:sparkles: add app mwe

app.py

@@ -0,0 +1,153 @@
+import logging
+from pathlib import Path
+import os
+import gradio as gr
+import nltk
+import torch
+from cleantext import clean
+
+from summarize import load_model_and_tokenizer, summarize_via_tokenbatches
+
+_here = Path(__file__).parent
+
+nltk.download("stopwords")  # TODO=find where this requirement originates from
+
+import transformers
+
+transformers.logging.set_verbosity_error()
+logging.basicConfig()
+
+def truncate_word_count(text, max_words=512):
+    """
+    truncate_word_count - a helper function for the gradio module
+    Parameters
+    ----------
+    text : str, required, the text to be processed
+    max_words : int, optional, the maximum number of words, default=512
+    Returns
+    -------
+    dict, the text and whether it was truncated
+    """
+    words = text.split()
+    processed = {}
+    if len(words) > max_words:
+        processed["was_truncated"] = True
+        processed["truncated_text"] = " ".join(words[:max_words])
+    else:
+        processed["was_truncated"] = False
+        processed["truncated_text"] = text
+    return processed
+
+def proc_submission(
+    input_text: str,
+    num_beams,
+    length_penalty,
+    repetition_penalty,
+    no_repeat_ngram_size,
+    token_batch_length,
+    max_input_length: int = 512,
+):
+    """
+    proc_submission - a helper function for the gradio module
+    Parameters
+    ----------
+    input_text : str, required, the text to be processed
+    max_input_length : int, optional, the maximum length of the input text, default=512
+    Returns
+    -------
+    str of HTML, the interactive HTML form for the model
+    """
+
+    settings = {
+        "length_penalty": length_penalty,
+        "repetition_penalty": repetition_penalty,
+        "no_repeat_ngram_size": no_repeat_ngram_size,
+        "encoder_no_repeat_ngram_size": 4,
+        "num_beams": num_beams,
+    }
+
+    history = {}
+    clean_text = clean(input_text, lower=False)
+    processed = truncate_word_count(clean_text, max_input_length)
+    if processed["was_truncated"]:
+        history["input_text"] = processed["truncated_text"]
+        history["was_truncated"] = True
+        msg = f"Input text was truncated to {max_input_length} characters."
+        logging.warning(msg)
+        history["WARNING"] = msg
+    else:
+        history["input_text"] = input_text
+        history["was_truncated"] = False
+
+    _summaries = summarize_via_tokenbatches(
+                    history["input_text"],
+                    model, tokenizer,
+                    batch_length=token_batch_length,
+                    **settings,
+                )
+    sum_text = [s['summary'][0] for s in _summaries]
+    sum_scores = [f"\n - {round(s['summary_score'],4)}" for s in _summaries]
+
+
+    history["Input"] = input_text
+    history["Summary Text"] = "\n\t".join(sum_text)
+    history["Summary Scores"] = "\n".join(sum_scores)
+    html = ""
+    for name, item in history.items():
+        html += (
+            f"<h2>{name}:</h2><hr><b>{item}</b><br><br>"
+            if "summary" not in name.lower()
+            else f"<h2>{name}:</h2><hr><b>{item}</b>"
+        )
+
+    html += ""
+
+    return html
+
+def load_examples(examples_dir='examples'):
+    src = _here / examples_dir
+    src.mkdir(exist_ok=True)
+    examples = [f for f in src.glob("*.txt")]
+    # load the examples into a list
+    text_examples = []
+    for example in examples:
+        with open(example, "r") as f:
+            text = f.read()
+            text_examples.append([text, 4, 2048, 0.7,3.5,3])
+
+    return text_examples
+
+if __name__ == "__main__":
+
+    model, tokenizer = load_model_and_tokenizer('pszemraj/led-large-book-summary')
+    title = "Long-form text summarization with LED on the BookSumm dataset"
+    description = "This is a simple example of using the LED model to summarize a long-form text."
+
+    gr.Interface(
+        proc_submission,
+        inputs=[
+            gr.inputs.Textbox(lines=10, label="input text"),
+            gr.inputs.Slider(
+                minimum=4, maximum=10, label="num_beams", default=4, step=1
+            ),
+            gr.Dropdown(choices=[512, 1024, 2048, 4096], label="token_batch_length", default=2048),
+            gr.inputs.Slider(
+                minimum=0.5, maximum=1.1, label="length_penalty", default=0.7, step=0.05
+            ),
+            gr.inputs.Slider(
+                minimum=1.0,
+                maximum=5.0,
+                label="repetition_penalty",
+                default=3.5,
+                step=0.1,
+            ),
+            gr.inputs.Slider(
+                minimum=2, maximum=4, label="no_repeat_ngram_size", default=3, step=1
+            ),
+        ],
+        outputs="html",
+        examples_per_page=4,
+        title=title,
+        description=description,
+        examples=load_examples(),
+    ).launch(enable_queue=True, share=True)