Update app.py

app.py

@@ -1,6 +1,7 @@
-# Load model directly
+
 import transformers
 from transformers import GraphormerForGraphClassification
+import gradio as gr
 
 import os 
 try:
@@ -9,4 +10,24 @@ except ImportError:
     os.system('pip install toml')
     import toml
 print('todo en orden')
-model = GraphormerForGraphClassification.from_pretrained("PedroLancharesSanchez/graph-regression")
+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)