Update app.py

app.py

@@ -5,10 +5,18 @@ import numpy as np
 from torchvision import transforms
 
 def predict(text):
-    
-    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))
+
+    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)
     
 # Creamos la interfaz y la lanzamos. 
 gr.Interface(fn=predict, inputs="text", outputs="text").launch();