:rocket: Deploy model

app.py

@@ -1,19 +1,11 @@
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import gradio as gr
-import torch
-
-# Cargar el modelo
-model_name = "nlmaldonadog/mbart-clarification-P8"
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-def predict(text):
-    inputs = tokenizer(text, return_tensors='pt')
-    outputs = model(**inputs)
-    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-    return {'negative': float(probs[0][0]), 'neutral': float(probs[0][1]), 'positive': float(probs[0][2])}
-
-texto = gr.inputs.Textbox(lines=2, placeholder='Escribe aquí...')
-
-# Creamos la interfaz y la lanzamos. 
-gr.Interface(fn=predict, inputs=texto, outputs=gr.outputs.Label()).launch(share=False)
+import gradio as gr
+from transformers import pipeline
+
+# Cargar el modelo
+model = pipeline('sec2sec', model='nlmaldonadog/mbart-clarification-P8')
+
+def predict(text):
+    return model(text)[0]['generated_text']
+
+iface = gr.Interface(fn=predict, inputs=[gr.Textbox(placeholder='Escribe aquí...')], outputs="text")
+iface.launch(share=True)