Update app.py

app.py

@@ -1,28 +1,48 @@
-import torch
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-import gradio as gr
+# import torch
+# from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+# import gradio as gr
+
+# # Load your custom model and tokenizer
+# model_name = "MiVaCod/mbart-neutralization"
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
+# model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# # Function to correct sentences
+# def predict(sentence):
+#     inputs = tokenizer.encode("correction: " + sentence, return_tensors="pt", max_length=512, truncation=True)
+#     outputs = model.generate(inputs, max_length=128, num_beams=4, early_stopping=True)
+#     corrected_sentence = tokenizer.decode(outputs[0], skip_special_tokens=True)
+#     return corrected_sentence
+
+# # Gradio Interface
+# iface = gr.Interface(
+#     fn=correct_sentence,
+#     inputs="text",
+#     outputs="text",
+#     title="Sentence Correction",
+#     description="Enter a sentence to be corrected:",
+#     theme="compact"
+# )
+
+# # Launch the interface
+# gr.Interface(fn=predict, inputs=gr.inputs.Textbox, outputs=gr.outputs.Textbox).launch(share=False)
+
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import gradio as grad
 
-# Load your custom model and tokenizer
 model_name = "MiVaCod/mbart-neutralization"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-# Function to correct sentences
-def predict(sentence):
-    inputs = tokenizer.encode("correction: " + sentence, return_tensors="pt", max_length=512, truncation=True)
-    outputs = model.generate(inputs, max_length=128, num_beams=4, early_stopping=True)
-    corrected_sentence = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return corrected_sentence
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=correct_sentence,
-    inputs="text",
-    outputs="text",
-    title="Sentence Correction",
-    description="Enter a sentence to be corrected:",
-    theme="compact"
-)
-
-# Launch the interface
-gr.Interface(fn=predict, inputs=gr.inputs.Textbox, outputs=gr.outputs.Textbox).launch(share=False)
+text2text_tkn= T5Tokenizer.from_pretrained(model_name)
+mdl = T5ForConditionalGeneration.from_pretrained(model_name)
+
+def text2text_paraphrase(sentence1,sentence2):
+     inp1 = "rte sentence1: "+sentence1
+     inp2 = "sentence2: "+sentence2
+     combined_inp=inp1+" "+inp2
+     enc = text2text_tkn(combined_inp, return_tensors="pt")
+     tokens = mdl.generate(**enc)
+     response=text2text_tkn.batch_decode(tokens)
+     return response
+
+sent1=grad.Textbox(lines=1, label="Frase misógina", placeholder="Introduce una frase misógina")
+out=grad.Textbox(lines=1, label="Frase corregida")
+grad.Interface(text2text_paraphrase, inputs=[sent1,sent2], outputs=out).launch()