Update app.py

app.py

@@ -1,17 +1,17 @@
-from huggingface_hub import from_pretrained_fastai
 import gradio as gr
-from fastai.text.all import *
 
-# Cargar el primer modelo
-repo_id1 = "nlmaldonadog/AWD_LSTM_P7"
-learner1 = from_pretrained_fastai(repo_id1)
-labels1 = learner1.dls.vocab
+from transformers import pipeline
+classifier = pipeline('text-classification', model='nlmaldonadog/clasificador-rotten-tomatoes-xlnet-base-cased')
 
-def predict1(text):
-    pred, pred_idx, probs = learner1.predict(text)
-    return {labels1[i]: float(probs[i]) for i in range(len(labels1))}
+def predict(text):
+    prediction = classifier(text)
+    score = int(round(prediction[0]['score'] * 100))
+    
+    if prediction[0]['label'] == "LABEL_0":
+        output = f"Negative sentiment with a confidence level of {score}%."
+    else:
+        output = f"Positive sentiment with a confidence level of {score}%."
+    return output   
 
-texto = gr.inputs.Textbox(lines=2, placeholder='Escribe aquí...')
-
-# Creamos la interfaz y la lanzamos. 
-gr.Interface(fn=predict1, inputs=texto, outputs=gr.outputs.Label()).launch(share=False)
+iface = gr.Interface(fn=predict, inputs=[gr.Textbox(placeholder='Escribe aquí...')], outputs="text")
+iface.launch(share=True)