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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")
example= '''{
"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],[1,0,2,3,4,1,1,1,5,23,4,6,5,7,6,8,22,7,9,8,10,9,11,22,10,12,11,13,14,12,12,15,14,16,20,15,17,16,18,17,19,18,20,15,19,21,20,7,10,23,4,22]],
"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
}'''
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']])
with torch.no_grad():
logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
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}'''
]
# Crear la interfaz Gradio
interfaz = gr.Interface(fn=predict, inputs="text", outputs='text', examples=ejemplos)
interfaz.launch(share=False)
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