plotly instead of matplotlib

app.py

@@ -15,14 +15,19 @@ with gr.Blocks(title="PSYCHOHISTORY") as app:
                 outputs=mem_results
             )
 
-        with gr.Row():
-            img_output = gr.Image(label="Graph Visualization", type="filepath")  # Add an Image component
+        # with gr.Row():
+        #     img_output = gr.Image(label="Graph Visualization", type="filepath")  # Add an Image component
 
-            # Trigger graph generation after JSON is generated
-            mem_results.change(
+        #     # Trigger graph generation after JSON is generated
+        #     mem_results.change(
+        #         psychohistory.main,
+        #         inputs=[mem_results],
+        #         outputs=img_output
+        #     )
+        mem_results.change(
                 psychohistory.main,
                 inputs=[mem_results],
-                outputs=img_output
+                outputs=None
             )
 
 if __name__ == "__main__":

psychohistory.py

@@ -1,4 +1,4 @@
-import matplotlib.pyplot as plt
+import plotly.graph_objects as go  # Import Plotly for interactive plots
 from mpl_toolkits.mplot3d import Axes3D
 import networkx as nx
 import numpy as np
@@ -95,38 +95,34 @@ def find_paths(G):
 
     return best_path, best_mean_prob, worst_path, worst_mean_prob, longest_path, shortest_path
 
-def draw_path_3d(G, path, filename='path_plot_3d.png', highlight_color='blue'):
-    """Draws a specific path in 3D."""
+
+def draw_path_3d_interactive(G, path, highlight_color='blue'):
+    """Draws a specific path in 3D using Plotly for interactivity."""
     H = G.subgraph(path).copy()
     pos = nx.get_node_attributes(G, 'pos')
     x_vals, y_vals, z_vals = zip(*[pos[node] for node in path])
 
-    fig = plt.figure(figsize=(16, 12))
-    ax = fig.add_subplot(111, projection='3d')
-
     node_colors = ['red' if prob < 0.33 else 'blue' if prob < 0.67 else 'green' for _, prob, _ in [pos[node] for node in path]]
-    ax.scatter(x_vals, y_vals, z_vals, c=node_colors, s=700, edgecolors='black', alpha=0.7)
+    node_trace = go.Scatter3d(x=x_vals, y=y_vals, z=z_vals, mode='markers+text',
+                             marker=dict(size=10, color=node_colors, line=dict(width=1, color='black')),
+                             text=list(map(str, path)), textposition='top center', hoverinfo='text')
 
+    edge_traces = []
     for edge in H.edges():
         x_start, y_start, z_start = pos[edge[0]]
         x_end, y_end, z_end = pos[edge[1]]
-        ax.plot([x_start, x_end], [y_start, y_end], [z_start, z_end], color=highlight_color, lw=2)
-
-    for node, (x, y, z) in pos.items():
-        if node in path:
-            ax.text(x, y, z, str(node), fontsize=12, color='black')
+        edge_trace = go.Scatter3d(x=[x_start, x_end], y=[y_start, y_end], z=[z_start, z_end],
+                                 mode='lines', line=dict(width=2, color=highlight_color), hoverinfo='none')
+        edge_traces.append(edge_trace)
 
-    ax.set_xlabel('Time (weeks)')
-    ax.set_ylabel('Event Probability')
-    ax.set_zlabel('Event Number')
-    ax.set_title('3D Event Tree - Path')
+    layout = go.Layout(scene=dict(xaxis_title='Time (weeks)', yaxis_title='Event Probability', zaxis_title='Event Number'),
+                       title='3D Event Tree - Path')
+    fig = go.Figure(data=[node_trace] + edge_traces, layout=layout)
+    fig.show()
 
-    plt.savefig(filename, bbox_inches='tight')
-    plt.close()
 
-
-def draw_global_tree_3d(G, filename='global_tree.png'):
-    """Draws the entire graph in 3D."""
+def draw_global_tree_3d_interactive(G):
+    """Draws the entire graph in 3D using Plotly for interactivity."""
     pos = nx.get_node_attributes(G, 'pos')
     labels = nx.get_node_attributes(G, 'label')
 
@@ -135,35 +131,32 @@ def draw_global_tree_3d(G, filename='global_tree.png'):
         return
 
     x_vals, y_vals, z_vals = zip(*pos.values())
-    fig = plt.figure(figsize=(16, 12))
-    ax = fig.add_subplot(111, projection='3d')
 
     node_colors = ['red' if prob < 0.33 else 'blue' if prob < 0.67 else 'green' for _, prob, _ in pos.values()]
-    ax.scatter(x_vals, y_vals, z_vals, c=node_colors, s=700, edgecolors='black', alpha=0.7)
+    node_trace = go.Scatter3d(x=x_vals, y=y_vals, z=z_vals, mode='markers+text',
+                             marker=dict(size=10, color=node_colors, line=dict(width=1, color='black')),
+                             text=list(labels.values()), textposition='top center', hoverinfo='text')
 
+    edge_traces = []
     for edge in G.edges():
         x_start, y_start, z_start = pos[edge[0]]
         x_end, y_end, z_end = pos[edge[1]]
-        ax.plot([x_start, x_end], [y_start, y_end], [z_start, z_end], color='gray', lw=2)
-
-    for node, (x, y, z) in pos.items():
-        label = labels.get(node, f"{node}")
-        ax.text(x, y, z, label, fontsize=12, color='black')
-
-    ax.set_xlabel('Time')
-    ax.set_ylabel('Probability')
-    ax.set_zlabel('Event Number')
-    ax.set_title('3D Event Tree')
+        edge_trace = go.Scatter3d(x=[x_start, x_end], y=[y_start, y_end], z=[z_start, z_end],
+                                 mode='lines', line=dict(width=2, color='gray'), hoverinfo='none')
+        edge_traces.append(edge_trace)
 
-    plt.savefig(filename, bbox_inches='tight')
-    plt.close()
+    layout = go.Layout(scene=dict(xaxis_title='Time', yaxis_title='Probability', zaxis_title='Event Number'),
+                       title='3D Event Tree')
+    fig = go.Figure(data=[node_trace] + edge_traces, layout=layout)
+    fig.show()
 
 
 def main(json_data):
     G = nx.DiGraph()
     build_graph_from_json(json_data, G)  # Build graph from the provided JSON data
 
-    draw_global_tree_3d(G, filename='global_tree.png')
+    # Draw the interactive graph using Plotly
+    draw_global_tree_3d_interactive(G)
 
     best_path, best_mean_prob, worst_path, worst_mean_prob, longest_path, shortest_path = find_paths(G)
 
@@ -181,12 +174,12 @@ def main(json_data):
         print(f"Duration: {max(G.nodes[node]['pos'][0] for node in shortest_path) - min(G.nodes[node]['pos'][0] for node in shortest_path):.2f}")
 
     if best_path:
-        draw_path_3d(G, best_path, 'best_path.png', 'blue')
+        draw_path_3d_interactive(G, best_path, 'blue')
     if worst_path:
-        draw_path_3d(G, worst_path, 'worst_path.png', 'red')
+        draw_path_3d_interactive(G, worst_path, 'red')
     if longest_path:
-        draw_path_3d(G, longest_path, 'longest_duration_path.png', 'green')
+        draw_path_3d_interactive(G, longest_path, 'green')
     if shortest_path:
-        draw_path_3d(G, shortest_path, 'shortest_duration_path.png', 'purple')
+        draw_path_3d_interactive(G, shortest_path, 'purple')
 
-    return 'global_tree.png'  # Return the filename of the global tree
+    return None  # No need to return a filename for interactive plot