|
|
|
import transformers |
|
from transformers import GraphormerForGraphClassification |
|
from transformers.models.graphormer.collating_graphormer import preprocess_item |
|
import gradio as gr |
|
import json |
|
|
|
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): |
|
print(instancia) |
|
instancia=json.loads(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']]) |
|
print(inputs) |
|
with torch.no_grad(): |
|
logits = model(**inputs).logits |
|
return str(logits.item()) |
|
|
|
ejemplos = '''{"node_feat": [[0],[0],[0],[0],[0],[0],[0],[0],[1],[0],[0],[0],[0],[1],[2],[0],[0],[0],[0],[0],[0],[3],[0],[0]], |
|
"edge_index": [[0,1,1,1,1,2,3,4,4,4,5,5,6,6,7,7,7,8,8,9,9,10,10,10,11,11,12,12,12,13,14,14,15,15,15,16,16,17,17,18,18,19,19,20,20,20,21,22,22,22,23,23]], |
|
"edge_attr": [[1],[1],[1],[1],[1],[1],[1],[1],[2],[1],[2],[1],[1],[2],[2],[1],[1],[1],[1],[1],[2],[2],[1],[1],[1],[1],[1],[2],[1],[2],[1],[1],[1],[2],[1],[2],[1],[1],[2],[2],[1],[1],[2],[1],[2],[1],[1],[1],[1],[2],[1],[2]], |
|
"y": [3.1381945610046387], |
|
"num_nodes": 24}''' |
|
|
|
|
|
interfaz = gr.Interface(fn=predict, inputs="file", outputs='text', examples=['grafo1.json']) |
|
interfaz.launch(share=False) |
|
|
