app3.py

import gradio as gr
import networkx as nx
import matplotlib.pyplot as plt
import random
import time
import json
import os
import shutil
from datetime import datetime

# ==========================================
# 1. JSON & HELPER LOGIC
# ==========================================
def get_sorted_nodes(G):
    """Returns nodes sorted by X, then Y. Matches JSON ID ordering."""
    return sorted(list(G.nodes()), key=lambda l: (l[0], l[1]))

def prepare_edges_for_json(G):
    nodes_list = get_sorted_nodes(G)
    nodes_list_dict = {}
    I = []
    for idx, node in enumerate(nodes_list):
        s_id = str(idx + 1)
        I.append(s_id)
        nodes_list_dict[s_id] = node

    coord_to_id = {v: k for k, v in nodes_list_dict.items()}
    edges_list = list(G.edges())
    edges_formatted = []

    for u, v in edges_list:
        if u in coord_to_id and v in coord_to_id:
            edges_formatted.append({
                "room1": coord_to_id[u],
                "room2": coord_to_id[v]
            })

    return edges_formatted, I, nodes_list_dict

def prepare_parameter_for_json(G, I, nodes_list_dict):
    n_count = len(G.nodes())
    weights = []
    for i in range(n_count):
        val = n_count / (n_count * (1 + (((i + 1) * 2) / 30)))
        weights.append(val)
        
    m_weights = random.choices(I, weights=weights, k=5)
    t_weights_probs = []
    for i in range(10):
        val = n_count / (n_count * (1 + (((i + 1) * 2) / 5)))
        t_weights_probs.append(val)
    t_weights = random.choices(range(1, 11), weights=t_weights_probs, k=5)

    dismantled = []
    conditioningDuration = []
    assignment = []
    help_list = []

    for m in range(5):
        dismantled.append({"m": str(m + 1), "i": str(m_weights[m]), "t": t_weights[m], "value": 1})
        conditioningDuration.append({"m": str(m + 1), "value": 1})
        x = random.randint(1, 3)
        if m > 2:
            if 1 not in help_list: x = 1
            if 2 not in help_list: x = 2
            if 3 not in help_list: x = 3
        help_list.append(x)
        assignment.append({"m": str(m + 1), "r": str(x), "value": 1})

    t_weights_del = random.choices(range(1, 11), weights=t_weights_probs[:10], k=3)
    delivered = []
    conditioningCapacity = []

    for r in range(3):
        delivered.append({"r": str(r + 1), "i": "1", "t": t_weights_del[r], "value": 1})
        conditioningCapacity.append({"r": str(r + 1), "value": 1})

    CostMT, CostMB, CostRT, CostRB, Coord = [], [], [], [], []
    for i in range(n_count):
        s_id = str(i + 1)
        CostMT.append({"i": s_id, "value": random.choice([2, 5])})
        CostMB.append({"i": s_id, "value": random.choice([5, 10, 30])})
        CostRT.append({"i": s_id, "value": random.choice([4, 10])})
        CostRB.append({"i": s_id, "value": 1000 if i==0 else random.choice([20, 30, 100])})
        if s_id in nodes_list_dict:
            Coord.append({"i": s_id, "Coordinates": nodes_list_dict[s_id]})

    return dismantled, assignment, delivered, conditioningCapacity, conditioningDuration, CostMT, CostMB, CostRT, CostRB, Coord

def generate_full_json_dict(G, loop=0):
    edges, I, nodes_list_dict = prepare_edges_for_json(G)
    dismantled, assignment, delivered, condCap, condDur, CostMT, CostMB, CostRT, CostRB, Coord = prepare_parameter_for_json(G, I, nodes_list_dict)
    sets = {
        "I": I,
        "E": {"bidirectional": True, "seed": 1, "edges": edges},
        "M": ["1", "2", "3", "4", "5"],
        "R": ["1", "2", "3"]
    }
    params = {
        "defaults": { "V": 1000, "CostMB": 100, "CostMT": 20, "CostRB": 300, "CostRT": 50 },
        "t_max": 100,
        "V": [{"m": "1", "i": "1", "value": 42}],
        "dismantled": dismantled,
        "delivered": delivered,
        "conditioningCapacity": condCap,
        "conditioningDuration": condDur,
        "assignment": assignment,
        "CostMT": CostMT, "CostMB": CostMB,
        "CostRT": CostRT, "CostRB": CostRB,
        "CostZR": 9, "CostZH": 5,
        "Coord": Coord
    }
    return {"description": "Generated by Gradio", "sets": sets, "params": params}

