app.py

I have this code what libires I should use import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
from scipy.sparse.linalg import eigsh
from scipy.sparse import csgraph
import ast

# Helper Functions
def parse_graph_input(graph_input):
"""Parse user input to create an adjacency list."""
try:
# Try interpreting as a dictionary (adjacency list)
graph = ast.literal_eval(graph_input)
if isinstance(graph, dict):
return graph
except:
pass
try:
# Try interpreting as an edge list
edges = ast.literal_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."""
if len(graph) > 50: # Skip visualization for large graphs
return None

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))
nx.draw(
nx.Graph(edges),
pos,
with_labels=True,
node_color='lightblue',
edge_color='gray',
node_size=500,
font_size=10
)
return plt.gcf()

def calculate_spectrum(matrix, k=6, which='LM'):
"""Calculate the largest k eigenvalues of a sparse matrix."""
eigenvalues, _ = eigsh(matrix, k=k, which=which)
return sorted(eigenvalues.real)

def spectral_isomorphism_test(graph1, graph2):
"""Perform spectral isomorphism test with step-by-step explanation."""
adj_matrix1 = nx.adjacency_matrix(nx.Graph(graph1))
adj_matrix2 = nx.adjacency_matrix(nx.Graph(graph2))
lap_matrix1 = nx.laplacian_matrix(nx.Graph(graph1))
lap_matrix2 = nx.laplacian_matrix(nx.Graph(graph2))
adj_spectrum1 = calculate_spectrum(adj_matrix1, k=min(6, len(graph1) - 1))
adj_spectrum2 = calculate_spectrum(adj_matrix2, k=min(6, len(graph2) - 1))
lap_spectrum1 = calculate_spectrum(lap_matrix1, k=min(6, len(graph1) - 1), which='SM')
lap_spectrum2 = calculate_spectrum(lap_matrix2, k=min(6, len(graph2) - 1), which='SM')
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 check_graph_homomorphism(graph1, graph2, mapping):
"""Check if a mapping defines a graph homomorphism."""
result = []
for u, v in graph1.edges():
mapped_u, mapped_v = mapping.get(u), mapping.get(v)
if mapped_u is None or mapped_v is None:
result.append(f"Mapping is incomplete. Missing vertex {u} or {v}.")
continue
if (mapped_u, mapped_v) not in graph2.edges() and (mapped_v, mapped_u) not in graph2.edges():
result.append(f"Edge ({u}, {v}) in Graph 1 maps to ({mapped_u}, {mapped_v}) in Graph 2. Edge does NOT exist in Graph 2.")
else:
result.append(f"Edge ({u}, {v}) in Graph 1 maps to ({mapped_u}, {mapped_v}) in Graph 2. Edge exists in Graph 2.")

is_homomorphism = all(("exists" in line) for line in result)
final_result = (
f"**Final Result:** {'✅ Mapping IS a Graph Homomorphism.' if is_homomorphism else '❌ Mapping IS NOT a Graph Homomorphism.'}\n"
f"Explanation: A graph homomorphism must preserve all adjacencies. If any edge fails to map correctly, the mapping is invalid."
)
return "\n".join(result) + "\n\n" + final_result

def demonstrate_matrix_representations(graph):
"""Display adjacency matrix, Laplacian matrix, and spectra."""
adj_matrix = nx.adjacency_matrix(nx.Graph(graph)).todense()
laplacian_matrix = nx.laplacian_matrix(nx.Graph(graph)).todense()
degree_matrix = np.diag([len(graph[v]) for v in graph])

adj_spectrum = calculate_spectrum(nx.adjacency_matrix(nx.Graph(graph)), k=min(6, len(graph) - 1))
lap_spectrum = calculate_spectrum(nx.laplacian_matrix(nx.Graph(graph)), k=min(6, len(graph) - 1), which='SM')

algebraic_connectivity = lap_spectrum[1] if len(lap_spectrum) > 1 else 0 # Second smallest eigenvalue

output = (
f"### **Matrix Representations and Spectra**\n\n"

f"#### **Adjacency Matrix**\n"
f"```\n{adj_matrix}\n```\n\n"

f"#### **Laplacian Matrix**\n"
f"```\n{laplacian_matrix}\n```\n\n"

f"#### **Degree Matrix**\n"
f"```\n{degree_matrix}\n```\n\n"

f"#### **Adjacency Spectrum**\n"
f"```{[round(x, 2) for x in adj_spectrum]}```\n\n"

f"#### **Laplacian Spectrum**\n"
f"```{[round(x, 2) for x in lap_spectrum]}```\n\n"

