Upload app.py

app.py

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+from fastai.text.all import *
+import gradio as gr
+from huggingface_hub import from_pretrained_fastai
+
+# Cargamos el learner
+repo_id = "ikerml/twitter_class"
+
+learner = from_pretrained_fastai(repo_id)
+
+# Definimos las etiquetas de nuestro modelo
+labels = ['❤','😍','😂','💕','🔥','😊','😎','✨','💙','😘','📷','🇺🇸','☀','💜','😉','💯','😁','🎄','📸','😜']
+
+example1 = "I like potatoe"
+example2 = "My house is on fire"
+example3 = "The other day, i eat a banana"
+
+# Definimos una función que se encarga de llevar a cabo las predicciones
+def predict(text):
+    pred,pred_idx,probs = learner.predict(text)
+    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(), outputs=gr.outputs.Label(),examples=[example1,example2,example3]).launch(share=False)