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import gradio as gr
import openai
import json
from graphviz import Digraph
from PIL import Image
import io
import requests
from bs4 import BeautifulSoup
# Function to generate a knowledge graph from text
def generate_knowledge_graph_from_text(api_key, user_input):
response_data = process_user_input(api_key, user_input)
return generate_knowledge_graph(response_data)
# Function to generate a knowledge graph from a URL
def generate_knowledge_graph_from_url(api_key, user_input):
text = scrape_text_from_url(user_input)
response_data = process_user_input(api_key, text)
return generate_knowledge_graph(response_data)
# Function to process user input and call OpenAI API
def process_user_input(api_key, user_input):
openai.api_key = api_key
completion = openai.ChatCompletion.create(
model="gpt-3.5-turbo-16k",
messages=[
{
"role": "user",
"content": f"Help me understand following by describing as a detailed knowledge graph: {user_input}",
}
],
functions=[
{
"name": "knowledge_graph",
"description": "Generate a knowledge graph with entities and relationships. Use the colors to help differentiate between different node or edge types/categories. Always provide light pastel colors that work well with black font.",
"parameters": {
"type": "object",
"properties": {
"metadata": {
"type": "object",
"properties": {
"createdDate": {"type": "string"},
"lastUpdated": {"type": "string"},
"description": {"type": "string"},
},
},
"nodes": {
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {"type": "string"},
"label": {"type": "string"},
"type": {"type": "string"},
"color": {"type": "string"}, # Added color property
"properties": {
"type": "object",
"description": "Additional attributes for the node",
},
},
"required": [
"id",
"label",
"type",
"color",
], # Added color to required
},
},
"edges": {
"type": "array",
"items": {
"type": "object",
"properties": {
"from": {"type": "string"},
"to": {"type": "string"},
"relationship": {"type": "string"},
"direction": {"type": "string"},
"color": {"type": "string"}, # Added color property
"properties": {
"type": "object",
"description": "Additional attributes for the edge",
},
},
"required": [
"from",
"to",
"relationship",
"color",
], # Added color to required
},
},
},
"required": ["nodes", "edges"],
},
}
],
function_call={"name": "knowledge_graph"},
)
response_data = completion.choices[0]["message"]["function_call"]["arguments"]
return response_data
# Function to generate a knowledge graph from response data
def generate_knowledge_graph(response_data):
dot = Digraph(comment="Knowledge Graph", format='png')
dot.attr(dpi='300')
dot.attr(bgcolor='white') # Set background color to white
dot.attr('node', shape='box', style='filled', fillcolor='lightblue', fontcolor='black')
for node in response_data.get("nodes", []):
dot.node(node["id"], f"{node['label']} ({node['type']})", color=node.get("color", "lightblue"))
dot.attr('edge', color='black', fontcolor='black')
for edge in response_data.get("edges", []):
dot.edge(edge["from"], edge["to"], label=edge["relationship"], color=edge.get("color", "black"))
image_data = dot.pipe()
image = Image.open(io.BytesIO(image_data))
return image
# Function to scrape text from a website
def scrape_text_from_url(url):
response = requests.get(url)
if response.status_code != 200:
return "Error: Could not retrieve content from URL."
soup = BeautifulSoup(response.text, "html.parser")
paragraphs = soup.find_all("p")
text = " ".join([p.get_text() for p in paragraphs])
return text
title_and_description = """
# Instagraph - Knowledge Graph Generator
**Created by [ArtificialGuyBR](https://twitter.com/ArtificialGuyBR)**
This interactive knowledge graph generator allows you to input either text or a URL.
If you provide text, it will generate a knowledge graph based on the text you provide.
If you provide a URL, it will scrape the content from the webpage and generate a knowledge graph from that.
To get started, enter your OpenAI API Key and either your text or a URL.
"""
iface = gr.Interface(
fn=generate_knowledge_graph_from_text,
inputs=[
gr.inputs.Textbox(label="OpenAI API Key", type="password"),
gr.inputs.Textbox(label="Text or URL", type="text"),
],
outputs=gr.outputs.Image(type="pil", label="Generated Knowledge Graph"),
live=False,
title=title_and_description,
)
iface.launch()
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