Create app.py

app.py

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+from fastai.vision.all import *
+import gradio as gr
+
+
+# Cargamos el learner
+learn = load_learner('export.pkl')
+
+# Definimos las etiquetas de nuestro modelo
+labels = ["0","1","2","3"]
+
+
+# Definimos una función que se encarga de llevar a cabo las predicciones
+def predict(string):
+    print(learn.predict(string))
+    pred,pred_idx,probs = learn.predict(string)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    
+# Creamos la interfaz y la lanzamos. 
+gr.Interface(fn=predict, inputs=gr.inputs.Textbox(lines=1), outputs=gr.outputs.Label(num_top_classes=3),examples=['it was so annoying to watch the president','I am so glad to see you'], title="Natural Language Model to classify the feelings of a message", description="This model has been trained with messages obtained from Twitter. Its purpose is to classify the possible feelings that a message might express. The labels obtained have the following meaning:\n 0: anger\n 1: joy\n 2: optimism\n 3: sadness").launch(share=False)