update app.py

app.py

@@ -1,17 +1,37 @@
+#python3
+#build a text summarizer using hugging face and gradio
+
+
 import gradio as gr
 import pandas as pd
-from transformers import pipeline
+import numpy as np
+import tensorflow as tf
+import transformers
+from transformers import TFAutoModel, AutoTokenizer
+
+model_class, tokenizer_class, pretrained_weights = (TFAutoModel, AutoTokenizer, 'bert-base-uncased')
+
+# Load pretrained model/tokenizer
+tokenizer = tokenizer_class.from_pretrained(pretrained_weights)
+model = model_class.from_pretrained(pretrained_weights)
+
+def get_summary(article):
+    article_input_ids = tokenizer.encode(article, return_tensors='tf')
+    summary_ids = model.generate(article_input_ids)
+    summary_txt = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary_txt
 
-summarizer = pipeline("summarization")
+def get_summary_gradio(article):
+    return get_summary(article)
 
-def summarize(text):
-    return summarizer(text, max_length=250, min_length=30)[0]['summary_text']
+iface = gr.Interface(get_summary_gradio, "textbox", "textbox", live=True,
+                     examples=[
+                         ["The quick brown fox jumps over the lazy dog."],
+                         ["The world is a strange place. Sometimes, things are what they seem. But then, if you look closer, they can become something entirely different."],
+                         ["The sky is clear; the stars are twinkling. I'm going to bed now. Good night."],
+                         ["The president of the United States, and the president of the United Kingdom, have both been in the White House."],
+                         ["The president of the United States, and the president of the United Kingdom, have both been in the White House."]
+                     ])
 
-gr.Interface(
-    summarize, 
-    gr.inputs.Textbox(lines=15, placeholder="Enter text here"), 
-    [gr.outputs.Textbox(lines=10, placeholder="Summarized text will appear here")],
-    title="Text Summarizer",
-    description="Summarizes text using the transformer library",
-    examples=[]
-).launch()
+if __name__ == "__main__":
+    iface.launch()