# ==========================================
# 2. NETWORK GENERATOR CLASS
# ==========================================
class NetworkGenerator:
    def __init__(self, width=10, height=10, variant="F", topology="highly_connected", 
                 node_drop_fraction=0.1, target_nodes=0, target_edges=0,
                 bottleneck_cluster_count=None, bottleneck_edges_per_link=1):
        
        self.variant = variant.upper() 
        self.topology = topology.lower()
        self.width = int(width)
        self.height = int(height)
        self.node_drop_fraction = float(node_drop_fraction)
        self.target_nodes = int(target_nodes)
        self.target_edges = int(target_edges)
        self.node_factor = 0.4 
        
        if bottleneck_cluster_count is None:
            area = self.width * self.height
            self.bottleneck_cluster_count = max(2, int(area / 18))
        else:
            self.bottleneck_cluster_count = int(bottleneck_cluster_count)
            
        self.bottleneck_edges_per_link = int(bottleneck_edges_per_link)
        self.graph = None
        self.active_positions = None 

    def calculate_defaults(self):
        total_possible = (self.width + 1) * (self.height + 1)
        scale = {"highly_connected": 1.2, "bottlenecks": 0.85, "linear": 0.75}.get(self.topology, 1.0)
        
        if self.topology == "highly_connected": vf = max(0.0, self.node_drop_fraction * 0.8)
        elif self.topology == "linear": vf = min(0.95, self.node_drop_fraction * 1.2)
        else: vf = self.node_drop_fraction
        
        active_pct = 1.0 - vf
        est_nodes = int(self.node_factor * scale * total_possible * active_pct)
        
        if self.topology == "highly_connected": est_edges = int(3.5 * est_nodes)
        elif self.topology == "bottlenecks": est_edges = int(1.8 * est_nodes)
        else: est_edges = int(1.5 * est_nodes)
        return est_nodes, est_edges

    def generate(self):
        max_attempts = 15 
        for attempt in range(max_attempts):
            self._build_node_mask()
            self._initialize_graph()
            self._add_nodes()

            nodes = list(self.graph.nodes())
            if len(nodes) < 2: continue

            if self.topology == "bottlenecks":
                self._build_bottleneck_clusters(nodes)
            else:
                self._connect_all_nodes_by_nearby_growth(nodes)
                self._add_edges()

            self._remove_intersections()
            
            if self.target_edges > 0:
                self._adjust_edges_to_target()
            else:
                self._enforce_edge_budget()

            if not nx.is_connected(self.graph):
                self._force_connect_components()
            
            self._remove_intersections() 
            
            if nx.is_connected(self.graph):
                return self.graph

        raise RuntimeError("Failed to generate valid network.")

    def _effective_node_drop_fraction(self):
        if self.target_nodes > 0: return 0.0 
        base = self.node_drop_fraction
        if self.topology == "highly_connected": return max(0.0, base * 0.8)
        if self.topology == "linear": return min(0.95, base * 1.2)
        return base

    def _build_node_mask(self):
        all_positions = [(x, y) for x in range(self.width + 1) for y in range(self.height + 1)]
        if self.target_nodes > 0:
            self.active_positions = set(all_positions)
        else:
            drop_frac = self._effective_node_drop_fraction()
            drop = int(drop_frac * len(all_positions))
            deactivated = set(random.sample(all_positions, drop)) if drop > 0 else set()
            self.active_positions = set(all_positions) - deactivated

    def _initialize_graph(self):
        self.graph = nx.Graph()
        margin_x = max(1, self.width // 4)
        margin_y = max(1, self.height // 4)
        low_x, high_x = margin_x, self.width - margin_x
        low_y, high_y = margin_y, self.height - margin_y
        middle_active = [p for p in self.active_positions if low_x <= p[0] <= high_x and low_y <= p[1] <= high_y]
        
