Update app.py

app.py

@@ -5,18 +5,10 @@ import numpy as np
 from torchvision import transforms
 
 def predict(text):
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
-    model = torch.jit.load("model.pth")
-    model = model.cpu()
-    model.eval()
-
-    model.to(device)
-    with torch.no_grad():
-        outputs = model(text)
-    outputs = torch.argmax(outputs,1)
-
-    return(outputs)
+    
+    learnClass = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics=accuracy,cbs=callbacks).to_fp16()
+    learnClass = learnClass.load_encoder('modelo')
+    return(learnClass.predict(text))
     
 # Creamos la interfaz y la lanzamos. 
 gr.Interface(fn=predict, inputs="text", outputs="text").launch();