f"#### **Algebraic Connectivity**\n"
f"The second smallest eigenvalue (Algebraic Connectivity): {round(algebraic_connectivity, 2)}\n\n"

f"**Explanation:** These matrices and spectra provide insights into the graph's structure. Algebraic connectivity measures robustness."
)
return output

def process_inputs(graph1_input, graph2_input, question_type, mapping=None):
"""Process user inputs and perform the selected operation."""
# Parse graphs
graph1 = parse_graph_input(graph1_input)
graph2 = parse_graph_input(graph2_input)
# Determine operation based on question type
if question_type == "Spectral Isomorphism Test":
result = spectral_isomorphism_test(graph1, graph2)
elif question_type == "Graph Homomorphism Check":
if mapping is None:
result = "Error: Mapping is required for Graph Homomorphism Check."
else:
result = check_graph_homomorphism(nx.Graph(graph1), nx.Graph(graph2), eval(mapping))
elif question_type == "Matrix Representations and Spectra":
result = demonstrate_matrix_representations(graph1)
else:
result = "Unsupported question type."
# Visualize graphs
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 graphs using algebraic methods!")
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)")
question_type = gr.Dropdown(
choices=["Spectral Isomorphism Test", "Graph Homomorphism Check", "Matrix Representations and Spectra"],
label="Select Question Type"
)
mapping_input = gr.Textbox(label="Mapping (for Graph Homomorphism Check, e.g., '{0: 0, 1: 1, 2: 2}')", visible=False)
def toggle_mapping_visibility(question_type):
return {"visible": question_type == "Graph Homomorphism Check"}
question_type.change(toggle_mapping_visibility, inputs=question_type, outputs=mapping_input)
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(
lambda g1, g2, qt, m: process_inputs(g1, g2, qt, m),
inputs=[graph1_input, graph2_input, question_type, mapping_input],
outputs=[graph1_output, graph2_output, result_output]
)

