[List[SearchStep]]]: + """ + Generic graph search algorithm. + + Args: + problem: The search problem to solve + frontier: The frontier data structure (Queue, Stack, or PriorityQueue) + visualize: If True, collect visualization steps + + Returns: + Tuple of (PathResult, Optional[List[SearchStep]]) + """ + # Initialize + start = problem.initial_state() + start_node = SearchNode(state=start, path_cost=0, depth=0) + frontier.push(start_node) + explored: set = set() + nodes_expanded = 0 + steps: List[SearchStep] = [] if visualize else None + + while not frontier.is_empty(): + # Get next node + node = frontier.pop() + + # Record step for visualization + if visualize: + steps.append(SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + )) + + # Goal test + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ), steps + + # Skip if already explored + if node.state in explored: + continue + + # Mark as explored and count + explored.add(node.state) + nodes_expanded += 1 + + # Expand node + for action in problem.actions(node.state): + child_state = problem.result(node.state, action) + if child_state not in explored and not frontier.contains_state(child_state): + step_cost = problem.step_cost(node.state, action, child_state) + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=node.path_cost + step_cost, + depth=node.depth + 1 + ) + frontier.push(child) + + # No solution found + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ), steps + + +def graph_search_generator( + problem: GenericSearch, + frontier: Frontier +) -> Generator[SearchStep, None, PathResult]: + """ + Generator version of graph search that yields steps during execution. + + Args: + problem: The search problem to solve + frontier: The frontier data structure + + Yields: + SearchStep objects + + Returns: + Final PathResult + """ + start = problem.initial_state() + start_node = SearchNode(state=start, path_cost=0, depth=0) + frontier.push(start_node) + explored: set = set() + nodes_expanded = 0 + + while not frontier.is_empty(): + node = frontier.pop() + + # Yield current step + yield SearchStep( + step_number=nodes_expanded, + current_node=node.state, + action=node.action, + frontier=frontier.get_states(), + explored=list(explored), + current_path=node.get_path(), + path_cost=node.path_cost + ) + + # Goal test + if problem.goal_test(node.state): + return PathResult( + plan=node.get_solution(), + cost=node.path_cost, + nodes_expanded=nodes_expanded, + path=node.get_path() + ) + + if node.state in explored: + continue + + explored.add(node.state) + nodes_expanded += 1 + + for action in problem.actions(node.state): + child_state = problem.result(node.state, action) + if child_state not in explored and not frontier.contains_state(child_state): + step_cost = problem.step_cost(node.state, action, child_state) + child = SearchNode( + state=child_state, + parent=node, + action=action, + path_cost=node.path_cost + step_cost, + depth=node.depth + 1 + ) + frontier.push(child) + + return PathResult( + plan="", + cost=float('inf'), + nodes_expanded=nodes_expanded, + path=[] + ) diff --git a/backend/app/core/node.py b/backend/app/core/node.py new file mode 100644 index 0000000000000000000000000000000000000000..5bdb4ab4f9c0e15ef79b858fa39347c575149bd1 --- /dev/null +++ b/backend/app/core/node.py @@ -0,0 +1,119 @@ +"""SearchNode class for the search tree.""" +from dataclasses import dataclass, field +from typing import Optional, List, Tuple, Any + + +@dataclass +class SearchNode: + """ + Represents a node in the search tree. + + Attributes: + state: Current position (x, y) on the grid + parent: Parent node for path reconstruction + action: Action taken to reach this node (up/down/left/right/tunnel) + path_cost: g(n) - cost from start to this node + depth: Depth in search tree + """ + state: Tuple[int, int] + parent: Optional['SearchNode'] = None + action: Optional[str] = None + path_cost: float = 0.0 + depth: int = 0 + # For priority queue - lower is better + priority: float = field(default=0.0, compare=False) + + def __lt__(self, other: 'SearchNode') -> bool: + """Compare nodes by priority for priority queue.""" + return self.priority < other.priority + + def __eq__(self, other: Any) -> bool: + """Nodes are equal if they have the same state.""" + if not isinstance(other, SearchNode): + return False + return self.state == other.state + + def __hash__(self) -> int: + """Hash by state for set membership.""" + return hash(self.state) + + def get_path(self) -> List[Tuple[int, int]]: + """ + Reconstruct the path from root to this node. + + Returns: + List of positions from start to current node + """ + path = [] + node: Optional[SearchNode] = self + while node is not None: + path.append(node.state) + node = node.parent + path.reverse() + return path + + def get_actions(self) -> List[str]: + """ + Reconstruct the sequence of actions from root to this node. + + Returns: + List of actions taken from start to current node + """ + actions = [] + node: Optional[SearchNode] = self + while node is not None and node.action is not None: + actions.append(node.action) + node = node.parent + actions.reverse() + return actions + + def get_solution(self) -> str: + """ + Get the solution as a comma-separated string of actions. + + Returns: + String in format "action1,action2,action3,..." + """ + actions = self.get_actions() + return ",".join(actions) if actions else "" + + def expand( + self, + actions_func, + result_func, + cost_func, + heuristic_func=None + ) -> List['SearchNode']: + """ + Expand this node by generating all child nodes. + + Args: + actions_func: Function(state) -> List[str] of valid actions + result_func: Function(state, action) -> new_state + cost_func: Function(state, action, new_state) -> step_cost + heuristic_func: Optional Function(state, goal) -> h(n) for A*/Greedy + + Returns: + List of child SearchNode objects + """ + children = [] + for action in actions_func(self.state): + new_state = result_func(self.state, action) + step_cost = cost_func(self.state, action, new_state) + child = SearchNode( + state=new_state, + parent=self, + action=action, + path_cost=self.path_cost + step_cost, + depth=self.depth + 1 + ) + # Set priority if heuristic is provided (for A*) + if heuristic_func is not None: + child.priority = child.path_cost + heuristic_func(new_state) + else: + child.priority = child.path_cost + children.append(child) + return children + + def __repr__(self) -> str: + return f"SearchNode(state={self.state}, depth={self.depth}, cost={self.path_cost})" diff --git a/backend/app/heuristics/__init__.py b/backend/app/heuristics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..14059e7e1b8242a19e4fe1593d643e47ebd97e46 --- /dev/null +++ b/backend/app/heuristics/__init__.py @@ -0,0 +1,14 @@ +"""Heuristics package for informed search algorithms.""" +from .manhattan import manhattan_heuristic +from .euclidean import euclidean_heuristic +from .traffic_weighted import traffic_weighted_heuristic, create_traffic_weighted_heuristic +from .tunnel_aware import tunnel_aware_heuristic, create_tunnel_aware_heuristic + +__all__ = [ + "manhattan_heuristic", + "euclidean_heuristic", + "traffic_weighted_heuristic", + "create_traffic_weighted_heuristic", + "tunnel_aware_heuristic", + "create_tunnel_aware_heuristic", +] diff --git a/backend/app/heuristics/euclidean.py b/backend/app/heuristics/euclidean.py new file mode 100644 index 0000000000000000000000000000000000000000..2da1e9c7f8e01bed900ab1855c4485fc179396bb --- /dev/null +++ b/backend/app/heuristics/euclidean.py @@ -0,0 +1,24 @@ +"""Euclidean distance heuristic.""" +import math +from typing import Tuple + + +def euclidean_heuristic(state: Tuple[int, int], goal: Tuple[int, int]) -> float: + """ + Euclidean distance heuristic. + + h(n) = sqrt((x1 - x2)^2 + (y1 - y2)^2) + + Admissible: Straight-line distance is always <= actual path distance. + Since we can only move in cardinal directions, this will never overestimate the actual cost. + + Args: + state: Current position (x, y) + goal: Goal position (x, y) + + Returns: + Estimated cost to reach goal + """ + if goal is None: + return 0.0 + return math.sqrt((state[0] - goal[0]) ** 2 + (state[1] - goal[1]) ** 2) diff --git a/backend/app/heuristics/manhattan.py b/backend/app/heuristics/manhattan.py new file mode 100644 index 0000000000000000000000000000000000000000..35d428cc86c8108871d0f718ac71ce8608dc7929 --- /dev/null +++ b/backend/app/heuristics/manhattan.py @@ -0,0 +1,22 @@ +"""Manhattan distance heuristic.""" +from typing import Tuple + + +def manhattan_heuristic(state: Tuple[int, int], goal: Tuple[int, int]) -> float: + """ + Manhattan distance heuristic. + + h(n) = |x1 - x2| + |y1 - y2| + + Admissible: Assumes minimum cost of 1 per step, which is the minimum possible traffic level. + + Args: + state: Current position (x, y) + goal: Goal position (x, y) + + Returns: + Estimated cost to reach goal + """ + if goal is None: + return 0.0 + return abs(state[0] - goal[0]) + abs(state[1] - goal[1]) diff --git a/backend/app/heuristics/traffic_weighted.py b/backend/app/heuristics/traffic_weighted.py new file mode 100644 index 0000000000000000000000000000000000000000..75e62b330a53fe672d17a050d3757193229293d4 --- /dev/null +++ b/backend/app/heuristics/traffic_weighted.py @@ -0,0 +1,43 @@ +"""Traffic-weighted Manhattan heuristic.""" +from typing import Tuple + + +def traffic_weighted_heuristic( + state: Tuple[int, int], + goal: Tuple[int, int], + min_traffic: float = 1.0 +) -> float: + """ + Traffic-weighted Manhattan distance heuristic. + + h(n) = manhattan_distance * minimum_traffic_cost + + Admissible: Uses the minimum possible traffic cost to ensure we never overestimate the actual cost. + + Args: + state: Current position (x, y) + goal: Goal position (x, y) + min_traffic: Minimum traffic level in the grid (default 1.0) + + Returns: + Estimated cost to reach goal + """ + if goal is None: + return 0.0 + manhattan = abs(state[0] - goal[0]) + abs(state[1] - goal[1]) + return manhattan * min_traffic + + +def create_traffic_weighted_heuristic(min_traffic: float = 1.0): + """ + Factory function to create a traffic-weighted heuristic with specific min_traffic. + + Args: + min_traffic: Minimum traffic level in the grid + + Returns: + Heuristic function + """ + def heuristic(state: Tuple[int, int], goal: Tuple[int, int]) -> float: + return traffic_weighted_heuristic(state, goal, min_traffic) + return heuristic diff --git a/backend/app/heuristics/tunnel_aware.py b/backend/app/heuristics/tunnel_aware.py new file mode 100644 index 0000000000000000000000000000000000000000..c6309048b9aaf145f5b026978291c3e2979acb47 --- /dev/null +++ b/backend/app/heuristics/tunnel_aware.py @@ -0,0 +1,77 @@ +"""Tunnel-aware Manhattan heuristic.""" +from typing import Tuple, List, Optional +from .manhattan import manhattan_heuristic + + +def tunnel_aware_heuristic( + state: