Update app.py

app.py

@@ -1,10 +1,24 @@
-from transformers import BertTokenizerFast,TFBertForSequenceClassification,TextClassificationPipeline
+from transformers import BertTokenizerFast,TFBertForSequenceClassification,TextClassificationPipeline, AutoTokenizer, T5ForConditionalGeneration
 import numpy as np
 import tensorflow as tf
 import gradio as gr 
 import openai
 import os
 
+#Summarization Fine Tune Model
+
+model_path = "leadingbridge/summarization"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = T5ForConditionalGeneration.from_pretrained(model_path)
+def summarize_text(text):
+	# Tokenize the input text
+	inputs = tokenizer.encode(text, return_tensors="pt")
+	# Generate the summary
+	summary_ids = model.generate(inputs)
+	summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+	# Return the summary
+	return summary
+
 # Sentiment Analysis Pre-Trained Model
 model_path = "leadingbridge/sentiment-analysis"
 tokenizer = BertTokenizerFast.from_pretrained(model_path)
@@ -116,6 +130,13 @@ with gr.Blocks() as demo:
         proceed_button = gr.Button("Translate")
         proceed_button.click(fn=openai_translation_ce, inputs=inputs, outputs=outputs)
         gr.Markdown("This model translate a Chinese sentence to English using the OpenAI engine. Enter a Chinese short sentence in the input box and click the 'Translate' button to get the translation result in English.")
+    with gr.Tab("📑Text Summarization"):
+        gr.Markdown("""<h4><center>📑Text Summarization</center></h4>""")
+        inputs = gr.Textbox(placeholder="Enter a Chinese text to summarize here.",lines=3)
+        outputs = gr.Textbox(label="Summary")
+        proceed_button = gr.Button("Summarize")
+        proceed_button.click(fn=summarize_text, inputs=inputs, outputs=outputs)
+        gr.Markdown("This model summarizes Chinese text using the MT5 language model. Enter a Chinese text in the input box and click the 'Summarize' button to get the summary.")
     gr.Markdown('''
     We are happy to share with you some Chinese language models that we've made using NLP. When we looked online, we noticed that there weren't many resources available for Chinese NLP, so we hope that our models can be useful to you.
     We want to mention that these models aren't perfect and there is still room for improvement. Because of limited resources, there might be some mistakes or limitations in the models.