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import transformers |
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from transformers import GraphormerForGraphClassification |
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import gradio as gr |
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import json |
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import os |
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try: |
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import toml |
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except ImportError: |
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os.system('pip install toml') |
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import toml |
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print('todo en orden') |
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model = GraphormerForGraphClassification.from_pretrained("PedroLancharesSanchez/graph-regression") |
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def predict(instancia): |
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instancia=json.loads(instancia) |
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instancia_preprocesada=preprocess_item(instancia) |
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inputs={} |
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inputs['input_nodes'] = torch.tensor([instancia_preprocesada['input_nodes']]) |
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inputs['input_edges'] = torch.tensor([instancia_preprocesada['input_edges']]) |
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inputs['attn_bias'] = torch.tensor([instancia_preprocesada['attn_bias']]) |
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inputs['in_degree'] = torch.tensor([instancia_preprocesada['in_degree']]) |
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inputs['out_degree'] = torch.tensor([instancia_preprocesada['out_degree']]) |
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inputs['spatial_pos'] = torch.tensor([instancia_preprocesada['spatial_pos']]) |
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inputs['attn_edge_type'] = torch.tensor([instancia_preprocesada['attn_edge_type']]) |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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predicted_class_id = logits.argmax().item() |
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return str(logits.item()) |
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gr.Interface(fn=predict, inputs='str', outputs='str',examples=['grafo1.txt','grafo2.txt','grafo3.txt']).launch(share=False) |
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