Tuple[int, int], + goal: Tuple[int, int], + tunnels: Optional[List] = None +) -> float: + """ + Tunnel-aware Manhattan distance heuristic. + + h(n) = min(direct_manhattan, tunnel_shortcuts) + + Considers potential tunnel shortcuts that might reduce the distance. + For each tunnel, calculates the cost of going to entrance, through tunnel, + and from exit to goal. + + Admissible: Takes minimum of all options, so never overestimates. + + Args: + state: Current position (x, y) + goal: Goal position (x, y) + tunnels: List of Tunnel objects with entrance1, entrance2, and cost + + Returns: + Estimated cost to reach goal + """ + if goal is None: + return 0.0 + + # Direct Manhattan distance + direct = manhattan_heuristic(state, goal) + + if not tunnels: + return direct + + # Check each tunnel for potential shortcut + best = direct + for tunnel in tunnels: + entrance1 = tunnel.entrance1 + entrance2 = tunnel.entrance2 + tunnel_cost = tunnel.cost + + # Path: state -> entrance1 -> (tunnel) -> entrance2 -> goal + via_tunnel_1 = ( + manhattan_heuristic(state, entrance1) + + tunnel_cost + + manhattan_heuristic(entrance2, goal) + ) + + # Path: state -> entrance2 -> (tunnel) -> entrance1 -> goal + via_tunnel_2 = ( + manhattan_heuristic(state, entrance2) + + tunnel_cost + + manhattan_heuristic(entrance1, goal) + ) + + best = min(best, via_tunnel_1, via_tunnel_2) + + return best + + +def create_tunnel_aware_heuristic(tunnels: List): + """ + Factory function to create a tunnel-aware heuristic with specific tunnels. + + Args: + tunnels: List of Tunnel objects + + Returns: + Heuristic function + """ + def heuristic(state: Tuple[int, int], goal: Tuple[int, int]) -> float: + return tunnel_aware_heuristic(state, goal, tunnels) + return heuristic diff --git a/backend/app/main.py b/backend/app/main.py new file mode 100644 index 0000000000000000000000000000000000000000..a783b93cc8b0506c46941f1cb3df978f61fecb52 --- /dev/null +++ b/backend/app/main.py @@ -0,0 +1,47 @@ +"""FastAPI application entry point.""" +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +import uvicorn + +from .api.routes import router + +# Create FastAPI app +app = FastAPI( + title="Package Delivery Search API", + description="Search algorithms for package delivery optimization", + version="1.0.0", +) + +# Configure CORS +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Include routes +app.include_router(router) + + +@app.get("/") +async def root(): + """Root endpoint.""" + return { + "name": "Package Delivery Search API", + "version": "1.0.0", + "endpoints": { + "health": "/api/health", + "algorithms": "/api/algorithms", + "generate": "/api/grid/generate", + "path": "/api/search/path", + "plan": "/api/search/plan", + "compare": "/api/search/compare", + "visualize": "ws://localhost:8000/ws/visualize" + } + } + + +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6df0a205caf8e712d6f8651b8b07f1469fa3c920 --- /dev/null +++ b/backend/app/models/__init__.py @@ -0,0 +1,63 @@ +"""Models package - exports all model classes.""" +from .grid import Grid, Segment +from .entities import Store, Destination, Tunnel, Truck +from .state import SearchState, PathResult, DeliveryAssignment, PlanResult, SearchStep, SearchMetrics +from .requests import ( + Algorithm, + Position, + SegmentData, + StoreData, + DestinationData, + TunnelData, + GridConfig, + SearchRequest, + PathRequest, + CompareRequest, + PathData, + GridData, + GenerateResponse, + SearchResponse, + PlanResponse, + ComparisonResult, + CompareResponse, + AlgorithmInfo, + AlgorithmsResponse, +) + +__all__ = [ + # Grid models + "Grid", + "Segment", + # Entity models + "Store", + "Destination", + "Tunnel", + "Truck", + # State models + "SearchState", + "PathResult", + "DeliveryAssignment", + "PlanResult", + "SearchStep", + "SearchMetrics", + # Request/Response models + "Algorithm", + "Position", + "SegmentData", + "StoreData", + "DestinationData", + "TunnelData", + "GridConfig", + "SearchRequest", + "PathRequest", + "CompareRequest", + "PathData", + "GridData", + "GenerateResponse", + "SearchResponse", + "PlanResponse", + "ComparisonResult", + "CompareResponse", + "AlgorithmInfo", + "AlgorithmsResponse", +] diff --git a/backend/app/models/entities.py b/backend/app/models/entities.py new file mode 100644 index 0000000000000000000000000000000000000000..017ef2f9749852c15d1a0bb4ef57ffd989caf7fd --- /dev/null +++ b/backend/app/models/entities.py @@ -0,0 +1,75 @@ +"""Entity models for stores, destinations, tunnels, and trucks.""" +from dataclasses import dataclass +from typing import Tuple + + +@dataclass +class Store: + """Represents a storage location / starting point for trucks.""" + id: int + position: Tuple[int, int] + + def to_dict(self) -> dict: + return { + "id": self.id, + "position": {"x": self.position[0], "y": self.position[1]} + } + + +@dataclass +class Destination: + """Represents a customer destination for package delivery.""" + id: int + position: Tuple[int, int] + + def to_dict(self) -> dict: + return { + "id": self.id, + "position": {"x": self.position[0], "y": self.position[1]} + } + + +@dataclass +class Tunnel: + """Represents an underground tunnel connecting two points.""" + entrance1: Tuple[int, int] + entrance2: Tuple[int, int] + + @property + def cost(self) -> int: + """Tunnel cost is Manhattan distance between entrances.""" + return abs(self.entrance1[0] - self.entrance2[0]) + abs(self.entrance1[1] - self.entrance2[1]) + + def get_other_entrance(self, entrance: Tuple[int, int]) -> Tuple[int, int]: + """Get the other entrance of the tunnel.""" + if entrance == self.entrance1: + return self.entrance2 + elif entrance == self.entrance2: + return self.entrance1 + raise ValueError(f"Position {entrance} is not an entrance of this tunnel") + + def has_entrance_at(self, pos: Tuple[int, int]) -> bool: + """Check if tunnel has an entrance at given position.""" + return pos == self.entrance1 or pos == self.entrance2 + + def to_dict(self) -> dict: + return { + "entrance1": {"x": self.entrance1[0], "y": self.entrance1[1]}, + "entrance2": {"x": self.entrance2[0], "y": self.entrance2[1]}, + "cost": self.cost + } + + +@dataclass +class Truck: + """Represents a delivery truck.""" + id: int + store_id: int + current_position: Tuple[int, int] + + def to_dict(self) -> dict: + return { + "id": self.id, + "store_id": self.store_id, + "position": {"x": self.current_position[0], "y": self.current_position[1]} + } diff --git a/backend/app/models/grid.py b/backend/app/models/grid.py new file mode 100644 index 0000000000000000000000000000000000000000..5de0a02812a73e112767403de2a551f636b25d92 --- /dev/null +++ b/backend/app/models/grid.py @@ -0,0 +1,84 @@ +"""Grid and Segment models for the delivery search problem.""" +from dataclasses import dataclass, field +from typing import Dict, Tuple, Optional + + +@dataclass +class Segment: + """Represents a road segment between two adjacent grid points.""" + src: Tuple[int, int] + dst: Tuple[int, int] + traffic: int # 0 = blocked, 1-4 = traffic level + + def __post_init__(self): + # Normalize segment direction (ensure src < dst lexicographically) + if self.src > self.dst: + self.src, self.dst = self.dst, self.src + + @property + def is_blocked(self) -> bool: + return self.traffic == 0 + + def get_key(self) -> Tuple[Tuple[int, int], Tuple[int, int]]: + """Get normalized key for segment lookup.""" + return (self.src, self.dst) + + +@dataclass +class Grid: + """Represents the city grid with all road segments.""" + width: int + height: int + segments: Dict[Tuple[Tuple[int, int], Tuple[int, int]], Segment] = field(default_factory=dict) + + def get_segment(self, src: Tuple[int, int], dst: Tuple[int, int]) -> Optional[Segment]: + """Get segment between two points (order doesn't matter).""" + key = (src, dst) if src < dst else (dst, src) + return self.segments.get(key) + + def get_traffic(self, src: Tuple[int, int], dst: Tuple[int, int]) -> int: + """Get traffic level for segment between two points.""" + segment = self.get_segment(src, dst) + return segment.traffic if segment else 0 + + def is_blocked(self, src: Tuple[int, int], dst: Tuple[int, int]) -> bool: + """Check if segment between two points is blocked.""" + return self.get_traffic(src, dst) == 0 + + def is_valid_position(self, pos: Tuple[int, int]) -> bool: + """Check if position is within grid bounds.""" + x, y = pos + return 0 <= x < self.width and 0 <= y < self.height + + def add_segment(self, src: Tuple[int, int], dst: Tuple[int, int], traffic: int): + """Add or update a segment.""" + segment = Segment(src, dst, traffic) + self.segments[segment.get_key()] = segment + + def get_neighbors(self, pos: Tuple[int, int]) -> list[Tuple[int, int]]: + """Get all valid neighboring positions (not blocked).""" + x, y = pos + neighbors = [] + directions = [(0, 1), (0, -1), (1, 0), (-1, 0)] # up, down, right, left + + for dx, dy in directions: + new_pos = (x + dx, y + dy) + if self.is_valid_position(new_pos) and not self.is_blocked(pos, new_pos): + neighbors.append(new_pos) + + return neighbors + + def to_dict(self) -> dict: + """Convert grid to dictionary for JSON serialization.""" + return { + "width": self.width, + "height": self.height, + "segments": [ + { + "src": {"x": seg.src[0], "y": seg.src[1]}, + "dst": {"x": seg.dst[0], "y": seg.dst[1]}, + "traffic": seg.traffic + } + for seg in self.segments.values() + ] + } diff --git a/backend/app/models/requests.py b/backend/app/models/requests.py new file mode 100644 index 0000000000000000000000000000000000000000..5d7134e5a0bb421ff6529f5b5794eb76292545e3 --- /dev/null +++ b/backend/app/models/requests.py @@ -0,0 +1,169 @@ +"""Pydantic models for API requests and responses.""" +from pydantic import BaseModel, Field +from typing import Optional, List, Tuple +from enum import Enum + + +class Algorithm(str, Enum): + """Available search algorithms.""" + BF = "BF" # Breadth-first search + DF = "DF" # Depth-first search + ID = "ID" # Iterative deepening + UC = "UC" # Uniform cost search + GR1 = "GR1" # Greedy with Manhattan heuristic + GR2 = "GR2" # Greedy with Euclidean heuristic + AS1 = "AS1" # A* with Manhattan heuristic + AS2 = "AS2" # A* with Tunnel-aware heuristic + + +class Position(BaseModel): + """A position on the grid.""" + x: int + y: int + + def to_tuple(self) -> Tuple[int, int]: + return (self.x, self.y) + + +class SegmentData(BaseModel): + """Segment data for API.""" + src: Position + dst: Position + traffic: int = Field(ge=0, le=4) + + +class StoreData(BaseModel): + """Store data for API.""" + id: int + position: Position + + +class DestinationData(BaseModel): + """Destination data for API.""" + id: int + position: Position + + +class TunnelData(BaseModel): + """Tunnel data for API.""" + entrance1: Position + entrance2: Position + cost: Optional[int] = None + + +# Request Models + +class GridConfig(BaseModel): + """Configuration for grid