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import gradio as gr
import openai
import json
import requests
from bs4 import BeautifulSoup
from graphviz import Digraph
import base64
from io import BytesIO
from PIL import Image
def generate_knowledge_graph(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}",
}
],
function_call={"name": "knowledge_graph"},
)
response_data = completion.choices[0]["message"]["function_call"]["arguments"]
response_dict = json.loads(response_data)
dot = Digraph(comment="Knowledge Graph")
# Add nodes to the graph
for node in response_dict.get("nodes", []):
dot.node(node["id"], f"{node['label']} ({node['type']})")
# Add edges to the graph
for edge in response_dict.get("edges", []):
dot.edge(edge["from"], edge["to"], label=edge["relationship"])
# Render to PNG format
dot.format = "png"
dot.render(filename="knowledge_graph", cleanup=True)
# Convert PNG to base64 to display in Gradio
with open("knowledge_graph.png", "rb") as img_file:
img_base64 = base64.b64encode(img_file.read()).decode()
return f"data:image/png;base64,{img_base64}"
iface = gr.Interface(
fn=generate_knowledge_graph,
inputs=[
gr.inputs.Textbox(label="OpenAI API Key", type="password"),
gr.inputs.Textbox(label="Text to Generate Knowledge Graph")
],
outputs=gr.outputs.Image(type="pil", label="Generated Knowledge Graph"),
live=False
)
iface.launch()
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