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import gradio as gr |
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import openai |
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import json |
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from graphviz import Digraph |
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def generate_knowledge_graph(api_key, user_input): |
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openai.api_key = api_key |
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print("Chamando a API da OpenAI...") |
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completion = openai.ChatCompletion.create( |
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model="gpt-3.5-turbo-16k", |
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messages=[ |
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{ |
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"role": "user", |
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"content": f"Help me understand following by describing as a detailed knowledge graph: {user_input}", |
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} |
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] |
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) |
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response_data = completion.choices[0].message.to_dict() |
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response_data = json.loads(response_data['content']) |
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print("Dados da resposta:") |
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print(response_data) |
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print("Gerando o conhecimento usando Graphviz...") |
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dot = Digraph(comment="Knowledge Graph") |
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for node in response_data.get("nodes", []): |
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dot.node(node["id"], f"{node['label']} ({node['type']})") |
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for edge in response_data.get("edges", []): |
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dot.edge(edge["from"], edge["to"], label=edge["relationship"]) |
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print("Renderizando o gráfico para o formato PNG...") |
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dot.format = "png" |
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dot.render(filename="knowledge_graph", cleanup=True) |
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print("Gráfico gerado com sucesso!") |
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return "knowledge_graph.png" |
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iface = gr.Interface( |
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fn=generate_knowledge_graph, |
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inputs=[ |
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gr.components.Textbox(label="OpenAI API Key", type="password"), |
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gr.components.Textbox(label="User Input for Graph") |
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], |
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outputs=gr.components.Image(type="filepath", label="Generated Knowledge Graph"), |
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live=False |
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) |
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print("Iniciando a interface Gradio...") |
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iface.launch() |