generation.""" + width: Optional[int] = Field(None, ge=5, le=50) + height: Optional[int] = Field(None, ge=5, le=50) + num_stores: Optional[int] = Field(None, ge=1, le=3) + num_destinations: Optional[int] = Field(None, ge=1, le=10) + num_tunnels: Optional[int] = Field(None, ge=0, le=10) + obstacle_density: float = Field(0.1, ge=0.0, le=0.5) + + +class SearchRequest(BaseModel): + """Request for running a search/plan.""" + initial_state: str + traffic: str + strategy: Algorithm + visualize: bool = False + + +class PathRequest(BaseModel): + """Request for finding a single path.""" + grid_width: int + grid_height: int + start: Position + goal: Position + segments: List[SegmentData] + tunnels: List[TunnelData] = [] + strategy: Algorithm + + +class CompareRequest(BaseModel): + """Request for comparing all algorithms.""" + initial_state: str + traffic: str + + +# Response Models + +class PathData(BaseModel): + """Path result data.""" + plan: str + cost: float + nodes_expanded: int + path: List[Position] + + +class GridData(BaseModel): + """Complete grid state data.""" + width: int + height: int + stores: List[StoreData] + destinations: List[DestinationData] + tunnels: List[TunnelData] + segments: List[SegmentData] + + +class GenerateResponse(BaseModel): + """Response from grid generation.""" + initial_state: str + traffic: str + parsed: GridData + + +class SearchResponse(BaseModel): + """Response from search/plan execution.""" + plan: str + cost: float + nodes_expanded: int + runtime_ms: float + memory_mb: float + cpu_percent: float + path: List[Position] + steps: Optional[List[dict]] = None + + +class PlanResponse(BaseModel): + """Response from delivery planning.""" + output: str + assignments: List[dict] + total_cost: float + total_nodes_expanded: int + runtime_ms: float + memory_mb: float + cpu_percent: float + + +class ComparisonResult(BaseModel): + """Result of comparing a single algorithm.""" + algorithm: str + name: str + plan: str + cost: float + nodes_expanded: int + runtime_ms: float + memory_mb: float + cpu_percent: float + is_optimal: bool = False + + +class CompareResponse(BaseModel): + """Response from algorithm comparison.""" + comparisons: List[ComparisonResult] + optimal_cost: float + + +class AlgorithmInfo(BaseModel): + """Information about an algorithm.""" + code: str + name: str + description: str + + +class AlgorithmsResponse(BaseModel): + """List of available algorithms.""" + algorithms: List[AlgorithmInfo] diff --git a/backend/app/models/state.py b/backend/app/models/state.py new file mode 100644 index 0000000000000000000000000000000000000000..0e53a4fb703ea89931ad3fa9e6dfcd66c6735061 --- /dev/null +++ b/backend/app/models/state.py @@ -0,0 +1,133 @@ +"""State models for search and planning results.""" +from dataclasses import dataclass, field +from typing import List, Optional, Tuple +from .grid import Grid +from .entities import Store, Destination, Tunnel + + +@dataclass +class SearchState: + """Represents the complete state for a delivery search problem.""" + grid: Grid + stores: List[Store] + destinations: List[Destination] + tunnels: List[Tunnel] + + def get_tunnel_at(self, pos: Tuple[int, int]) -> Optional[Tunnel]: + """Get tunnel with entrance at given position.""" + for tunnel in self.tunnels: + if tunnel.has_entrance_at(pos): + return tunnel + return None + + def to_dict(self) -> dict: + return { + "grid": self.grid.to_dict(), + "stores": [s.to_dict() for s in self.stores], + "destinations": [d.to_dict() for d in self.destinations], + "tunnels": [t.to_dict() for t in self.tunnels] + } + + +@dataclass +class PathResult: + """Result of finding a path from start to goal.""" + plan: str # Comma-separated actions: "up,down,left,right,tunnel" + cost: float # Total traffic cost + nodes_expanded: int # Number of nodes expanded during search + path: List[Tuple[int, int]] = field(default_factory=list) # Actual positions in path + + def to_string(self) -> str: + """Format as required: plan;cost;nodesExpanded""" + return f"{self.plan};{self.cost};{self.nodes_expanded}" + + def to_dict(self) -> dict: + return { + "plan": self.plan, + "cost": self.cost, + "nodes_expanded": self.nodes_expanded, + "path": [{"x": p[0], "y": p[1]} for p in self.path] + } + + +@dataclass +class DeliveryAssignment: + """Assignment of a destination to a store/truck.""" + store_id: int + destination_id: int + path_result: PathResult + + def to_dict(self) -> dict: + return { + "store_id": self.store_id, + "destination_id": self.destination_id, + "path": self.path_result.to_dict() + } + + +@dataclass +class PlanResult: + """Result of the complete delivery planning.""" + assignments: List[DeliveryAssignment] + total_cost: float + total_nodes_expanded: int + + def to_string(self) -> str: + """Format output as specified.""" + parts = [] + for assignment in self.assignments: + parts.append( + f"({assignment.store_id},{assignment.destination_id}):{assignment.path_result.to_string()}" + ) + return ";".join(parts) + + def to_dict(self) -> dict: + return { + "assignments": [a.to_dict() for a in self.assignments], + "total_cost": self.total_cost, + "total_nodes_expanded": self.total_nodes_expanded + } + + +@dataclass +class SearchStep: + """Represents a single step in the search process for visualization.""" + step_number: int + current_node: Tuple[int, int] + action: Optional[str] + frontier: List[Tuple[int, int]] + explored: List[Tuple[int, int]] + current_path: List[Tuple[int, int]] + path_cost: float + + def to_dict(self) -> dict: + return { + "stepNumber": self.step_number, + "currentNode": {"x": self.current_node[0], "y": self.current_node[1]}, + "action": self.action, + "frontier": [{"x": p[0], "y": p[1]} for p in self.frontier], + "explored": [{"x": p[0], "y": p[1]} for p in self.explored], + "currentPath": [{"x": p[0], "y": p[1]} for p in self.current_path], + "pathCost": self.path_cost + } + + +@dataclass +class SearchMetrics: + """Performance metrics for a search execution.""" + runtime_ms: float + memory_mb: float + cpu_percent: float + nodes_expanded: int + path_cost: float + path_length: int + + def to_dict(self) -> dict: + return { + "runtime_ms": self.runtime_ms, + "memory_mb": self.memory_mb, + "cpu_percent": self.cpu_percent, + "nodes_expanded": self.nodes_expanded, + "path_cost": self.path_cost, + "path_length": self.path_length + } diff --git a/backend/app/services/__init__.py b/backend/app/services/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9802be9d6d1a0571b3316ac0743c8a019e166bd0 --- /dev/null +++ b/backend/app/services/__init__.py @@ -0,0 +1,28 @@ +"""Services package.""" +from .parser import ( + parse_initial_state, + parse_traffic, + parse_full_state, + format_initial_state, + format_traffic, +) +from .grid_generator import gen_grid +from .metrics import ( + MetricsCollector, + measure_performance, + run_with_metrics, + format_metrics, +) + +__all__ = [ + "parse_initial_state", + "parse_traffic", + "parse_full_state", + "format_initial_state", + "format_traffic", + "gen_grid", + "MetricsCollector", + "measure_performance", + "run_with_metrics", + "format_metrics", +] diff --git a/backend/app/services/grid_generator.py b/backend/app/services/grid_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..834c36e035e9372426de611215140bcda03ca461 --- /dev/null +++ b/backend/app/services/grid_generator.py @@ -0,0 +1,281 @@ +"""Grid generator service for random grid creation.""" +import random +from typing import Tuple, List, Set, Optional +from ..models.grid import Grid +from ..models.entities import Store, Destination, Tunnel +from ..models.state import SearchState +from .parser import format_initial_state, format_traffic + + +def gen_grid( + width: Optional[int] = None, + height: Optional[int] = None, + num_stores: Optional[int] = None, + num_destinations: Optional[int] = None, + num_tunnels: Optional[int] = None, + obstacle_density: float = 0.1, + seed: Optional[int] = None +) -> Tuple[str, str, SearchState]: + """ + Randomly generate a valid grid configuration. + + Args: + width: Grid width (random 5-15 if None) + height: Grid height (random 5-15 if None) + num_stores: Number of stores (random 1-3 if None) + num_destinations: Number of destinations (random 1-10 if None) + num_tunnels: Number of tunnels (random 0-5 if None) + obstacle_density: Fraction of segments to block (0.0-0.5) + seed: Random seed for reproducibility + + Returns: + Tuple of (initial_state_string, traffic_string, SearchState) + """ + if seed is not None: + random.seed(seed) + + # Set defaults + width = width or random.randint(5, 15) + height = height or random.randint(5, 15) + num_stores = num_stores or random.randint(1, 3) + num_destinations = num_destinations or random.randint(1, min(10, width * height // 4)) + num_tunnels = num_tunnels or random.randint(0, min(5, width * height // 10)) + + # Validate constraints + num_stores = min(num_stores, 3) + num_destinations = min(num_destinations, 10) + + # Track occupied positions + occupied: Set[Tuple[int, int]] = set() + + # Generate stores + stores = _generate_stores(width, height, num_stores, occupied) + + # Generate destinations + destinations = _generate_destinations(width, height, num_destinations, occupied) + + # Generate tunnels + tunnels = _generate_tunnels(width, height, num_tunnels, occupied) + + # Generate grid with traffic + grid = _generate_traffic(width, height, obstacle_density, stores, destinations) + + # Create search state + state = SearchState(grid=grid, stores=stores, destinations=destinations, tunnels=tunnels) + + # Format strings + initial_state = format_initial_state(width, height, stores, destinations, tunnels) + traffic = format_traffic(grid) + + return initial_state, traffic, state + + +def _generate_stores( + width: int, + height: int, + num_stores: int, + occupied: Set[Tuple[int, int]] +) -> List[Store]: + """Generate store positions at corners/edges.""" + stores = [] + + # Prefer corners + corners = [ + (0, 0), + (width - 1, 0), + (0, height - 1), + (width - 1, height - 1), + ] + random.shuffle(corners) + + for i, pos in enumerate(corners[:num_stores]): + stores.append(Store(id=i + 1, position=pos)) + occupied.add(pos) + + # If need more, use edges + if len(stores) < num_stores: + edges = [] + for x in range(1, width - 1): + edges.append((x, 0)) + edges.append((x, height - 1)) + for y in range(1, height - 1): + edges.append((0, y)) + edges.append((width - 1, y)) + + random.shuffle(edges) + for pos in edges: + if pos not in occupied and len(stores) < num_stores: + stores.append(Store(id=len(stores) + 1, position=pos)) + occupied.add(pos) + + return stores + + +def _generate_destinations( + width: int, + height: int, + num_destinations: int, + occupied: Set[Tuple[int, int]] +) -> List[Destination]: + """Generate random destination positions.""" + destinations = [] + + # Try to spread destinations across the grid + available = [] + for x in range(width): + for y in range(height): + if (x, y) not in occupied: + available.append((x, y)) + + random.shuffle(available) + + for i, pos in enumerate(available[:num_destinations]): + destinations.append(Destination(id=i + 1, position=pos)) + occupied.add(pos) + + return destinations + + +def _generate_tunnels( + width: int, + height: int, + num_tunnels: int, + occupied: Set[Tuple[int, int]] +) -> List[Tunnel]: + """Generate random tunnel pairs.""" + tunnels = [] + + # Find available positions for tunnel entrances + available = [] + for x in range(width): + for y in range(height): + if (x, y) not in occupied: + available.append((x, y)) + + random.shuffle(available) + + # Need at least 2 positions per tunnel + for i in range(min(num_tunnels, len(available) // 2)): + entrance1 = available[i * 2] + entrance2 = available[i * 2 + 1] + + # Ensure tunnels are useful (span reasonable distance) + dist = abs(entrance1[0] - entrance2[0]) + abs(entrance1[1] - entrance2[1]) + if dist >= 3: # Only create if Manhattan distance >= 3 + tunnels.append(Tunnel(entrance1=entrance1, entrance2=entrance2)) + occupied.add(entrance1) + occupied.add(entrance2) + + return tunnels + + +def _generate_traffic( + width: int, + height: int, + obstacle_density: float, + stores: List[Store], + destinations: List[Destination] +) -> Grid: + """ + Generate traffic levels for all segments. + + Ensures connectivity between stores and destinations. + """ + grid = Grid(width=width, height=height) + + # First, add all segments with random traffic + all_segments = [] + + # Horizontal segments + for x in range(width - 1): + for y in range(height): + all_segments.append(((x, y), (x + 1, y))) + + # Vertical segments + for x in range(width): + for y in range(height - 1): + all_segments.append(((x, y), (x, y + 1))) + + # Add segments with traffic + for src, dst in all_segments: + # Random traffic level 1-4 or blocked (0) + if random.random() < obstacle_density: + traffic = 0 # Blocked + else: + traffic = random.randint(1, 4) + grid.add_segment(src, dst, traffic) + + # Ensure connectivity - make sure there's a path from each store to each destination + _ensure_connectivity(grid, stores, destinations) + + return grid + + +def _ensure_connectivity( + grid: Grid, + stores: List[Store], + destinations: List[Destination] +) -> None: + """ + Ensure the grid is connected between stores and destinations. + + Uses BFS to check connectivity and unblocks segments if needed. + """ + # Get all important positions + important_positions = [s.position for s in stores] + [d.position for d in destinations] + + if len(important_positions) < 2: + return + + # Check connectivity from first store to all destinations + start = stores[0].position if stores else important_positions[0] + + # BFS to find reachable positions + visited = {start} + queue = [start] + + while queue: + current = queue.pop(0) + for neighbor in grid.get_neighbors(current): + if neighbor not in visited: + visited.add(neighbor) + queue.append(neighbor) + + # Check if all important positions are reachable + unreachable = [pos for pos in important_positions if pos not in visited] + + # If some positions are unreachable, create paths to them + for pos in unreachable: + _create_path_to(grid, start, pos, visited) + + +def _create_path_to( + grid: Grid, + start: Tuple[int, int], + goal: Tuple[int, int], + visited: Set[Tuple[int, int]] +) -> None: + """Create a path from visited area to goal by unblocking segments.""" + # Simple approach: find closest visited cell to goal and unblock path + closest = min(visited, key=lambda p: abs(p[0] - goal[0]) + abs(p[1] - goal[1])) + + # Create path from closest to goal + current = closest + while current != goal: + dx = 0 if goal[0] == current[0] else (1 if goal[0] > current[0] else -1) + dy = 0 if goal[1] == current[1] else (1 if goal[1] > current[1] else -1) + + # Prefer moving in direction with larger difference + if abs(goal[0] - current[0]) >= abs(goal[1] - current[1]) and dx != 0: + next_pos = (current[0] + dx, current[1]) + elif dy != 0: + next_pos = (current[0], current[1] + dy) + else: + next_pos = (current[0] + dx, current[1]) + + # Unblock segment if blocked + if grid.is_blocked(current, next_pos): + grid.add_segment(current, next_pos, random.randint(1, 4)) + + visited.add(next_pos) + current = next_pos diff --git a/backend/app/services/metrics.py b/backend/app/services/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..1344d8a5c1e9dee17952ba214cb9aa3731cf0c5b --- /dev/null +++ b/backend/app/services/metrics.py @@ -0,0 +1,126 @@ +"""Performance metrics collection service.""" +import time +import psutil +from contextlib import contextmanager +from dataclasses import dataclass +from typing import Generator, Callable, Any, Tuple +from ..models.state import SearchMetrics + + +@dataclass +class MetricsCollector: + """Collects performance metrics during search execution.""" + + def __init__(self): + self.start_time: float = 0 + self.end_time: float = 0 + self.start_memory: int = 0 + self.end_memory: int = 0 + self.peak_memory: int = 0 + self.cpu_samples: list = [] + self._process = psutil.Process() + + def start(self) -> None: + """Start collecting metrics.""" + self.start_time = time.perf_counter() + self.start_memory = self._process.memory_info().rss + self.peak_memory = self.start_memory + self.cpu_samples = [] + # Initial CPU sample + self._process.cpu_percent() + + def sample(self) -> None: + """Take a sample of current metrics.""" + current_memory = self._process.memory_info().rss + self.peak_memory = max(self.peak_memory, current_memory) + self.cpu_samples.append(self._process.cpu_percent()) + + def stop(self) -> None: + """Stop collecting metrics.""" + self.end_time = time.perf_counter() + self.end_memory = self._process.memory_info().rss + # Final CPU sample + self.cpu_samples.append(self._process.cpu_percent()) + + @property + def runtime_ms(self) -> float: + """Get runtime in milliseconds.""" + return (self.end_time - self.start_time) * 1000 + + @property + def memory_mb(self) -> float: + """Get peak memory usage in MB.""" + return (self.peak_memory - self.start_memory) / (1024 * 1024) + + @property + def cpu_percent(self) -> float: + """Get average CPU percentage.""" + if not self.cpu_samples: + return 0.0 + return sum(self.cpu_samples) / len(self.cpu_samples) + + def to_metrics(self, nodes_expanded: int, path_cost: float, path_length: int) -> SearchMetrics: + """Convert to SearchMetrics object.""" + return SearchMetrics( + runtime_ms=self.runtime_ms, + memory_mb=max(0, self.memory_mb), # Ensure non-negative + cpu_percent=self.cpu_percent, + nodes_expanded=nodes_expanded, + path_cost=path_cost, + path_length=path_length + ) + + +@contextmanager +def measure_performance() -> Generator[MetricsCollector, None, None]: + """ + Context manager for measuring search performance. + + Usage: + with measure_performance() as metrics: + result = search.solve(strategy) + print(f"Runtime: {metrics.runtime_ms}ms") + """ + collector = MetricsCollector() + collector.start() + try: + yield collector + finally: + collector.stop() + + +def run_with_metrics( + func: Callable[..., Any], + *args, + **kwargs +) -> Tuple[Any, MetricsCollector]: + """ + Run a function and collect performance metrics. + + Args: + func: Function to run + *args: Positional arguments for func + **kwargs: Keyword arguments for func + + Returns: + Tuple of (function result, MetricsCollector) + """ + collector = MetricsCollector() + collector.start() + try: + result = func(*args, **kwargs) + finally: + collector.stop() + return result, collector + + +def format_metrics(metrics: SearchMetrics) -> str: + """Format metrics for display.""" + return ( + f"Runtime: {metrics.runtime_ms:.2f}ms | " + f"Memory: {metrics.memory_mb:.2f}MB | " + f"CPU: {metrics.cpu_percent:.1f}% | " + f"Nodes: {metrics.nodes_expanded} | " + f"Cost: {metrics.path_cost} | " + f"Path Length: {metrics.path_length}" + ) diff --git a/backend/app/services/parser.py b/backend/app/services/parser.py new file mode 100644 index 0000000000000000000000000000000000000000..a6f6cb88e6c322e0572ea376b0c1a37247141e94 --- /dev/null +++ b/backend/app/services/parser.py @@ -0,0 +1,249 @@ +"""Parser service for initial state and traffic strings.""" +from typing import Tuple, List +from ..models.grid import Grid +from ..models.entities import Store, Destination, Tunnel +from ..models.state import SearchState + + +def parse_initial_state(initial_state: str) -> Tuple[int, int, List[Store], List[Destination], List[Tunnel]]: + """ + Parse the initial state string. + + Format: + m;n;P;S;CustomerX_1,CustomerY_1,CustomerX_2,CustomerY_2,...; + TunnelX_1,TunnelY_1,TunnelX_1',TunnelY_1',TunnelX_2,TunnelY_2,TunnelX_2',TunnelY_2',... + + Args: + initial_state: The initial state string + + Returns: + Tuple of (width, height, stores, destinations, tunnels) + """ + parts = initial_state.strip().split(';') + + # Grid dimensions + width = int(parts[0]) # m + height = int(parts[1]) # n + + # Number of packages/customers and stores + num_packages = int(parts[2]) # P + num_stores = int(parts[3]) # S + + # Parse customer locations + destinations: List[Destination] = [] + if len(parts) > 4 and parts[4]: + customer_coords = parts[4].split(',') + for i in range(0, len(customer_coords), 2): + if i + 1 < len(customer_coords): + x = int(customer_coords[i]) + y = int(customer_coords[i + 1]) + dest_id = len(destinations) + 1 + destinations.append(Destination(id=dest_id, position=(x, y))) + + # Parse tunnel locations + tunnels: List[Tunnel] = [] + if len(parts) > 5 and parts[5]: + tunnel_coords = parts[5].split(',') + for i in range(0, len(tunnel_coords), 4): + if i + 3 < len(tunnel_coords): + x1 = int(tunnel_coords[i]) + y1 = int(tunnel_coords[i + 1]) + x2 = int(tunnel_coords[i + 2]) + y2 = int(tunnel_coords[i + 3]) + tunnels.append(Tunnel(entrance1=(x1, y1), entrance2=(x2, y2))) + + # Generate stores (positions need to be provided or generated) + # For now, place stores at corners/edges + stores: List[Store] = [] + store_positions = _generate_store_positions(width, height, num_stores, destinations, tunnels) + for i, pos in enumerate(store_positions): + stores.append(Store(id=i + 1, position=pos)) + + return width, height, stores, destinations, tunnels + + +def _generate_store_positions( + width: int, + height: int, + num_stores: int, + destinations: List[Destination], + tunnels: List[Tunnel] +) -> List[Tuple[int, int]]: + """ + Generate store positions avoiding conflicts. + + Places stores at corners and edges of the grid. + """ + occupied = set() + for dest in destinations: + occupied.add(dest.position) + for tunnel in tunnels: + occupied.add(tunnel.entrance1) + occupied.add(tunnel.entrance2) + + # Preferred positions (corners first, then edges) + preferred = [ + (0, 0), + (width - 1, 0), + (0, height - 1), + (width - 