Create app.py

app.py

@@ -0,0 +1,26 @@
+import pandas as pd
+from fastai.text.all import *
+from datasets import load_dataset
+from transformers import pipeline
+import gradio as gr
+
+#repo_id = "maviced/practica7-1"
+#learn = from_pretrained_fastai(repo_id)
+#model = learn.model
+#model = model.cpu()
+
+#repo_id = "maviced/practica7-2"
+#learn = from_pretrained_fastai(repo_id)
+#model = learn.model
+#model = model.cpu()
+
+classifier = pipeline('text-classification', model='maviced/clasificador-rotten-tomatoes')
+
+def predict(text):
+    #pred,pred_idx,probs = learner.predict(text)
+    #return pred
+    
+    return classifier(text)[0]['label']
+    
+gr.Interface(fn=predict, inputs="text", outputs="text",examples=['A visually stunning and emotionally resonant film that leaves a lasting impact.',
+                       'The film, while visually stunning, falls short with a predictable plot and underdeveloped characters, leaving the audience craving more substance.']).launch(share=True)