# Launch the app with share=True to make it publicly accessible
demo.launch(share=True)

app.py

@@ -0,0 +1,201 @@
+I have this code what libires I should use import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+import networkx as nx
+from scipy.sparse.linalg import eigsh
+from scipy.sparse import csgraph
+import ast
+
+# Helper Functions
+def parse_graph_input(graph_input):
+    """Parse user input to create an adjacency list."""
+    try:
+        # Try interpreting as a dictionary (adjacency list)
+        graph = ast.literal_eval(graph_input)
+        if isinstance(graph, dict):
+            return graph
+    except:
+        pass
+    try:
+        # Try interpreting as an edge list
+        edges = ast.literal_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."""
+    if len(graph) > 50:  # Skip visualization for large graphs
+        return None
+    
+    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))
+    nx.draw(
+        nx.Graph(edges),
+        pos,
+        with_labels=True,
+        node_color='lightblue',
+        edge_color='gray',
+        node_size=500,
+        font_size=10
+    )
+    return plt.gcf()
+
+def calculate_spectrum(matrix, k=6, which='LM'):
+    """Calculate the largest k eigenvalues of a sparse matrix."""
+    eigenvalues, _ = eigsh(matrix, k=k, which=which)
+    return sorted(eigenvalues.real)
+
+def spectral_isomorphism_test(graph1, graph2):
+    """Perform spectral isomorphism test with step-by-step explanation."""
+    adj_matrix1 = nx.adjacency_matrix(nx.Graph(graph1))
+    adj_matrix2 = nx.adjacency_matrix(nx.Graph(graph2))
+    lap_matrix1 = nx.laplacian_matrix(nx.Graph(graph1))
+    lap_matrix2 = nx.laplacian_matrix(nx.Graph(graph2))
+    adj_spectrum1 = calculate_spectrum(adj_matrix1, k=min(6, len(graph1) - 1))
+    adj_spectrum2 = calculate_spectrum(adj_matrix2, k=min(6, len(graph2) - 1))
+    lap_spectrum1 = calculate_spectrum(lap_matrix1, k=min(6, len(graph1) - 1), which='SM')
+    lap_spectrum2 = calculate_spectrum(lap_matrix2, k=min(6, len(graph2) - 1), which='SM')
+    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 check_graph_homomorphism(graph1, graph2, mapping):
+    """Check if a mapping defines a graph homomorphism."""
+    result = []
+    for u, v in graph1.edges():
+        mapped_u, mapped_v = mapping.get(u), mapping.get(v)
+        if mapped_u is None or mapped_v is None:
+            result.append(f"Mapping is incomplete. Missing vertex {u} or {v}.")
+            continue
+        if (mapped_u, mapped_v) not in graph2.edges() and (mapped_v, mapped_u) not in graph2.edges():
+            result.append(f"Edge ({u}, {v}) in Graph 1 maps to ({mapped_u}, {mapped_v}) in Graph 2. Edge does NOT exist in Graph 2.")
+        else:
+            result.append(f"Edge ({u}, {v}) in Graph 1 maps to ({mapped_u}, {mapped_v}) in Graph 2. Edge exists in Graph 2.")
+    
+    is_homomorphism = all(("exists" in line) for line in result)
+    final_result = (
+        f"**Final Result:** {'✅ Mapping IS a Graph Homomorphism.' if is_homomorphism else '❌ Mapping IS NOT a Graph Homomorphism.'}\n"
+        f"Explanation: A graph homomorphism must preserve all adjacencies. If any edge fails to map correctly, the mapping is invalid."
+    )
+    return "\n".join(result) + "\n\n" + final_result
+
+def demonstrate_matrix_representations(graph):
+    """Display adjacency matrix, Laplacian matrix, and spectra."""
+    adj_matrix = nx.adjacency_matrix(nx.Graph(graph)).todense()
+    laplacian_matrix = nx.laplacian_matrix(nx.Graph(graph)).todense()
+    degree_matrix = np.diag([len(graph[v]) for v in graph])
+    
+    adj_spectrum = calculate_spectrum(nx.adjacency_matrix(nx.Graph(graph)), k=min(6, len(graph) - 1))
+    lap_spectrum = calculate_spectrum(nx.laplacian_matrix(nx.Graph(graph)), k=min(6, len(graph) - 1), which='SM')
+    
+    algebraic_connectivity = lap_spectrum[1] if len(lap_spectrum) > 1 else 0  # Second smallest eigenvalue
+    
+    output = (
+        f"### **Matrix Representations and Spectra**\n\n"
+        
+        f"#### **Adjacency Matrix**\n"
+        f"```\n{adj_matrix}\n```\n\n"
+        
+        f"#### **Laplacian Matrix**\n"
+        f"```\n{laplacian_matrix}\n```\n\n"
+        
+        f"#### **Degree Matrix**\n"
+        f"```\n{degree_matrix}\n```\n\n"
+        
+        f"#### **Adjacency Spectrum**\n"
+        f"```{[round(x, 2) for x in adj_spectrum]}```\n\n"
+        
+        f"#### **Laplacian Spectrum**\n"
+        f"```{[round(x, 2) for x in lap_spectrum]}```\n\n"
+        
+        f"#### **Algebraic Connectivity**\n"
+        f"The second smallest eigenvalue (Algebraic Connectivity): {round(algebraic_connectivity, 2)}\n\n"
+        
+        f"**Explanation:** These matrices and spectra provide insights into the graph's structure. Algebraic connectivity measures robustness."
+    )
+    return output
+
+def process_inputs(graph1_input, graph2_input, question_type, mapping=None):
+    """Process user inputs and perform the selected operation."""
+    # Parse graphs
+    graph1 = parse_graph_input(graph1_input)
+    graph2 = parse_graph_input(graph2_input)
+    # Determine operation based on question type
+    if question_type == "Spectral Isomorphism Test":
+        result = spectral_isomorphism_test(graph1, graph2)
+    elif question_type == "Graph Homomorphism Check":
+        if mapping is None:
+            result = "Error: Mapping is required for Graph Homomorphism Check."
+        else:
+            result = check_graph_homomorphism(nx.Graph(graph1), nx.Graph(graph2), eval(mapping))
+    elif question_type == "Matrix Representations and Spectra":
+        result = demonstrate_matrix_representations(graph1)
+    else:
+        result = "Unsupported question type."
+    # Visualize graphs
+    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 graphs using algebraic methods!")
+    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)")
+    question_type = gr.Dropdown(
+        choices=["Spectral Isomorphism Test", "Graph Homomorphism Check", "Matrix Representations and Spectra"],
+        label="Select Question Type"
+    )
+    mapping_input = gr.Textbox(label="Mapping (for Graph Homomorphism Check, e.g., '{0: 0, 1: 1, 2: 2}')", visible=False)
+    def toggle_mapping_visibility(question_type):
+        return {"visible": question_type == "Graph Homomorphism Check"}
+    question_type.change(toggle_mapping_visibility, inputs=question_type, outputs=mapping_input)
+    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(
+        lambda g1, g2, qt, m: process_inputs(g1, g2, qt, m),
+        inputs=[graph1_input, graph2_input, question_type, mapping_input],
+        outputs=[graph1_output, graph2_output, result_output]
+    )
+
+# Launch the app with share=True to make it publicly accessible
+demo.launch(share=True)