Create app.py

app.py

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+import os
+import networkx as nx
+from sentence_transformers import SentenceTransformer
+import spacy
+import matplotlib.pyplot as plt
+import gradio as gr
+from io import BytesIO
+import base64
+
+# Load pre-trained models
+nlp = spacy.load("en_core_web_sm")
+sent_model = SentenceTransformer('bert-base-nli-mean-tokens')
+
+def extract_entities(text):
+    """Extract entities from text using spaCy"""
+    doc = nlp(text)
+    entities = [(e.text, e.label_) for e in doc.ents]
+    return entities
+
+def extract_relations(text):
+    """Extract relationships between entities using spaCy's dependency parser"""
+    doc = nlp(text)
+    relations = []
+    for token in doc:
+        if token.dep_ in ("nsubj", "dobj", "prep"):
+            subject = token.head.text
+            predicate = token.text
+            object = token.text if token.dep_ == "prep" else token.head.text
+            relations.append((subject, predicate, object))
+    return relations
+
+def build_knowledge_graph(entities, relations):
+    """Construct the knowledge graph using NetworkX"""
+    G = nx.Graph()
+    for entity, entity_type in entities:
+        G.add_node(entity, type=entity_type)
+    for subject, predicate, object in relations:
+        G.add_edge(subject, object, label=predicate)
+    return G
+
+def visualize_graph(graph):
+    """Visualize the knowledge graph using NetworkX and Matplotlib"""
+    pos = nx.spring_layout(graph)
+    plt.figure(figsize=(12, 8))
+    nx.draw(graph, pos, with_labels=True, node_color='lightblue', node_size=2000, font_size=10, font_weight='bold', edge_color='gray')
+    edge_labels = nx.get_edge_attributes(graph, 'label')
+    nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels, font_color='red')
+    
+    # Save the plot to a BytesIO object
+    img = BytesIO()
+    plt.savefig(img, format='png')
+    img.seek(0)
+    plt.close()
+    
+    # Encode the image to base64
+    plot_data = base64.b64encode(img.getvalue()).decode()
+    return plot_data
+
+def run_app(input_text):
+    try:
+        # Extract entities
+        entities = extract_entities(input_text)
+        entity_text = "\n".join([f"{e[0]} ({e[1]})" for e in entities])
+
+        # Extract relations
+        relations = extract_relations(input_text)
+        relation_text = "\n".join([f"{r[0]} --{r[1]}--> {r[2]}" for r in relations])
+
+        # Build knowledge graph
+        graph = build_knowledge_graph(entities, relations)
+
+        # Visualize graph
+        plot_data = visualize_graph(graph)
+
+        # Convert base64 image to HTML img tag
+        plot_html = f'<img src="data:image/png;base64,{plot_data}" alt="Knowledge Graph">'
+
+        return f"Entities:\n{entity_text}\n\nRelations:\n{relation_text}\n\nKnowledge graph created and visualized.", plot_html
+    except Exception as e:
+        return f"An error occurred: {str(e)}", None
+
+# Sample input text
+sample_text = "This is a sample text. John Smith is the CEO of Apple Inc. located in Cupertino, California. The Paris Agreement is a landmark international treaty on climate change."
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=run_app,
+    inputs=gr.Textbox(label="Input Text", value=sample_text),
+    outputs=[gr.Textbox(label="Output Text"), gr.HTML(label="Knowledge Graph Visualization")],
+    title="Knowledge Graph Builder",
+    description="Enter text to generate and visualize a knowledge graph"
+)
+
+demo.launch()