1, height - 1), + (width // 2, 0), + (0, height // 2), + (width - 1, height // 2), + (width // 2, height - 1), + ] + + positions = [] + for pos in preferred: + if pos not in occupied and len(positions) < num_stores: + positions.append(pos) + occupied.add(pos) + + # If still need more positions, find any valid position + if len(positions) < num_stores: + for x in range(width): + for y in range(height): + if (x, y) not in occupied and len(positions) < num_stores: + positions.append((x, y)) + occupied.add((x, y)) + + return positions + + +def parse_traffic(traffic_str: str, width: int, height: int) -> Grid: + """ + Parse the traffic string and create a Grid. + + Format: + SrcX_1,SrcY_1,DstX_1,DstY_1,Traffic_1;SrcX_2,SrcY_2,DstX_2,DstY_2,Traffic_2;... + + Args: + traffic_str: Traffic string + width: Grid width + height: Grid height + + Returns: + Grid with traffic information + """ + grid = Grid(width=width, height=height) + + if not traffic_str: + # Initialize all segments with default traffic level 1 + _initialize_default_traffic(grid) + return grid + + segments = traffic_str.strip().split(';') + for segment in segments: + if not segment: + continue + parts = segment.split(',') + if len(parts) >= 5: + src_x = int(parts[0]) + src_y = int(parts[1]) + dst_x = int(parts[2]) + dst_y = int(parts[3]) + traffic = int(parts[4]) + grid.add_segment((src_x, src_y), (dst_x, dst_y), traffic) + + return grid + + +def _initialize_default_traffic(grid: Grid, default_traffic: int = 1) -> None: + """ + Initialize all grid segments with default traffic. + + Creates horizontal and vertical segments between adjacent cells. + """ + for x in range(grid.width): + for y in range(grid.height): + # Horizontal segment (right) + if x + 1 < grid.width: + grid.add_segment((x, y), (x + 1, y), default_traffic) + # Vertical segment (up) + if y + 1 < grid.height: + grid.add_segment((x, y), (x, y + 1), default_traffic) + + +def parse_full_state(initial_state: str, traffic_str: str) -> SearchState: + """ + Parse both initial state and traffic into a complete SearchState. + + Args: + initial_state: Initial state string + traffic_str: Traffic string + + Returns: + Complete SearchState object + """ + width, height, stores, destinations, tunnels = parse_initial_state(initial_state) + grid = parse_traffic(traffic_str, width, height) + + return SearchState( + grid=grid, + stores=stores, + destinations=destinations, + tunnels=tunnels + ) + + +def format_initial_state( + width: int, + height: int, + stores: List[Store], + destinations: List[Destination], + tunnels: List[Tunnel] +) -> str: + """ + Format state back into initial state string. + + Args: + width: Grid width + height: Grid height + stores: List of stores + destinations: List of destinations + tunnels: List of tunnels + + Returns: + Formatted initial state string + """ + parts = [ + str(width), + str(height), + str(len(destinations)), + str(len(stores)), + ] + + # Customer coordinates + customer_coords = [] + for dest in destinations: + customer_coords.extend([str(dest.position[0]), str(dest.position[1])]) + parts.append(','.join(customer_coords)) + + # Tunnel coordinates + tunnel_coords = [] + for 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a/frontend/.dockerignore b/frontend/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..49ad59ef56376857361f2d98f99032a5d9332142 --- /dev/null +++ b/frontend/.dockerignore @@ -0,0 +1,34 @@ +# Dependencies +node_modules/ + +# Build output +dist/ +build/ + +# IDE +.vscode/ +.idea/ +*.swp +*.swo + +# Testing +coverage/ +.nyc_output/ + +# OS +.DS_Store +Thumbs.db + +# Logs +*.log +npm-debug.log* + +# Environment +.env +.env.* +!.env.example + +# Cache +.cache/ +.parcel-cache/ +.turbo/ diff --git a/frontend/.env.example b/frontend/.env.example new file mode 100644 index 0000000000000000000000000000000000000000..19ccda1c5894cb6b8ae417a78eb62e089bd8e808 --- /dev/null +++ b/frontend/.env.example @@ -0,0 +1,3 @@ +# API URL for development +# Leave empty for production (uses relative URLs) +VITE_API_URL=http://localhost:8000 diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..a547bf36d8d11a4f89c59c144f24795749086dd1 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d2e77611fd3d959fee0487c41bd27b318be32b04 --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,73 @@ +# React + TypeScript + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh + +## React Compiler + +The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation). + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules: + +```js +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + + // Remove tseslint.configs.recommended and replace with this + tseslint.configs.recommendedTypeChecked, + // Alternatively, use this for stricter rules + tseslint.configs.strictTypeChecked, + // Optionally, add this for stylistic rules + tseslint.configs.stylisticTypeChecked, + + // Other configs... + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` + +You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules: + +```js +// eslint.config.js +import reactX from 'eslint-plugin-react-x' +import reactDom from 'eslint-plugin-react-dom' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + // Enable lint rules for React + reactX.configs['recommended-typescript'], + // Enable lint rules for React DOM + reactDom.configs.recommended, + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` diff --git a/frontend/components.json b/frontend/components.json new file mode 100644 index 0000000000000000000000000000000000000000..2b0833f0977df39fe315f4da463ce4cf2754815f --- /dev/null +++ b/frontend/components.json @@ -0,0 +1,22 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "new-york", + "rsc": false, + "tsx": true, + "tailwind": { + "config": "", + "css": "src/index.css", + "baseColor": "neutral", + "cssVariables": true, + "prefix": "" + }, + "iconLibrary": "lucide", + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + }, + "registries": {} +} diff --git a/frontend/eslint.config.js b/frontend/eslint.config.js new file mode 100644 index 0000000000000000000000000000000000000000..5e6b472f583e34a1cca751440d4f241495475723 --- /dev/null +++ b/frontend/eslint.config.js @@ -0,0 +1,23 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + js.configs.recommended, + tseslint.configs.recommended, + reactHooks.configs.flat.recommended, + reactRefresh.configs.vite, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + }, +]) diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000000000000000000000000000000000000..072a57e8e46c28ad65b5b51b3405b16c37d91667 --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + frontend + + +
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--git a/frontend/package.json b/frontend/package.json new file mode 100644 index 0000000000000000000000000000000000000000..9d14d7499367a50e5f725d5cb80698920747d73f --- /dev/null +++ b/frontend/package.json @@ -0,0 +1,41 @@ +{ + "name": "frontend", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc -b && vite build", + "lint": "eslint .", + "preview": "vite preview" + }, + "dependencies": { + "@tailwindcss/vite": "^4.1.17", + "@tanstack/react-query": "^5.90.11", + "axios": "^1.13.2", + "class-variance-authority": "^0.7.1", + "clsx": "^2.1.1", + "lucide-react": "^0.555.0", + "react": "^19.2.0", + "react-dom": "^19.2.0", + "recharts": "^3.5.1", + "tailwind-merge": "^3.4.0", + "tailwindcss": "^4.1.17", + "zustand": "^5.0.8" + }, + "devDependencies": { + "@eslint/js": "^9.39.1", + "@types/node": "^24.10.1", + "@types/react": "^19.2.5", + "@types/react-dom": "^19.2.3", + "@vitejs/plugin-react": "^5.1.1", + "eslint": "^9.39.1", + "eslint-plugin-react-hooks": "^7.0.1", + "eslint-plugin-react-refresh": "^0.4.24", + "globals": "^16.5.0", + "tw-animate-css": "^1.4.0", + "typescript": "~5.9.3", + "typescript-eslint": "^8.46.4", + "vite": "^7.2.4" + } +} diff --git a/frontend/public/vite.svg b/frontend/public/vite.svg new file mode 100644 index 0000000000000000000000000000000000000000..e7b8dfb1b2a60bd50538bec9f876511b9cac21e3 --- /dev/null +++ b/frontend/public/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/App.css b/frontend/src/App.css new file mode 100644 index 0000000000000000000000000000000000000000..b9d355df2a5956b526c004531b7b0ffe412461e0 --- /dev/null +++ b/frontend/src/App.css @@ -0,0 +1,42 @@ +#root { + max-width: 1280px; + margin: 0 auto; + padding: 2rem; + text-align: center; +} + +.logo { + height: 6em; + padding: 1.5em; + will-change: filter; + transition: filter 300ms; +} +.logo:hover { + filter: drop-shadow(0 0 2em #646cffaa); +} +.logo.react:hover { + filter: drop-shadow(0 0 2em #61dafbaa); +} + +@keyframes logo-spin { + from { + transform: rotate(0deg); + } + to { + transform: rotate(360deg); + } +} + +@media (prefers-reduced-motion: no-preference) { + a:nth-of-type(2) .logo { + animation: logo-spin infinite 20s linear; + } +} + +.card { + padding: 2em; +} + +.read-the-docs { + color: #888; +} diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx new file mode 100644 index 0000000000000000000000000000000000000000..6d3ef4f39372779f2437d7b5eb39d2c65d2e67d1 --- /dev/null +++ b/frontend/src/App.tsx @@ -0,0 +1,262 @@ +import { useState, useEffect } from 'react'; +import { useGridStore } from './store/gridStore'; +import { Grid } from './components/Grid/Grid'; +import { GridGenerator } from './components/Controls/GridGenerator'; +import { AlgorithmSelector } from './components/Controls/AlgorithmSelector'; +import { PlaybackControls } from './components/Controls/PlaybackControls'; +import { MetricsPanel } from './components/Stats/MetricsPanel'; +import { ComparisonDashboard } from './components/Stats/ComparisonDashboard'; +import { GroupInfo } from './components/Info/GroupInfo'; +import { PlanResultsModal } from './components/Stats/PlanResultsModal'; +import { Button } from './components/ui/button'; +import { + ChevronLeft, + ChevronRight, + Play, + FileText, + Eye, + BarChart3, + Truck, + AlertCircle, + Users, + Circle, + Square, + X, +} from 'lucide-react'; + +type Tab = 'visualize' | 'compare' | 'info'; + +function App() { + const [activeTab, setActiveTab] = useState('visualize'); + const [sidebarCollapsed, setSidebarCollapsed] = useState(false); + const [showPlanModal, setShowPlanModal] = useState(false); + const { + grid, + planResult, + runPlan, + runSearch, + isLoading, + error, + } = useGridStore(); + + // Open modal when planResult is updated + useEffect(() => { + if (planResult) { + setShowPlanModal(true); + } + }, [planResult]); + + const handleRunSearch = () => { + if (!grid || grid.stores.length === 0 || grid.destinations.length === 0) return; + const start = grid.stores[0].position; + const goal = grid.destinations[0].position; + runSearch(start, goal); + }; + + const handleRunPlan = async () => { + await runPlan(); + }; + + return ( +
+ {/* Header */} +
+
+
+
+ +
+
+