        if middle_active: seed = random.choice(middle_active)
        elif self.active_positions: seed = random.choice(list(self.active_positions))
        else: return 
        self.graph.add_node(tuple(seed))

    def _add_nodes(self):
        if self.target_nodes > 0:
            needed = self.target_nodes - len(self.graph.nodes())
            if needed <= 0: return
            available = list(self.active_positions - set(self.graph.nodes()))
            if self.topology != "linear" and len(available) > needed:
                center = random.choice(list(self.graph.nodes()))
                available.sort(key=lambda n: (n[0]-center[0])**2 + (n[1]-center[1])**2)
                chosen = random.sample(available, needed)
                for n in chosen: self.graph.add_node(n)
            else:
                if len(available) < needed:
                    for n in available: self.graph.add_node(n)
                else:
                    for n in random.sample(available, needed): self.graph.add_node(n)
            return

        total_possible = (self.width + 1) * (self.height + 1)
        base = self.node_factor if self.variant == "F" else random.uniform(0.3, 0.6)
        scale = {"highly_connected": 1.2, "bottlenecks": 0.85, "linear": 0.75}.get(self.topology, 1.0)
        target = int(base * scale * total_possible)
        target = min(target, len(self.active_positions))
        
        attempts = 0
        while len(self.graph.nodes()) < target and attempts < (target * 20):
            attempts += 1
            x = random.randint(0, self.width)
            y = random.randint(0, self.height)
            if (x, y) in self.active_positions and (x, y) not in self.graph:
                self.graph.add_node((x, y))

    def _connect_all_nodes_by_nearby_growth(self, nodes):
        connected = set()
        remaining = set(nodes)
        if not remaining: return
        current = random.choice(nodes)
        connected.add(current)
        remaining.remove(current)

        while remaining:
            candidates = []
            for n in remaining:
                closest_dist = min([abs(n[0]-c[0]) + abs(n[1]-c[1]) for c in connected])
                if closest_dist <= 4:
                    candidates.append(n)
            
            if not candidates:
                 best_n = min(remaining, key=lambda r: min(abs(r[0]-c[0]) + abs(r[1]-c[1]) for c in connected))
                 candidates.append(best_n)

            candidate = random.choice(candidates)
            neighbors = sorted(list(connected), key=lambda c: abs(c[0]-candidate[0]) + abs(c[1]-candidate[1]))
            for n in neighbors[:3]:
                if not self._would_create_intersection(n, candidate):
                    self.graph.add_edge(n, candidate)
                    break
            else:
                self.graph.add_edge(neighbors[0], candidate)

            connected.add(candidate)
            remaining.remove(candidate)

    def _compute_edge_count(self):
        if self.target_edges > 0: return self.target_edges
        n = len(self.graph.nodes())
        if self.topology == "highly_connected": return int(3.5 * n)
        if self.topology == "bottlenecks": return int(1.8 * n)
        return int(random.uniform(1.2, 2.0) * n)

    def _add_edges(self):
        nodes = list(self.graph.nodes())
        if self.topology == "highly_connected": self._add_cluster_dense(nodes, self._compute_edge_count())
        elif self.topology == "linear": self._make_linear(nodes)

    def _make_linear(self, nodes):
        nodes_sorted = sorted(nodes, key=lambda x: (x[0], x[1]))
        if not nodes_sorted: return
        prev = nodes_sorted[0]
        for nxt in nodes_sorted[1:]:
            if not self._would_create_intersection(prev, nxt): self.graph.add_edge(prev, nxt)
            prev = nxt

    def _add_cluster_dense(self, nodes, max_edges):
        edges_added = 0
        nodes = list(nodes)
        random.shuffle(nodes)
        dist_limit = 10 if self.target_edges > 0 else 4
        
        for i in range(len(nodes)):
            for j in range(i + 1, len(nodes)):
                if self.target_edges == 0 and edges_added >= max_edges: return
                n1, n2 = nodes[i], nodes[j]
                dist = max(abs(n1[0]-n2[0]), abs(n1[1]-n2[1]))
                if dist <= dist_limit: 
                    if not self._would_create_intersection(n1, n2):
                        self.graph.add_edge(n1, n2)
                        edges_added += 1

