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| import gradio as gr | |
| import os | |
| import openai | |
| # Defina sua chave da API OpenAI | |
| api_key = None | |
| def create_knowledge_graph(user_input): | |
| global api_key | |
| if not api_key: | |
| return "Por favor, insira sua chave da API OpenAI." | |
| # Configurar a chamada para a API OpenAI | |
| openai.api_key = api_key | |
| response = 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...", | |
| "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"}, | |
| "properties": { | |
| "type": "object", | |
| "description": "Additional attributes for the node", | |
| }, | |
| }, | |
| "required": [ | |
| "id", | |
| "label", | |
| "type", | |
| "color", | |
| ], | |
| }, | |
| }, | |
| "edges": { | |
| "type": "array", | |
| "items": { | |
| "type": "object", | |
| "properties": { | |
| "from": {"type": "string"}, | |
| "to": {"type": "string"}, | |
| "relationship": {"type": "string"}, | |
| "direction": {"type": "string"}, | |
| "color": {"type": "string"}, | |
| "properties": { | |
| "type": "object", | |
| "description": "Additional attributes for the edge", | |
| }, | |
| }, | |
| "required": [ | |
| "from", | |
| "to", | |
| "relationship", | |
| "color", | |
| ], | |
| }, | |
| }, | |
| }, | |
| "required": ["nodes", "edges"], | |
| }, | |
| } | |
| ], | |
| function_call={"name": "knowledge_graph"}, | |
| ) | |
| response_data = response.choices[0]["message"]["function_call"]["arguments"] | |
| return response_data | |
| # Defina a interface Gradio | |
| iface = gr.Interface( | |
| fn=create_knowledge_graph, | |
| inputs=gr.Textbox("Texto para criar o gráfico de conhecimento:"), | |
| outputs=gr.Image(type="pil"), # Imagem de saída para o gráfico | |
| live=True, | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |