| import transformers | |
| from transformers import GraphormerForGraphClassification | |
| import gradio as gr | |
| import os | |
| try: | |
| import toml | |
| except ImportError: | |
| os.system('pip install toml') | |
| import toml | |
| print('todo en orden') | |
| model = GraphormerForGraphClassification.from_pretrained("PedroLancharesSanchez/graph-regression") | |
| def predict(instancia): | |
| instancia_preprocesada=preprocess_item(instancia) | |
| inputs={} | |
| inputs['input_nodes'] = torch.tensor([instancia_preprocesada['input_nodes']]) | |
| inputs['input_edges'] = torch.tensor([instancia_preprocesada['input_edges']]) | |
| inputs['attn_bias'] = torch.tensor([instancia_preprocesada['attn_bias']]) | |
| inputs['in_degree'] = torch.tensor([instancia_preprocesada['in_degree']]) | |
| inputs['out_degree'] = torch.tensor([instancia_preprocesada['out_degree']]) | |
| inputs['spatial_pos'] = torch.tensor([instancia_preprocesada['spatial_pos']]) | |
| inputs['attn_edge_type'] = torch.tensor([instancia_preprocesada['attn_edge_type']]) | |
| with torch.no_grad(): | |
| logits = model(**inputs).logits | |
| predicted_class_id = logits.argmax().item() | |
| return logits | |
| graph_input = gr.inputs.Graph(graph_type="networkx", label="Grafo de entrada") | |
| regression_output = gr.outputs.Textbox(label="Valor de regresión") | |
| gr.Interface(fn=predict, inputs=graph_input, outputs=regression_output,examples=['grafo1.json','grafo2.json']).launch(share=False) | |