    def _build_bottleneck_clusters(self, nodes):
        self.graph.remove_edges_from(list(self.graph.edges()))
        clusters, centers = self._spatial_cluster_nodes(nodes, k=self.bottleneck_cluster_count)
        for cluster in clusters:
            if len(cluster) < 2: continue
            self._connect_cluster_by_nearby_growth(cluster)
            self._add_cluster_dense(list(cluster), max_edges=max(1, int(3.5 * len(cluster))))
        order = sorted(range(len(clusters)), key=lambda i: (centers[i][0], centers[i][1]))
        for a_idx, b_idx in zip(order[:-1], order[1:]):
            self._add_bottleneck_links(clusters[a_idx], clusters[b_idx], self.bottleneck_edges_per_link)
        if not nx.is_connected(self.graph): self._force_connect_components()

    def _force_connect_components(self):
        components = list(nx.connected_components(self.graph))
        while len(components) > 1:
            c1, c2 = list(components[0]), list(components[1])
            best_pair, min_dist = None, float('inf')
            s1 = c1 if len(c1)<30 else random.sample(c1, 30)
            s2 = c2 if len(c2)<30 else random.sample(c2, 30)
            for u in s1:
                for v in s2:
                    d = (u[0]-v[0])**2 + (u[1]-v[1])**2
                    if d < min_dist and not self._would_create_intersection(u, v):
                        min_dist, best_pair = d, (u, v)
            if best_pair: self.graph.add_edge(best_pair[0], best_pair[1])
            else: break 
            prev_len = len(components)
            components = list(nx.connected_components(self.graph))
            if len(components) == prev_len: break

    def _spatial_cluster_nodes(self, nodes, k):
        nodes = list(nodes)
        if k >= len(nodes): return [[n] for n in nodes], nodes[:]
        centers = random.sample(nodes, k)
        clusters = [[] for _ in range(k)]
        for n in nodes:
            best_i = min(range(k), key=lambda i: max(abs(n[0]-centers[i][0]), abs(n[1]-centers[i][1])))
            clusters[best_i].append(n)
        return clusters, centers

    def _connect_cluster_by_nearby_growth(self, cluster_nodes): self._connect_all_nodes_by_nearby_growth(cluster_nodes)

    def _add_bottleneck_links(self, cluster_a, cluster_b, m):
        pairs = []
        for u in cluster_a:
            for v in cluster_b:
                dist = max(abs(u[0]-v[0]), abs(u[1]-v[1]))
                pairs.append((dist, u, v))
        pairs.sort(key=lambda t: t[0])
        added = 0
        for _, u, v in pairs:
            if added >= m: break
            if not self.graph.has_edge(u, v) and not self._would_create_intersection(u, v): 
                self.graph.add_edge(u, v)
                added += 1

    def _remove_intersections(self):
        pass_no = 0
        while pass_no < 5:
            pass_no += 1
            edges = list(self.graph.edges())
            intersections = []
            check_edges = random.sample(edges, 400) if len(edges) > 600 else edges
            for i in range(len(check_edges)):
                for j in range(i+1, len(check_edges)):
                    e1, e2 = check_edges[i], check_edges[j]
                    if self._segments_intersect(e1[0], e1[1], e2[0], e2[1]): intersections.append((e1, e2))
            if not intersections: break
            for e1, e2 in intersections:
                if not self.graph.has_edge(*e1) or not self.graph.has_edge(*e2): continue
                l1 = (e1[0][0]-e1[1][0])**2 + (e1[0][1]-e1[1][1])**2
                l2 = (e2[0][0]-e2[1][0])**2 + (e2[0][1]-e2[1][1])**2
                rem = e1 if l1 > l2 else e2
                self.graph.remove_edge(*rem)

    def _adjust_edges_to_target(self):
        current_edges = list(self.graph.edges())
        curr_count = len(current_edges)
        if curr_count > self.target_edges:
            to_remove = curr_count - self.target_edges
            sorted_edges = sorted(current_edges, key=lambda e: (e[0][0]-e[1][0])**2 + (e[0][1]-e[1][1])**2, reverse=True)
            for e in sorted_edges:
                if len(self.graph.edges()) <= self.target_edges: break
                self.graph.remove_edge(*e)
                if not nx.is_connected(self.graph): self.graph.add_edge(*e) 
        elif curr_count < self.target_edges:
            needed = self.target_edges - curr_count
            nodes = list(self.graph.nodes())
            attempts = 0
            while len(self.graph.edges()) < self.target_edges and attempts < (needed * 30):
                attempts += 1
                u = random.choice(nodes)
                candidates = sorted(nodes, key=lambda n: (n[0]-u[0])**2 + (n[1]-u[1])**2)
                if len(candidates) < 2: continue
                v = random.choice(candidates[1:min(len(candidates), 10)])
                if not self.graph.has_edge(u, v) and not self._would_create_intersection(u, v):
                    self.graph.add_edge(u, v)

    def _enforce_edge_budget(self):
        budget = self._compute_edge_count()
        while len(self.graph.edges()) > budget:
            edges = list(self.graph.edges())
            rem = random.choice(edges)
            self.graph.remove_edge(*rem)
            if not nx.is_connected(self.graph):
                self.graph.add_edge(*rem)
                break 

    def _segments_intersect(self, a, b, c, d):
        if a == c or a == d or b == c or b == d: return False
        def ccw(A,B,C): return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])
        return ccw(a,c,d) != ccw(b,c,d) and ccw(a,b,c) != ccw(a,b,d)

    def _would_create_intersection(self, u, v):
        for a, b in self.graph.edges():
            if u == a or u == b or v == a or v == b: continue
            if self._segments_intersect(u, v, a, b): return True
        return False

    # === MANUAL EDITING ===
    def manual_add_node(self, x, y):
        # FIX: Force Int Cast to avoid "Already Exists" due to float mismatch
        x, y = int(x), int(y)
        
        if not (0 <= x <= self.width and 0 <= y <= self.height): return False, "Out of bounds."
        if self.graph.has_node((x, y)): return False, "Already exists."
        self.graph.add_node((x, y))
        nodes = list(self.graph.nodes())
        if len(nodes) > 1:
            closest = min([n for n in nodes if n != (x,y)], key=lambda n: (n[0]-x)**2 + (n[1]-y)**2)
            if not self._would_create_intersection((x,y), closest): self.graph.add_edge((x,y), closest)
        return True, "Added."

    def manual_delete_node_by_id(self, node_id):
        sorted_nodes = get_sorted_nodes(self.graph)
        try:
            idx = int(node_id) - 1
            if idx < 0 or idx >= len(sorted_nodes):
                return False, f"ID {node_id} not found."
            node_to_del = sorted_nodes[idx]
            self.graph.remove_node(node_to_del)
            if len(self.graph.nodes()) > 1 and not nx.is_connected(self.graph):
                 self._force_connect_components()
            return True, f"Node {node_id} {node_to_del} removed."
        except ValueError:
            return False, "Invalid ID."

# ==========================================
# GRADIO HELPERS
# ==========================================
def plot_graph(graph, width, height, title="Network", highlight_node=None):
    fig, ax = plt.subplots(figsize=(8, 8))
    pos = {node: (node[0], node[1]) for node in graph.nodes()}
    
    # 1. Edges
    nx.draw_networkx_edges(graph, pos, ax=ax, width=2, alpha=0.6, edge_color="#333")
    
    # 2. Nodes (Standard)
    # Filter nodes that are NOT highlighted
    normal_nodes = [n for n in graph.nodes() if n != highlight_node]
    nx.draw_networkx_nodes(graph, pos, ax=ax, nodelist=normal_nodes, node_size=350, node_color="#4F46E5", edgecolors="white", linewidths=1.5)
    
