Delete app.py

app.py

@@ -1,119 +0,0 @@
-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()