Create main.py

main.py

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+from typing import NoReturn
+import spacy
+import networkx as nx
+import matplotlib.pyplot as plt
+import io
+from PIL import Image
+import gradio as gr
+
+# Load the spaCy model for dependency parsing
+nlp = spacy.load("en_core_web_sm")
+
+# Function to extract entities using NER
+def extract_entities(text):
+    doc = nlp(text)
+    entities = [(ent.text, ent.label_) for ent in doc.ents]
+    return entities
+
+# Function to extract relationships dynamically from the text
+def extract_relationships(text):
+    relationships = []
+    doc = nlp(text.lower())
+    subject, verb, obj, Noun = None, None, None, None
+    entities = []
+    for token in doc:
+        if token.dep_ in ("compound"):
+            Noun = token.text + " "
+            continue
+        if not Noun:
+            if token.dep_ in ("nsubj", "nsubjpass"):
+                subject = token.text
+            if token.dep_ in ("dobj", "attr", "pobj"):
+                obj = token.text
+                entities.append(obj)
+            if token.dep_ in ("ROOT", "xcomp", "ccomp"):
+                verb = token.text
+        elif Noun:
+            if token.dep_ in ("nsubj", "nsubjpass"):
+                subject = Noun
+                entities.append(subject)
+            if token.dep_ in ("dobj", "attr", "pobj"):
+                obj = Noun
+                entities.append(obj)
+            Noun = None
+        if token.dep_ == "prep":
+            subject = entities[-1]
+            if token.head.dep_ == "ROOT":
+                verb = token.head.text + " " + token.text
+            else:
+                verb = token.text
+        if subject and verb and obj:
+            relationships.append((subject.strip(), verb.strip(), obj.strip()))
+            subject, verb, obj = None, None, None
+    return relationships, entities
+
+# Function to create the knowledge graph
+def create_knowledge_graph(entities, relationships):
+    G = nx.DiGraph()
+    involved_entities = set()
+    for subj, rel, obj in relationships:
+        involved_entities.add(subj)
+        involved_entities.add(obj)
+    for entity in involved_entities:
+        G.add_node(entity)
+    for subj, rel, obj in relationships:
+        G.add_edge(subj, obj, label=rel)
+    return G
+
+# Function to visualize the graph
+def visualize_graph(G):
+    pos = nx.spring_layout(G)
+    edge_labels = nx.get_edge_attributes(G, 'label')
+    plt.figure(figsize=(12, 8))
+    nx.draw(G, pos, with_labels=True, node_size=2000, node_color="lightblue", font_size=10, font_weight="bold")
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+    plt.close()
+    pil_image = Image.open(buf)
+    return pil_image
+
+# Function to process input and generate output
+def process_text(text: str):
+    relationships, entities = extract_relationships(text)
+    G = create_knowledge_graph(entities, relationships)
+    return visualize_graph(G)
+
+# Gradio Interface
+gr.Interface(
+    fn=process_text,
+    inputs=gr.Textbox(placeholder="Enter knowledge prompt here"),
+    outputs=gr.Image(type="pil"),
+    title="Knowledge Graph Generator"
+).launch()