    # 3. Nodes (Highlight)
    if highlight_node and graph.has_node(highlight_node):
        nx.draw_networkx_nodes(graph, pos, ax=ax, nodelist=[highlight_node], node_size=400, node_color="#EF4444", edgecolors="white", linewidths=2.0)
    
    sorted_nodes = get_sorted_nodes(graph)
    labels = {node: str(i+1) for i, node in enumerate(sorted_nodes)}
    nx.draw_networkx_labels(graph, pos, labels, ax=ax, font_size=8, font_color="white", font_weight="bold")
    
    ax.set_xlim(-1, width + 1)
    ax.set_ylim(-1, height + 1)
    ax.invert_yaxis() 
    ax.grid(True, linestyle=':', alpha=0.3)
    ax.set_axis_on()
    ax.tick_params(left=True, bottom=True, labelleft=False, labelbottom=False)
    ax.set_title(title)
    return fig

def get_preset_dims(preset_mode, topology):
    if preset_mode == "Custom": return gr.update(interactive=True), gr.update(interactive=True)
    if topology == "linear":
        dims = (4, 4) if preset_mode == "Small" else (6, 11) if preset_mode == "Medium" else (10, 26)
    else: 
        dims = (4, 4) if preset_mode == "Small" else (8, 8) if preset_mode == "Medium" else (16, 16)
    return gr.update(value=dims[0], interactive=False), gr.update(value=dims[1], interactive=False)

def update_ui_for_variant(variant, width, height, topology, void_frac):
    is_custom = (variant == "Custom")
    if is_custom:
        temp_gen = NetworkGenerator(width, height, "F", topology, void_frac)
        def_nodes, def_edges = temp_gen.calculate_defaults()
        void_update = gr.update(interactive=True)
        target_node_update = gr.update(value=def_nodes, interactive=True)
        target_edge_update = gr.update(value=def_edges, interactive=True)
    else:
        area = width * height
        val = 0.60 if area <= 20 else 0.35
        void_update = gr.update(value=val, interactive=False)
        target_node_update = gr.update(value=0, interactive=False)
        target_edge_update = gr.update(value=0, interactive=False)
    return void_update, target_node_update, target_edge_update

def save_single_visual_action(state_data):
    if not state_data or "graph" not in state_data: return None
    graph = state_data["graph"]
    width = state_data["width"]
    height = state_data["height"]
    fig = plot_graph(graph, width, height, "Network Visual")
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    fname = f"network_visual_{timestamp}.png"
    fig.savefig(fname)
    plt.close(fig)
    return fname

def generate_and_store(topology, width, height, variant, void_frac, t_nodes, t_edges):
    try:
        var_code = "F" if variant == "Fixed" else "R"
        if variant == "Fixed": t_nodes, t_edges = 0, 0
        gen = NetworkGenerator(width, height, var_code, topology, void_frac, t_nodes, t_edges)
        graph = gen.generate()
        fig = plot_graph(graph, width, height, f"{topology} ({len(graph.nodes())}N, {len(graph.edges())}E)")
        metrics = f"**Nodes:** {len(graph.nodes())} | **Edges:** {len(graph.edges())} | **Density:** {nx.density(graph):.2f}"
        state_data = { "graph": graph, "width": width, "height": height, "topology": topology }
        return fig, metrics, state_data, gr.update(interactive=True), gr.update(interactive=True)
    except Exception as e:
        return None, f"Error: {e}", None, gr.update(interactive=False), gr.update(interactive=False)

def manual_edit_action(action, x, y, node_id, state_data):
    if not state_data or "graph" not in state_data: return None, "No graph.", state_data
    gen = NetworkGenerator(state_data["width"], state_data["height"]) 
    gen.graph = state_data["graph"] 
    
    # Store added node to pass to plotter
    highlight = None
    
    if action == "Add Node":
        # Ensure Int here too
        x, y = int(x), int(y)
        success, msg = gen.manual_add_node(x, y)
        if success: highlight = (x, y)
    else:
        success, msg = gen.manual_delete_node_by_id(node_id)
        
    if success:
        fig = plot_graph(gen.graph, state_data["width"], state_data["height"], "Edited", highlight_node=highlight)
        metrics = f"**Nodes:** {len(gen.graph.nodes())} | **Edges:** {len(gen.graph.edges())} | {msg}"
        state_data["graph"] = gen.graph 
        return fig, metrics, state_data
    else:
        return gr.update(), f"Error: {msg}", state_data

def run_batch_generation(count, topology, width, height, variant, void_frac, t_nodes, t_edges):
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    dir_name = f"batch_{timestamp}"
    os.makedirs(dir_name, exist_ok=True)
    var_code = "F" if variant == "Fixed" else "R"
    if variant == "Fixed": t_nodes, t_edges = 0, 0
    try:
        for i in range(int(count)):
            gen = NetworkGenerator(width, height, var_code, topology, void_frac, t_nodes, t_edges)
            G = gen.generate()
            json_content = generate_full_json_dict(G, loop=i+1)
            with open(os.path.join(dir_name, f"inst_{i+1}.json"), 'w') as f:
                json.dump(json_content, f, indent=4)
        zip_path = shutil.make_archive(dir_name, 'zip', dir_name)
        shutil.rmtree(dir_name) 
        return zip_path
    except Exception as e:
        return None