Pathfinding Visualizer

+

Search Algorithm Analysis

+
+
+
+ + {/* Tab navigation */} +
+ + + +
+ +
+ {grid && ( + <> + + + + )} +
+
+ + {/* Main content */} +
+ {/* Sidebar */} + + + {/* Main area */} +
+ {/* Error display */} + {error && ( +
+ + {error} +
+ )} + + {/* Content */} +
+ {activeTab === 'visualize' ? ( + <> + + {/* Legend overlay */} +
+

Legend

+
+
+
+ + Current +
+
+ + Path +
+
+ + Frontier +
+
+ + Explored +
+
+
+
+ + Store +
+
+ + Destination +
+
+ + Tunnel +
+
+ + Blocked +
+
+
+

Edge Costs

+
+ 1 + 2 + 3 + 4 +
+
+
+
+ + ) : activeTab === 'compare' ? ( +
+ +
+ ) : ( + + )} +
+
+
+ + {/* Status bar */} +
+
+ {grid && ( + <> + Grid {grid.width}x{grid.height} + | + S:{grid.stores.length} + D:{grid.destinations.length} + T:{grid.tunnels.length} + + )} +
+
+ + Connected +
+
+ + {/* Plan Results Modal */} + setShowPlanModal(false)} + /> +
+ ); +} + +export default App; diff --git a/frontend/src/api/client.ts b/frontend/src/api/client.ts new file mode 100644 index 0000000000000000000000000000000000000000..37bebb60346212b7f863f2ced18b4008f3a79936 --- /dev/null +++ b/frontend/src/api/client.ts @@ -0,0 +1,146 @@ +import axios from 'axios'; +import type { + GridState, + GridConfig, + Algorithm, + SearchResult, + PlanResult, + ComparisonResult, + AlgorithmInfo, + Position, + Segment, + Tunnel, +} from '../types'; + +// Use environment variable or default to localhost for development +// In production (HF Spaces), use relative URLs since nginx proxies /api +const API_BASE = import.meta.env.VITE_API_URL || ''; + +const api = axios.create({ + baseURL: API_BASE, + headers: { + 'Content-Type': 'application/json', + }, +}); + +// Health check +export async function checkHealth(): Promise<{ status: string }> { + const response = await api.get('/api/health'); + return response.data; +} + +// List algorithms +export async function getAlgorithms(): Promise { + const response = await api.get('/api/algorithms'); + return response.data.algorithms; +} + +// Generate random grid +export async function generateGrid(config: GridConfig = {}): Promise<{ + initialState: string; + traffic: string; + parsed: GridState; +}> { + const response = await api.post('/api/grid/generate', { + width: config.width, + height: config.height, + num_stores: config.numStores, + num_destinations: config.numDestinations, + num_tunnels: config.numTunnels, + obstacle_density: config.obstacleDensity ?? 0.1, + }); + return { + initialState: response.data.initial_state, + traffic: response.data.traffic, + parsed: response.data.parsed, + }; +} + +// Find path +export async function findPath( + gridWidth: number, + gridHeight: number, + start: Position, + goal: Position, + segments: Segment[], + tunnels: Tunnel[], + strategy: Algorithm +): Promise { + const response = await api.post('/api/search/path', { + grid_width: gridWidth, + grid_height: gridHeight, + start, + goal, + segments: segments.map(s => ({ + src: s.src, + dst: s.dst, + traffic: s.traffic, + })), + tunnels: tunnels.map(t => ({ + entrance1: t.entrance1, + entrance2: t.entrance2, + })), + strategy, + }); + return { + plan: response.data.plan, + cost: response.data.cost, + nodesExpanded: response.data.nodes_expanded, + runtimeMs: response.data.runtime_ms, + memoryMb: response.data.memory_mb, + cpuPercent: response.data.cpu_percent, + path: response.data.path, + steps: response.data.steps, + }; +} + +// Create plan +export async function createPlan( + initialState: string, + traffic: string, + strategy: Algorithm, + visualize: boolean = false +): Promise { + const response = await api.post('/api/search/plan', { + initial_state: initialState, + traffic, + strategy, + visualize, + }); + return { + output: response.data.output, + assignments: response.data.assignments, + totalCost: response.data.total_cost, + totalNodesExpanded: response.data.total_nodes_expanded, + runtimeMs: response.data.runtime_ms, + memoryMb: response.data.memory_mb, + cpuPercent: response.data.cpu_percent, + }; +} + +// Compare algorithms +export async function compareAlgorithms( + initialState: string, + traffic: string +): Promise<{ comparisons: ComparisonResult[]; optimalCost: number }> { + const response = await api.post('/api/search/compare', { + initial_state: initialState, + traffic, + }); + return { + comparisons: response.data.comparisons.map((c: any) => ({ + algorithm: c.algorithm, + name: c.name, + plan: c.plan, + cost: c.cost, + nodesExpanded: c.nodes_expanded, + runtimeMs: c.runtime_ms, + memoryMb: c.memory_mb, + cpuPercent: c.cpu_percent, + isOptimal: c.is_optimal, + })), + optimalCost: response.data.optimal_cost, + }; +} + +export default api; diff --git a/frontend/src/assets/react.svg b/frontend/src/assets/react.svg new file mode 100644 index 0000000000000000000000000000000000000000..6c87de9bb3358469122cc991d5cf578927246184 --- /dev/null +++ b/frontend/src/assets/react.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/components/Controls/AlgorithmSelector.tsx b/frontend/src/components/Controls/AlgorithmSelector.tsx new file mode 100644 index 0000000000000000000000000000000000000000..55d479201905b890967b473e6c526c05e17a0be7 --- /dev/null +++ b/frontend/src/components/Controls/AlgorithmSelector.tsx @@ -0,0 +1,72 @@ +import React from 'react'; +import { useGridStore } from '../../store/gridStore'; +import { Card, CardContent, CardHeader, CardTitle } from '../ui/card'; +import { Button } from '../ui/button'; +import { Brain } from 'lucide-react'; +import type { Algorithm } from '../../types'; + +const ALGORITHMS: { code: Algorithm; name: string; category: 'uninformed' | 'informed' }[] = [ + { code: 'BF', name: 'Breadth-First', category: 'uninformed' }, + { code: 'DF', name: 'Depth-First', category: 'uninformed' }, + { code: 'ID', name: 'Iterative Deepening', category: 'uninformed' }, + { code: 'UC', name: 'Uniform Cost', category: 'uninformed' }, + { code: 'GR1', name: 'Greedy (Manhattan)', category: 'informed' }, + { code: 'GR2', name: 'Greedy (Euclidean)', category: 'informed' }, + { code: 'AS1', name: 'A* (Manhattan)', category: 'informed' }, + { code: 'AS2', name: 'A* (Tunnel)', category: 'informed' }, +]; + +export const AlgorithmSelector: React.FC = () => { + const { selectedAlgorithm, setAlgorithm } = useGridStore(); + + const uninformed = ALGORITHMS.filter(a => a.category === 'uninformed'); + const informed = ALGORITHMS.filter(a => a.category === 'informed'); + + return ( + + + + + Algorithm + + + +
+

Uninformed

+
+ {uninformed.map((algo) => ( + + ))} +
+
+ +
+

Informed

+
+ {informed.map((algo) => ( + + ))} +
+
+ + + ); +}; + +export default AlgorithmSelector; diff --git a/frontend/src/components/Controls/GridGenerator.tsx b/frontend/src/components/Controls/GridGenerator.tsx new file mode 100644 index 0000000000000000000000000000000000000000..ab370fa2310d14ed11d362253dbd61738748c903 --- /dev/null +++ b/frontend/src/components/Controls/GridGenerator.tsx @@ -0,0 +1,122 @@ +import React, { useState } from 'react'; +import { useGridStore } from '../../store/gridStore'; +import { Card, CardContent, CardHeader, CardTitle } from '../ui/card'; +import { Button } from '../ui/button'; +import { Settings, Shuffle } from 'lucide-react'; +import type { GridConfig } from '../../types'; + +export const GridGenerator: React.FC = () => { + const { generateNewGrid, isLoading } = useGridStore(); + const [config, setConfig] = useState({ + width: 8, + height: 8, + numStores: 2, + numDestinations: 3, + numTunnels: 2, + obstacleDensity: 0.1, + }); + + const handleGenerate = () => { + generateNewGrid(config); + }; + + return ( + + + + + Configuration + + + +
+
+ + setConfig({ ...config, width: parseInt(e.target.value) })} + className="w-full px-2.5 py-1.5 bg-zinc-800 border border-zinc-700 rounded text-zinc-200 text-sm font-mono focus:outline-none focus:border-zinc-600" + /> +
+ +
+ + setConfig({ ...config, height: parseInt(e.target.value) })} + className="w-full px-2.5 py-1.5 bg-zinc-800 border border-zinc-700 rounded text-zinc-200 text-sm font-mono focus:outline-none focus:border-zinc-600" + /> +
+ +
+ + setConfig({ ...config, numStores: parseInt(e.target.value) })} + className="w-full px-2.5 py-1.5 bg-zinc-800 border border-zinc-700 rounded text-zinc-200 text-sm font-mono focus:outline-none focus:border-zinc-600" + /> +
+ +
+ + setConfig({ ...config, numDestinations: parseInt(e.target.value) })} + className="w-full px-2.5 py-1.5 bg-zinc-800 border border-zinc-700 rounded text-zinc-200 text-sm font-mono focus:outline-none focus:border-zinc-600" + /> +
+ +
+ + setConfig({ ...config, numTunnels: parseInt(e.target.value) })} + className="w-full px-2.5 py-1.5 bg-zinc-800 border border-zinc-700 rounded text-zinc-200 text-sm font-mono focus:outline-none focus:border-zinc-600" + /> +
+ +
+ + setConfig({ ...config, obstacleDensity: parseFloat(e.target.value) })} + className="w-full px-2.5 py-1.5 bg-zinc-800 border border-zinc-700 rounded text-zinc-200 text-sm font-mono focus:outline-none focus:border-zinc-600" + /> +
+
+ + + + + ); +}; + +export default GridGenerator; diff --git a/frontend/src/components/Controls/PlaybackControls.tsx b/frontend/src/components/Controls/PlaybackControls.tsx new file mode 100644 index 0000000000000000000000000000000000000000..dc223ef2998074d79110f51ad05baddc79174099 --- /dev/null +++ b/frontend/src/components/Controls/PlaybackControls.tsx @@ -0,0 +1,139 @@ +import React, { useEffect } from 'react'; +import { useGridStore } from '../../store/gridStore'; +import { Card, CardContent, CardHeader, CardTitle } from '../ui/card'; +import { Button } from '../ui/button'; +import { Slider } from '../ui/slider'; +import { + Play, + Pause, + SkipBack, + SkipForward, + ChevronsLeft, + ChevronsRight, + Film, +} from 'lucide-react'; + +export const PlaybackControls: React.FC = () => { + const { + steps, + currentStep, + isPlaying, + playbackSpeed, + play, + pause, + reset, + nextStep, + prevStep, + setCurrentStep, + setSpeed, + } = useGridStore(); + + useEffect(() => { + if (!isPlaying) return; + + const interval = setInterval(() => { + nextStep(); + }, playbackSpeed); + + return () => clearInterval(interval); + }, [isPlaying, playbackSpeed, nextStep]); + + if (steps.length === 0) { + return null; + } + + const progress = ((currentStep + 1) / steps.length) * 100; + + return ( + + + + + Playback + + + + {/* Progress bar */} +
+
+
+
+
+ {currentStep + 1} + {steps.length} +
+
+ + {/* Control buttons */} +
+ + + + + +
+ + {/* Speed control */} + setSpeed(parseInt(e.target.value))} + /> + + {/* Current step info */} + {steps[currentStep] && ( +
+
+ Position +

+ ({steps[currentStep].currentNode.x}, {steps[currentStep].currentNode.y}) +

+
+
+ Cost +

+ {steps[currentStep].pathCost} +

+
+
+ Frontier +

+ {steps[currentStep].frontier.length} +

+
+
+ Explored +

+ {steps[currentStep].explored.length} +

+
+
+ )} + + + ); +}; + +export default PlaybackControls; diff --git a/frontend/src/components/Grid/Grid.tsx b/frontend/src/components/Grid/Grid.tsx new file mode 100644 index 0000000000000000000000000000000000000000..97618262259676063f958063616d3a0d52dbe375 --- /dev/null +++ b/frontend/src/components/Grid/Grid.tsx @@ -0,0 +1,533 @@ +import React, { useMemo } from 'react'; +import { useGridStore } from '../../store/gridStore'; +import { Map } from 'lucide-react'; +import type { SearchStep } from '../../types'; + +const CELL_SIZE = 90; +const PADDING = 70; +const NODE_RADIUS = 14; + +const COLORS = { + background: '#0a0a0b', + gridLine: '#1f1f23', + + // Node states + nodeDefault: '#27272a', + nodeDefaultStroke: '#3f3f46', + frontier: '#0ea5e9', + frontierLight: '#38bdf8', + explored: '#52525b', + exploredLight: '#71717a', + current: '#f59e0b', + currentLight: '#fbbf24', + path: '#10b981', + pathLight: '#34d399', + + // Entities + store: '#3b82f6', + storeLight: '#60a5fa', + destination: '#10b981', + destinationLight: '#34d399', + tunnel: '#a855f7', + tunnelLight: '#c084fc', + + // Edges + edge1: '#a1a1aa', + edge2: '#71717a', + edge3: '#52525b', + edge4: '#3f3f46', + blocked: '#ef4444', + blockedBg: '#450a0a', + + // Text + text: '#fafafa', + textMuted: '#a1a1aa', + textDim: '#52525b', +}; + +export const Grid: React.FC = () => { + const { grid, steps, currentStep, searchResult } = useGridStore(); + + const stepData = useMemo(() => { + const step: SearchStep | null = steps[currentStep] || null; + return { + step, + exploredSet: new Set(step?.explored.map(p => `${p.x},${p.y}`) || []), + frontierSet: new Set(step?.frontier.map(p => `${p.x},${p.y}`) || []), + pathSet: new Set(step?.currentPath.map(p => `${p.x},${p.y}`) || []), + finalPathSet: new Set((searchResult?.path || []).map(p => `${p.x},${p.y}`)), + }; + }, [steps, currentStep, searchResult]); + + if (!grid) { + return ( +
+
+
+ +
+

