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app.py

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+import gradio as gr
+import re
+
+from gradio.mix import Parallel
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSeq2SeqLM,
+)
+
+def clean_text(text):
+    text = text.encode("ascii", errors="ignore").decode(
+        "ascii"
+    )  # remove non-ascii, Chinese characters
+    text = re.sub(r"\n", " ", text)
+    text = re.sub(r"\n\n", " ", text)
+    text = re.sub(r"\t", " ", text)
+    text = text.strip(" ")
+    text = re.sub(
+        " +", " ", text
+    ).strip()  # get rid of multiple spaces and replace with a single
+    return text
+
+modchoice_1 = "chinhon/headline_writer"
+
+def headline_writer1(text):
+    input_text = clean_text(text)
+
+    tokenizer_1 = AutoTokenizer.from_pretrained(modchoice_1)
+
+    model_1 = AutoModelForSeq2SeqLM.from_pretrained(modchoice_1)
+
+    with tokenizer_1.as_target_tokenizer():
+        batch = tokenizer_1(
+            input_text, truncation=True, padding="longest", return_tensors="pt"
+        )
+
+    translated = model_1.generate(**batch)
+
+    summary_1 = tokenizer_1.batch_decode(translated, skip_special_tokens=True)
+
+    return summary_1[0]
+
+
+headline1 = gr.Interface(
+    fn=headline_writer1,
+    inputs=gr.inputs.Textbox(),
+    outputs=gr.outputs.Textbox(label=""),
+)
+
+
+modchoice_2 = "chinhon/pegasus-multi_news-headline"
+
+def headline_writer2(text):
+    input_text = clean_text(text)
+
+    tokenizer_2 = AutoTokenizer.from_pretrained(modchoice_2)
+
+    model_2 = AutoModelForSeq2SeqLM.from_pretrained(modchoice_2)
+
+    with tokenizer_2.as_target_tokenizer():
+        batch = tokenizer_2(
+            input_text, truncation=True, padding="longest", return_tensors="pt"
+        )
+
+    translated = model_2.generate(**batch)
+
+    summary_2 = tokenizer_2.batch_decode(translated, skip_special_tokens=True)
+
+    return summary_2[0]
+
+
+headline2 = gr.Interface(
+    fn=headline_writer2,
+    inputs=gr.inputs.Textbox(),
+    outputs=gr.outputs.Textbox(label=""),
+)
+
+
+modchoice_3 = "chinhon/pegasus-newsroom-headline_writer"
+
+def headline_writer3(text):
+    input_text = clean_text(text)
+
+    tokenizer_3 = AutoTokenizer.from_pretrained(modchoice_3)
+
+    model_3 = AutoModelForSeq2SeqLM.from_pretrained(modchoice_3)
+
+    with tokenizer_3.as_target_tokenizer():
+        batch = tokenizer_3(
+            input_text, truncation=True, padding="longest", return_tensors="pt"
+        )
+
+    translated = model_3.generate(**batch)
+
+    summary_3 = tokenizer_3.batch_decode(
+        translated, skip_special_tokens=True, max_length=100
+    )
+
+    return summary_3[0]
+
+
+headline3 = gr.Interface(
+    fn=headline_writer3,
+    inputs=gr.inputs.Textbox(),
+    outputs=gr.outputs.Textbox(label=""),
+)
+
+
+Parallel(
+    headline1,
+    headline2,
+    headline3,
+    title="AI Headlines Generator",
+    inputs=gr.inputs.Textbox(
+        lines=20,
+        label="Paste the first few paragraphs of your story here, and choose from 3 suggested headlines",
+    ),
+    theme="darkhuggingface",
+).launch(enable_queue=True)