# ==========================================
# GRADIO UI
# ==========================================
with gr.Blocks(title="Graph Generator Pro") as demo:
    state = gr.State()
    gr.Markdown("# Spatial Network Generator Pro")
    
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Tab("Config"):
                topology = gr.Dropdown(["highly_connected", "bottlenecks", "linear"], value="highly_connected", label="Topology")
                preset = gr.Radio(["Small", "Medium", "Large", "Custom"], value="Medium", label="Preset")
                with gr.Row():
                    width = gr.Number(8, label="Width", interactive=False)
                    height = gr.Number(8, label="Height", interactive=False)
                variant = gr.Dropdown(["Fixed", "Custom"], value="Fixed", label="Variant")
                void_frac = gr.Slider(0.0, 0.9, 0.35, label="Void Fraction", interactive=False)
                gr.Markdown("### Custom Overrides")
                with gr.Row():
                    t_nodes = gr.Number(0, label="Nodes", interactive=False)
                    t_edges = gr.Number(0, label="Edges", interactive=False)
                gen_btn = gr.Button("Generate", variant="primary")
                save_viz_btn = gr.Button("Download Visual", interactive=False)
                viz_file = gr.File(label="Saved Visual", interactive=False, visible=False)

            with gr.Tab("Editor"):
                with gr.Tab("Add"):
                    with gr.Row():
                        ed_x = gr.Number(0, label="X", precision=0)
                        ed_y = gr.Number(0, label="Y", precision=0)
                    btn_add = gr.Button("Add Node at (X,Y)")
                with gr.Tab("Delete"):
                    ed_id = gr.Number(1, label="Node Number (ID)", precision=0)
                    btn_del = gr.Button("Delete Node ID")

            with gr.Tab("Batch"):
                batch_count = gr.Slider(1, 50, 5, step=1, label="Count")
                batch_btn = gr.Button("Generate Batch ZIP")
                file_out = gr.File(label="Download ZIP")

        with gr.Column(scale=2):
            metrics = gr.Markdown("Ready.")
            plot = gr.Plot()

    inputs_dims = [preset, topology]
    preset.change(get_preset_dims, inputs_dims, [width, height])
    topology.change(get_preset_dims, inputs_dims, [width, height])
    
    inputs_var = [variant, width, height, topology, void_frac]
    variant.change(update_ui_for_variant, inputs_var, [void_frac, t_nodes, t_edges])
    width.change(update_ui_for_variant, inputs_var, [void_frac, t_nodes, t_edges])
    height.change(update_ui_for_variant, inputs_var, [void_frac, t_nodes, t_edges])
    topology.change(update_ui_for_variant, inputs_var, [void_frac, t_nodes, t_edges])

    gen_args = [topology, width, height, variant, void_frac, t_nodes, t_edges]
    gen_btn.click(generate_and_store, gen_args, [plot, metrics, state, save_viz_btn, viz_file])
    save_viz_btn.click(save_single_visual_action, [state], [viz_file]).then(
        lambda: gr.update(visible=True), None, [viz_file]
    )

    btn_add.click(manual_edit_action, [gr.State("Add Node"), ed_x, ed_y, gr.State(0), state], [plot, metrics, state])
    btn_del.click(manual_edit_action, [gr.State("Del Node"), gr.State(0), gr.State(0), ed_id, state], [plot, metrics, state])

    batch_args = [batch_count, topology, width, height, variant, void_frac, t_nodes, t_edges]
    batch_btn.click(run_batch_generation, batch_args, [file_out])

if __name__ == "__main__":
    demo.launch()