No Grid Generated

+

Configure and generate a grid from the sidebar

+
+
+ ); + } + + const width = grid.width * CELL_SIZE + PADDING * 2; + const height = grid.height * CELL_SIZE + PADDING * 2; + + const { step, exploredSet, frontierSet, pathSet, finalPathSet } = stepData; + + const getNodeState = (x: number, y: number): string => { + const key = `${x},${y}`; + if (step?.currentNode.x === x && step?.currentNode.y === y) return 'current'; + if (pathSet.has(key) || (!step && finalPathSet.has(key))) return 'path'; + if (frontierSet.has(key)) return 'frontier'; + if (exploredSet.has(key)) return 'explored'; + return 'default'; + }; + + const getEdgeStyle = (traffic: number) => { + if (traffic === 0) return { color: COLORS.blocked, width: 2, opacity: 0.4 }; + const colors = [COLORS.edge1, COLORS.edge2, COLORS.edge3, COLORS.edge4]; + const widths = [3, 2.5, 2, 1.5]; + return { color: colors[traffic - 1], width: widths[traffic - 1], opacity: 0.7 }; + }; + + const toSvgY = (gridY: number) => PADDING + (grid.height - 1 - gridY) * CELL_SIZE + CELL_SIZE / 2; + const toSvgX = (gridX: number) => PADDING + gridX * CELL_SIZE + CELL_SIZE / 2; + + return ( +
+ + + {/* Glows for different states */} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + {/* Tunnel gradient */} + + + + + + + {/* Blocked pattern */} + + + + + + {/* Background */} + + + {/* Grid cells background */} + {Array.from({ length: grid.width }).map((_, x) => + Array.from({ length: grid.height }).map((_, y) => ( + + )) + )} + + {/* Edges */} + {grid.segments.map((segment, i) => { + const x1 = toSvgX(segment.src.x); + const y1 = toSvgY(segment.src.y); + const x2 = toSvgX(segment.dst.x); + const y2 = toSvgY(segment.dst.y); + const midX = (x1 + x2) / 2; + const midY = (y1 + y2) / 2; + const isBlocked = segment.traffic === 0; + const style = getEdgeStyle(segment.traffic); + + if (isBlocked) { + return ( + + {/* Blocked edge line */} + + {/* Blocked indicator */} + + + + + + + ); + } + + return ( + + {/* Edge line */} + + {/* Cost badge */} + + + + {segment.traffic} + + + + ); + })} + + {/* Tunnels */} + {grid.tunnels.map((tunnel, i) => { + const x1 = toSvgX(tunnel.entrance1.x); + const y1 = toSvgY(tunnel.entrance1.y); + const x2 = toSvgX(tunnel.entrance2.x); + const y2 = toSvgY(tunnel.entrance2.y); + const midX = (x1 + x2) / 2; + const midY = (y1 + y2) / 2; + const dx = x2 - x1; + const dy = y2 - y1; + const len = Math.sqrt(dx * dx + dy * dy); + const curveOffset = Math.min(60, len / 2.5); + const perpX = -dy / len * curveOffset; + const perpY = dx / len * curveOffset; + + return ( + + {/* Tunnel glow background */} + + {/* Tunnel path */} + + {/* Animated dashes */} + + + + + {/* Portal entrance 1 */} + + + + + + + {/* Portal entrance 2 */} + + + + + + + {/* Cost label */} + + + + T:{tunnel.cost} + + + + ); + })} + + {/* Nodes */} + {Array.from({ length: grid.width }).map((_, x) => + Array.from({ length: grid.height }).map((_, y) => { + const cx = toSvgX(x); + const cy = toSvgY(y); + const state = getNodeState(x, y); + + let fill = COLORS.nodeDefault; + let stroke = COLORS.nodeDefaultStroke; + let filter = ''; + let radius = NODE_RADIUS; + let strokeWidth = 1.5; + + switch (state) { + case 'current': + fill = COLORS.current; + stroke = COLORS.currentLight; + filter = 'url(#glow-current)'; + radius = NODE_RADIUS + 2; + strokeWidth = 2; + break; + case 'path': + fill = COLORS.path; + stroke = COLORS.pathLight; + filter = 'url(#glow-path)'; + strokeWidth = 2; + break; + case 'frontier': + fill = COLORS.frontier; + stroke = COLORS.frontierLight; + filter = 'url(#glow-frontier)'; + break; + case 'explored': + fill = COLORS.explored; + stroke = COLORS.exploredLight; + break; + } + + return ( + + + {state === 'current' && ( + + + + + )} + + ); + }) + )} + + {/* Stores */} + {grid.stores.map((store) => { + const cx = toSvgX(store.position.x); + const cy = toSvgY(store.position.y); + + return ( + + + + + S{store.id} + + + ); + })} + + {/* Destinations */} + {grid.destinations.map((dest) => { + const cx = toSvgX(dest.position.x); + const cy = toSvgY(dest.position.y); + + return ( + + + {/* Target rings */} + + + + D{dest.id} + + + ); + })} + + {/* Axis labels */} + {Array.from({ length: grid.width }).map((_, x) => ( + + {x} + + ))} + {Array.from({ length: grid.height }).map((_, y) => ( + + {y} + + ))} + +
+ ); +}; + +export default Grid; diff --git a/frontend/src/components/Info/GroupInfo.tsx b/frontend/src/components/Info/GroupInfo.tsx new file mode 100644 index 0000000000000000000000000000000000000000..3548ac1fa0f5b36a641b6181555e8837f566c353 --- /dev/null +++ b/frontend/src/components/Info/GroupInfo.tsx @@ -0,0 +1,206 @@ +import React from 'react'; +import { BookOpen, Users, Code, FileText, GraduationCap, Calendar, Building } from 'lucide-react'; + +export const GroupInfo: React.FC = () => { + return ( +
+
+ {/* Paper-style header */} +
+

+ Artificial Intelligence Project +

+

+ Package Delivery Search Agent:
+ A Comparative Study of Informed and Uninformed Search Algorithms +

+
+ + Academic Year 2024-2025 +
+
+ + {/* Authors section */} +
+
+ +

Authors

+
+
+ {[ + { name: 'Kacem Mathlouthi', role: 'Developer' }, + { name: 'Mohamed Amine Houas', role: 'Developer' }, + { name: 'Oussema Kraiem', role: 'Developer' }, + { name: 'Alaeddine El Zaouali', role: 'Developer' }, + ].map((author) => ( +
+

{author.name}

+

{author.role}

+
+ ))} +
+
+ + {/* Abstract */} +
+
+ +

Abstract

+
+
+

+ This project presents an interactive visualization tool for comparing search algorithms in the context + of package delivery optimization. We implement and analyze six fundamental search strategies: Breadth-First + Search (BFS), Depth-First Search (DFS), Iterative Deepening Search (IDS), Uniform Cost Search (UCS), + Greedy Best-First Search, and A* Search. The system models a grid-based city environment with varying + traffic conditions, blocked roads, and tunnel shortcuts, providing a comprehensive testbed for evaluating + algorithm performance in terms of path optimality, computational efficiency, and memory utilization. +

+
+
+ + {/* Project Details */} +
+
+ +

Technical Implementation

+
+
+
+
+

Backend

+

Python 3.11 + FastAPI

+
+
+

Frontend

+

React 19 + TypeScript + Vite

+
+
+

State Management

+

Zustand

+
+
+
+
+

Visualization

+

SVG + Recharts

+
+
+

Styling

+

Tailwind CSS v4

+
+
+

Communication

+

REST API + WebSocket

+
+
+
+
+ + {/* Algorithms */} +
+
+ +

Implemented Algorithms

+
+
+ + + + + + + + + + + {[ + { name: 'Breadth-First Search', code: 'BFS', type: 'Uninformed', optimal: 'Yes*', complete: 'Yes' }, + { name: 'Depth-First Search', code: 'DFS', type: 'Uninformed', optimal: 'No', complete: 'No' }, + { name: 'Iterative Deepening', code: 'IDS', type: 'Uninformed', optimal: 'Yes*', complete: 'Yes' }, + { name: 'Uniform Cost Search', code: 'UCS', type: 'Uninformed', optimal: 'Yes', complete: 'Yes' }, + { name: 'Greedy Best-First', code: 'GBFS', type: 'Informed', optimal: 'No', complete: 'No' }, + { name: 'A* Search', code: 'A*', type: 'Informed', optimal: 'Yes', complete: 'Yes' }, + ].map((algo) => ( + + + + + + + ))} + +
AlgorithmTypeOptimalComplete
+ {algo.name} + ({algo.code}) + {algo.type}{algo.optimal}{algo.complete}
+
+

* When step costs are uniform

+
+ + {/* Heuristics */} +
+
+ +

Heuristic Functions

+
+
+
+

Manhattan Distance

+

h(n) = |xn - xg| + |yn - yg|

+
+
+

Euclidean Distance

+

h(n) = sqrt((xn - xg)2 + (yn - yg)2)

+
+
+

Tunnel-Aware Heuristic

+

Considers available tunnel shortcuts to improve estimate accuracy

+
+
+
+ + {/* References */} +
+
+ +

References

+
+
+
+ [1] +

+ Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. +

+
+
+ [2] +

+ Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100-107. +

+
+
+ [3] +

+ Korf, R. E. (1985). Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence, 27(1), 97-109. +

