Create spacy.py

spacy.py

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+import streamlit as st
+import spacy
+import networkx as nx
+import plotly.graph_objs as go
+
+# Load the spaCy model
+nlp = spacy.load("en_core_web_sm")
+
+def create_knowledge_graph(text):
+    # Process the text using spaCy
+    doc = nlp(text)
+    entities = [(ent.text, ent.label_) for ent in doc.ents]
+    
+    # Create a graph
+    G = nx.Graph()
+    for entity, label in entities:
+        G.add_node(entity, label=label)
+
+    # Add edges (for simplicity, connect each entity to the next)
+    for i in range(len(entities) - 1):
+        G.add_edge(entities[i][0], entities[i + 1][0])
+    
+    return G, entities
+
+def plot_graph(G):
+    pos = nx.spring_layout(G)  # positions for all nodes
+    edges = G.edges()
+    edge_x = []
+    edge_y = []
+    
+    for edge in edges:
+        x0, y0 = pos[edge[0]]
+        x1, y1 = pos[edge[1]]
+        edge_x.append(x0)
+        edge_x.append(x1)
+        edge_x.append(None)  # None to break line
+        edge_y.append(y0)
+        edge_y.append(y1)
+        edge_y.append(None)  # None to break line
+
+    node_x = []
+    node_y = []
+    for node in G.nodes():
+        x, y = pos[node]
+        node_x.append(x)
+        node_y.append(y)
+
+    # Create Plotly figure
+    fig = go.Figure()
+    
+    # Add edges to figure
+    fig.add_trace(go.Scatter(
+        x=edge_x, y=edge_y,
+        line=dict(width=0.5, color='black'),
+        hoverinfo='none',
+        mode='lines'))
+
+    # Add nodes to figure
+    fig.add_trace(go.Scatter(
+        x=node_x, y=node_y,
+        mode='markers+text',
+        text=list(G.nodes()),
+        textposition="top center",
+        hoverinfo='text',
+        marker=dict(
+            showscale=True,
+            colorscale='YlGnBu',
+            size=10,
+            color=node_y,
+            colorbar=dict(thickness=15, title="Node Connections", xanchor='left', titleside='right'),
+        )))
+
+    fig.update_layout(showlegend=False, hovermode='closest',
+                      margin=dict(b=0, t=0, l=0, r=0),
+                      xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+                      yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))
+    
+    return fig
+
+# Streamlit app
+# st.title("Knowledge Graph Generator")
+
+text_input = st.text_area("Enter your text here:")
+if st.button("Generate Knowledge Graph"):
+    if text_input:
+        G, entities = create_knowledge_graph(text_input)
+        
+        # Plot the graph using Plotly
+        fig = plot_graph(G)
+        st.plotly_chart(fig)
+        
+        st.write("Extracted Entities:")
+        st.write(entities)
+    else:
+        st.warning("Please enter some text.")