app.py

import subprocess
import sys

# Check if matplotlib is installed, if not, install it
try:
    import matplotlib.pyplot as plt
except ImportError:
    subprocess.check_call([sys.executable, "-m", "pip", "install", "matplotlib"])
    import matplotlib.pyplot as plt  # Now import matplotlib after installation

import numpy as np
import networkx as nx

# Helper Functions
def parse_graph_input(graph_input):
    """Parse user input to create an adjacency list."""
    try:
        graph = eval(graph_input)
        if isinstance(graph, dict):
            return graph
    except:
        pass

    try:
        edges = eval(graph_input)
        if not isinstance(edges, list):
            raise ValueError("Invalid graph input. Please use an adjacency list or edge list.")
        
        graph = {}
        for u, v in edges:
            graph.setdefault(u, []).append(v)
            graph.setdefault(v, []).append(u)
        return graph
    except:
        raise ValueError("Invalid graph input. Please use a valid adjacency list or edge list.")

def visualize_graph(graph):
    """Generate a visualization of the graph using a circular layout."""
    plt.figure()
    nodes = list(graph.keys())
    edges = [(u, v) for u in graph for v in graph[u]]
    
    pos = nx.circular_layout(nx.Graph(edges))  # NetworkX layout for graph visualization
    nx.draw(
        nx.Graph(edges),
        pos,
        with_labels=True,
        node_color='lightblue',
        edge_color='gray',
        node_size=500,
        font_size=10
    )
    # The plot is created using Matplotlib
    return plt.gcf()

def spectral_isomorphism_test(graph1, graph2):
    """Perform spectral isomorphism test with step-by-step explanation."""
    adj_spectrum1 = sorted(np.linalg.eigvals(nx.adjacency_matrix(nx.Graph(graph1)).todense()).real)
    adj_spectrum2 = sorted(np.linalg.eigvals(nx.adjacency_matrix(nx.Graph(graph2)).todense()).real)
    lap_spectrum1 = sorted(np.linalg.eigvals(nx.laplacian_matrix(nx.Graph(graph1)).todense()).real)
    lap_spectrum2 = sorted(np.linalg.eigvals(nx.laplacian_matrix(nx.Graph(graph2)).todense()).real)

    adj_spectrum1 = [round(float(x), 2) for x in adj_spectrum1]
    adj_spectrum2 = [round(float(x), 2) for x in adj_spectrum2]
    lap_spectrum1 = [round(float(x), 2) for x in lap_spectrum1]
    lap_spectrum2 = [round(float(x), 2) for x in lap_spectrum2]

    output = (
        f"### **Spectral Isomorphism Test Results**\n\n"
        f"#### **Step 1: Node and Edge Counts**\n"
        f"- **Graph 1**: Nodes: {len(graph1)}, Edges: {sum(len(neighbors) for neighbors in graph1.values()) // 2}\n"
        f"- **Graph 2**: Nodes: {len(graph2)}, Edges: {sum(len(neighbors) for neighbors in graph2.values()) // 2}\n\n"
        f"#### **Step 2: Adjacency Spectra**\n"
        f"- Graph 1: {adj_spectrum1}\n"
        f"- Graph 2: {adj_spectrum2}\n"
        f"- Are the adjacency spectra approximately equal? {'✅ Yes' if np.allclose(adj_spectrum1, adj_spectrum2) else '❌ No'}\n\n"
        f"#### **Step 3: Laplacian Spectra**\n"
        f"- Graph 1: {lap_spectrum1}\n"
        f"- Graph 2: {lap_spectrum2}\n"
        f"- Are the Laplacian spectra approximately equal? {'✅ Yes' if np.allclose(lap_spectrum1, lap_spectrum2) else '❌ No'}\n\n"
        f"#### **Final Result**\n"
        f"- Outcome: {'✅ PASS' if np.allclose(adj_spectrum1, adj_spectrum2) and np.allclose(lap_spectrum1, lap_spectrum2) else '❌ FAIL'}\n"
        f"- Conclusion: The graphs are {'isomorphic' if np.allclose(adj_spectrum1, adj_spectrum2) and np.allclose(lap_spectrum1, lap_spectrum2) else 'NOT isomorphic'}.\n"
    )
    return output

def process_inputs(graph1_input, graph2_input):
    """Process user inputs and perform the spectral isomorphism test."""
    graph1 = parse_graph_input(graph1_input)
    graph2 = parse_graph_input(graph2_input)

    result = spectral_isomorphism_test(graph1, graph2)

    graph1_plot = visualize_graph(graph1)
    graph2_plot = visualize_graph(graph2)

    return graph1_plot, graph2_plot, result

# Gradio Interface
with gr.Blocks(title="Graph Theory Project") as demo:
    gr.Markdown("# Graph Theory Project")
    gr.Markdown("Analyze two graphs using spectral isomorphism tests!")

    with gr.Row():
        graph1_input = gr.Textbox(label="Graph 1 Input (e.g., '{0: [1], 1: [0, 2], 2: [1]}' or edge list)")
        graph2_input = gr.Textbox(label="Graph 2 Input (e.g., '{0: [1], 1: [0, 2], 2: [1]}' or edge list)")

    with gr.Row():
        graph1_output = gr.Plot(label="Graph 1 Visualization")
        graph2_output = gr.Plot(label="Graph 2 Visualization")

    result_output = gr.Textbox(label="Results", lines=20)

    submit_button = gr.Button("Run")
    submit_button.click(process_inputs, inputs=[graph1_input, graph2_input], outputs=[graph1_output, graph2_output, result_output])

# Launch the app
demo.launch()