+
+
+
+ + {/* Footer */} +
+
+ + Department of Computer Science +
+

+ This project was developed as part of the Artificial Intelligence curriculum +

+
+
+
+ ); +}; + +export default GroupInfo; diff --git a/frontend/src/components/Stats/ComparisonDashboard.tsx b/frontend/src/components/Stats/ComparisonDashboard.tsx new file mode 100644 index 0000000000000000000000000000000000000000..1272d7fec216e389a6a33ed9fcc13b2f0413762d --- /dev/null +++ b/frontend/src/components/Stats/ComparisonDashboard.tsx @@ -0,0 +1,328 @@ +import React, { useMemo } from 'react'; +import { Bar, BarChart, XAxis, YAxis, CartesianGrid, Cell, ResponsiveContainer } from 'recharts'; +import { useGridStore } from '../../store/gridStore'; +import { Card, CardContent, CardHeader, CardTitle } from '../ui/card'; +import { Button } from '../ui/button'; +import { + BarChart3, + Activity, + Clock, + HardDrive, + Route, + Play, + RefreshCw, + Check, + FlaskConical, + Trophy, + Medal, + TrendingDown, +} from 'lucide-react'; + +export const ComparisonDashboard: React.FC = () => { + const { comparisonResults, runComparison, isLoading, grid } = useGridStore(); + + // Sort results by cost (best to worst) + const sortedResults = useMemo(() => { + if (!comparisonResults) return null; + return [...comparisonResults].sort((a, b) => { + if (a.cost === Infinity && b.cost === Infinity) return 0; + if (a.cost === Infinity) return 1; + if (b.cost === Infinity) return -1; + return a.cost - b.cost; + }); + }, [comparisonResults]); + + // Prepare sorted chart data for each metric + const chartDataByMetric = useMemo(() => { + if (!comparisonResults) return null; + + const baseData = comparisonResults.map((r) => ({ + name: r.algorithm, + fullName: r.name, + nodesExpanded: r.nodesExpanded, + runtime: r.runtimeMs, + cost: r.cost === Infinity ? 0 : r.cost, + memory: r.memoryMb, + isOptimal: r.isOptimal, + })); + + return { + nodes: [...baseData].sort((a, b) => b.nodesExpanded - a.nodesExpanded), + runtime: [...baseData].sort((a, b) => b.runtime - a.runtime), + cost: [...baseData].sort((a, b) => b.cost - a.cost), + memory: [...baseData].sort((a, b) => b.memory - a.memory), + }; + }, [comparisonResults]); + + if (!grid) { + return ( +
+
+ +

Generate a grid first

+

Then compare all algorithms

+
+
+ ); + } + + if (!comparisonResults) { + return ( +
+
+ +

Compare Search Algorithms

+ +
+
+ ); + } + + const getRankIcon = (index: number) => { + if (index === 0) return ; + if (index === 1) return ; + if (index === 2) return ; + return {index + 1}; + }; + + // Custom tooltip + const CustomTooltip = ({ active, payload, label }: any) => { + if (active && payload && payload.length) { + const data = payload[0].payload; + return ( +
+

{data.fullName}

+

+ {payload[0].name}: {typeof payload[0].value === 'number' ? payload[0].value.toLocaleString(undefined, { maximumFractionDigits: 2 }) : payload[0].value} +

+
+ ); + } + return null; + }; + + // Chart component with gradient + const GradientBarChart = ({ + data, + dataKey, + title, + icon: Icon, + gradientId, + unit = '' + }: { + data: any[]; + dataKey: string; + title: string; + icon: React.ElementType; + gradientId: string; + unit?: string; + }) => { + return ( + + + +
+ + {title} +
+
+ + Sorted high to low +
+ + + + + + + + + + + + + + + + + + + + value.toLocaleString()} + /> + + {data.map((entry, index) => { + // Highlight the best performer (lowest value for cost, or mark optimal) + const isBest = entry.isOptimal || (dataKey === 'cost' && entry[dataKey] === Math.min(...data.filter(d => d[dataKey] > 0).map(d => d[dataKey]))); + return ( + + ); + })} + + + + + + + ); + }; + + return ( +
+
+
+

Algorithm Comparison

+

8 algorithms on the same problem instance

+
+ +
+ + {/* Charts Grid */} +
+ + + + +
+ + {/* Results Table - Sorted by best to worst */} + + + + + Rankings (Best to Worst by Path Cost) + + + +
+ + + + + + + + + + + + + + {sortedResults?.map((result, index) => ( + + + + + + + + + + ))} + +
RankAlgorithmCostNodesRuntimeMemoryOptimal
+ {getRankIcon(index)} + + {result.name} + ({result.algorithm}) + + {result.cost === Infinity ? 'No path' : result.cost} + + {result.nodesExpanded.toLocaleString()} + + {result.runtimeMs.toFixed(2)}ms + + {result.memoryMb.toFixed(3)}MB + + {result.isOptimal && ( + + )} +
+
+ + +
+ ); +}; + +export default ComparisonDashboard; diff --git a/frontend/src/components/Stats/MetricsPanel.tsx b/frontend/src/components/Stats/MetricsPanel.tsx new file mode 100644 index 0000000000000000000000000000000000000000..32dd906befffe128ff5f7389bd7de35007c0cd33 --- /dev/null +++ b/frontend/src/components/Stats/MetricsPanel.tsx @@ -0,0 +1,74 @@ +import React from 'react'; +import { useGridStore } from '../../store/gridStore'; +import { Card, CardContent, CardHeader, CardTitle } from '../ui/card'; +import { Activity, Clock, HardDrive, Route } from 'lucide-react'; + +export const MetricsPanel: React.FC = () => { + const { searchResult, planResult } = useGridStore(); + + const result = searchResult || planResult; + + if (!result) { + return null; + } + + const metrics = [ + { + label: 'Cost', + value: 'cost' in result ? result.cost : result.totalCost, + icon: Route, + }, + { + label: 'Nodes', + value: 'nodesExpanded' in result ? result.nodesExpanded : result.totalNodesExpanded, + icon: Activity, + }, + { + label: 'Runtime', + value: `${result.runtimeMs.toFixed(1)}ms`, + icon: Clock, + }, + { + label: 'Memory', + value: `${result.memoryMb.toFixed(2)}MB`, + icon: HardDrive, + }, + ]; + + return ( + + + + + Results + + + +
+ {metrics.map((metric) => ( +
+
+ + {metric.label} +
+

+ {metric.value} +

+
+ ))} +
+ + {searchResult?.plan && ( +
+
Path
+
+ {searchResult.plan || 'No path found'} +
+
+ )} + + + ); +}; + +export default MetricsPanel; diff --git a/frontend/src/components/Stats/PlanResultsModal.tsx b/frontend/src/components/Stats/PlanResultsModal.tsx new file mode 100644 index 0000000000000000000000000000000000000000..5225ebd09ee2fd24edf91d63eadb50075c458c2d --- /dev/null +++ b/frontend/src/components/Stats/PlanResultsModal.tsx @@ -0,0 +1,172 @@ +import React from 'react'; +import { useGridStore } from '../../store/gridStore'; +import { Card, CardContent, CardHeader, CardTitle } from '../ui/card'; +import { Button } from '../ui/button'; +import { + X, + Truck, + Package, + Route, + Clock, + Activity, + HardDrive, + ArrowRight, + CheckCircle, +} from 'lucide-react'; + +interface PlanResultsModalProps { + isOpen: boolean; + onClose: () => void; +} + +export const PlanResultsModal: React.FC = ({ isOpen, onClose }) => { + const { planResult, selectedAlgorithm } = useGridStore(); + + if (!isOpen || !planResult) return null; + + return ( +
+ {/* Backdrop */} +
+ + {/* Modal */} +
+ {/* Header */} +
+
+
+ +
+
+

Delivery Plan Results

+

Algorithm: {selectedAlgorithm}

+
+
+ +
+ + {/* Content */} +
+ {/* Summary Stats */} +
+ + +
+ + Total Cost +
+

+ {planResult.totalCost} +

+ + + + +
+ + Nodes Expanded +
+

+ {planResult.totalNodesExpanded.toLocaleString()} +

+ + + + +
+ + Runtime +
+

+ {planResult.runtimeMs.toFixed(2)}ms +

+ + + + +
+ + Memory +
+

+ {planResult.memoryMb.toFixed(2)}MB +

+ + +
+ + {/* Plan Output */} + + + Plan Output + + +
+ {planResult.output || 'No output'} +
+ + + + {/* Assignments */} + + + + + Delivery Assignments ({planResult.assignments.length}) + + + + {planResult.assignments.length === 0 ? ( +

No assignments made

+ ) : ( + planResult.assignments.map((assignment, index) => ( +
+
+
+
+
+ S{assignment.store_id} +
+ +
+ D{assignment.destination_id} +
+
+ +
+
+ Cost: {assignment.path.cost} + Nodes: {assignment.path.nodes_expanded} +
+
+
+ Path: + {assignment.path.plan || 'Direct'} +
+
+ )) + )} + + +
+ + {/* Footer */} +
+ +
+
+
+ ); +}; + +export default PlanResultsModal; diff --git a/frontend/src/components/ui/badge.tsx b/frontend/src/components/ui/badge.tsx new file mode 100644 index 0000000000000000000000000000000000000000..aeceae5a233ab114589deca5fbb2b2839af5379d --- /dev/null +++ b/frontend/src/components/ui/badge.tsx @@ -0,0 +1,41 @@ +import * as React from "react" +import { cva, type VariantProps } from "class-variance-authority" +import { cn } from "@/lib/utils" + +const badgeVariants = cva( + "inline-flex items-center rounded-md border px-2 py-0.5 text-xs font-medium transition-colors focus:outline-none focus:ring-2 focus:ring-zinc-400 focus:ring-offset-2", + { + variants: { + variant: { + default: + "border-transparent bg-zinc-50 text-zinc-900", + secondary: + "border-transparent bg-zinc-800 text-zinc-50", + destructive: + "border-transparent bg-red-500/15 text-red-400 border-red-500/20", + outline: "text-zinc-50 border-zinc-700", + success: + "border-transparent bg-emerald-500/15 text-emerald-400 border-emerald-500/20", + warning: + "border-transparent bg-amber-500/15 text-amber-400 border-amber-500/20", + info: + "border-transparent bg-blue-500/15 text-blue-400 border-blue-500/20", + }, + }, + defaultVariants: { + variant: "default", + }, + } +) + +export interface BadgeProps + extends React.HTMLAttributes, + VariantProps {} + +function Badge({ className, variant, ...props }: BadgeProps) { + return ( +
+ ) +} + +export { Badge, badgeVariants } diff --git a/frontend/src/components/ui/button.tsx b/frontend/src/components/ui/button.tsx new file mode 100644 index 0000000000000000000000000000000000000000..851fea1b750e2f7f2a5bfb2590a722df059154b6 --- /dev/null +++ b/frontend/src/components/ui/button.tsx @@ -0,0 +1,57 @@ +import * as React from "react" +import { cva, type VariantProps } from "class-variance-authority" +import { cn } from "@/lib/utils" + +const buttonVariants = cva( + "inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-zinc-400 disabled:pointer-events-none disabled:opacity-50 [&_svg]:pointer-events-none [&_svg]:size-4 [&_svg]:shrink-0", + { + variants: { + variant: { + default: + "bg-zinc-50 text-zinc-900 shadow hover:bg-zinc-50/90", + destructive: + "bg-red-500/10 text-red-400 border border-red-500/20 hover:bg-red-500/20", + outline: + "border border-zinc-700 bg-transparent shadow-sm hover:bg-zinc-800 hover:text-zinc-50", + secondary: + "bg-zinc-800 text-zinc-50 shadow-sm hover:bg-zinc-700", + ghost: "hover:bg-zinc-800 hover:text-zinc-50", + link: "text-zinc-50 underline-offset-4 hover:underline", + primary: + "bg-blue-600 text-white shadow hover:bg-blue-500", + success: + "bg-emerald-600 text-white shadow hover:bg-emerald-500", + }, + size: { + default: "h-9 px-4 py-2", + sm: "h-8 rounded-md px-3 text-xs", + lg: "h-10 rounded-md px-8", + icon: "h-9 w-9", + xs: "h-7 rounded px-2 text-xs", + }, + }, + defaultVariants: { + variant: "default", + size: "default", + }, + } +) + +export interface ButtonProps + extends React.ButtonHTMLAttributes, + VariantProps {} + +const Button = React.forwardRef( + ({ className, variant, size, ...props }, ref) => { + return ( +