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app.py

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+import logging
+import time
+from pathlib import Path
+
+import gradio as gr
+import nltk
+from cleantext import clean
+
+from summarize import load_model_and_tokenizer, summarize_via_tokenbatches
+from utils import load_example_filenames, truncate_word_count
+
+_here = Path(__file__).parent
+
+nltk.download("stopwords")  # TODO=find where this requirement originates from
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+
+def proc_submission(
+    input_text: str,
+    model_size: str,
+    num_beams,
+    token_batch_length,
+    length_penalty,
+    repetition_penalty,
+    no_repeat_ngram_size,
+    max_input_length: int = 768,
+):
+    """
+    proc_submission - a helper function for the gradio module to process submissions
+    Args:
+        input_text (str): the input text to summarize
+        model_size (str): the size of the model to use
+        num_beams (int): the number of beams to use
+        token_batch_length (int): the length of the token batches to use
+        length_penalty (float): the length penalty to use
+        repetition_penalty (float): the repetition penalty to use
+        no_repeat_ngram_size (int): the no repeat ngram size to use
+        max_input_length (int, optional): the maximum input length to use. Defaults to 768.
+    Returns:
+        str in HTML format, string of the summary, str of score
+    """
+
+    settings = {
+        "length_penalty": float(length_penalty),
+        "repetition_penalty": float(repetition_penalty),
+        "no_repeat_ngram_size": int(no_repeat_ngram_size),
+        "encoder_no_repeat_ngram_size": 4,
+        "num_beams": int(num_beams),
+        "min_length": 4,
+        "max_length": int(token_batch_length // 4),
+        "early_stopping": True,
+        "do_sample": False,
+    }
+    st = time.perf_counter()
+    history = {}
+    clean_text = clean(input_text, lower=False)
+    max_input_length = 2048 if model_size == "base" else max_input_length
+    processed = truncate_word_count(clean_text, max_input_length)
+
+    if processed["was_truncated"]:
+        tr_in = processed["truncated_text"]
+        msg = f"Input text was truncated to {max_input_length} words (based on whitespace)"
+        logging.warning(msg)
+        history["WARNING"] = msg
+    else:
+        tr_in = input_text
+        msg = None
+
+    _summaries = summarize_via_tokenbatches(
+        tr_in,
+        model_sm if model_size == "base" else model,
+        tokenizer_sm if model_size == "base" else tokenizer,
+        batch_length=token_batch_length,
+        **settings,
+    )
+    sum_text = [f"Section {i}: " + s["summary"][0] for i, s in enumerate(_summaries)]
+    sum_scores = [
+        f" - Section {i}: {round(s['summary_score'],4)}"
+        for i, s in enumerate(_summaries)
+    ]
+
+    sum_text_out = "\n".join(sum_text)
+    history["Summary Scores"] = "<br><br>"
+    scores_out = "\n".join(sum_scores)
+    rt = round((time.perf_counter() - st) / 60, 2)
+    print(f"Runtime: {rt} minutes")
+    html = ""
+    html += f"<p>Runtime: {rt} minutes on CPU</p>"
+    if msg is not None:
+        html += f"<h2>WARNING:</h2><hr><b>{msg}</b><br><br>"
+
+    html += ""
+
+    return html, sum_text_out, scores_out
+
+
+def load_single_example_text(
+    example_path: str or Path,
+):
+    """
+    load_single_example - a helper function for the gradio module to load examples
+    Returns:
+        list of str, the examples
+    """
+    global name_to_path
+    full_ex_path = name_to_path[example_path]
+    full_ex_path = Path(full_ex_path)
+    # load the examples into a list
+    with open(full_ex_path, "r", encoding="utf-8", errors="ignore") as f:
+        raw_text = f.read()
+        text = clean(raw_text, lower=False)
+    return text
+
+
+def load_uploaded_file(file_obj):
+    """
+    load_uploaded_file - process an uploaded file
+    Args:
+        file_obj (POTENTIALLY list): Gradio file object inside a list
+    Returns:
+        str, the uploaded file contents
+    """
+
+    # file_path = Path(file_obj[0].name)
+
+    # check if mysterious file object is a list
+    if isinstance(file_obj, list):
+        file_obj = file_obj[0]
+    file_path = Path(file_obj.name)
+    try:
+        with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
+            raw_text = f.read()
+        text = clean(raw_text, lower=False)
+        return text
+    except Exception as e:
+        logging.info(f"Trying to load file with path {file_path}, error: {e}")
+        return "Error: Could not read file. Ensure that it is a valid text file with encoding UTF-8."
+
+
+if __name__ == "__main__":
+
+    model, tokenizer = load_model_and_tokenizer("pszemraj/led-large-book-summary")
+    model_sm, tokenizer_sm = load_model_and_tokenizer("pszemraj/led-base-book-summary")
+
+    name_to_path = load_example_filenames(_here / "examples")
+    logging.info(f"Loaded {len(name_to_path)} examples")
+    demo = gr.Blocks()
+
+    with demo:
+
+        gr.Markdown("# Long-Form Summarization: LED & BookSum")
+        gr.Markdown(
+            "A simple demo using a fine-tuned LED model to summarize long-form text. See [model card](https://huggingface.co/pszemraj/led-large-book-summary) for a notebook with GPU inference (much faster) on Colab."
+        )
+        with gr.Column():
+
+            gr.Markdown("## Load Inputs & Select Parameters")
+            gr.Markdown(
+                "Enter text below in the text area. The text will be summarized [using the selected parameters](https://huggingface.co/blog/how-to-generate). Optionally load an example below or upload a file."
+            )
+            with gr.Row():
+                model_size = gr.Radio(
+                    choices=["base", "large"], label="Model Variant", value="large"
+                )
+                num_beams = gr.Radio(
+                    choices=[2, 3, 4],
+                    label="Beam Search: # of Beams",
+                    value=2,
+                )
+            gr.Markdown(
+                "_The base model is less performant than the large model, but is faster and will accept up to 2048 words per input (Large model accepts up to 768)._"
+            )
+            with gr.Row():
+                length_penalty = gr.inputs.Slider(
+                    minimum=0.5,
+                    maximum=1.0,
+                    label="length penalty",
+                    default=0.7,
+                    step=0.05,
+                )
+                token_batch_length = gr.Radio(
+                    choices=[512, 768, 1024],
+                    label="token batch length",
+                    value=512,
+                )
+
+            with gr.Row():
+                repetition_penalty = gr.inputs.Slider(
+                    minimum=1.0,
+                    maximum=5.0,
+                    label="repetition penalty",
+                    default=3.5,
+                    step=0.1,
+                )
+                no_repeat_ngram_size = gr.Radio(
+                    choices=[2, 3, 4],
+                    label="no repeat ngram size",
+                    value=3,
+                )
+            with gr.Row():
+                example_name = gr.Dropdown(
+                    list(name_to_path.keys()),
+                    label="Choose an Example",
+                )
+                load_examples_button = gr.Button(
+                    "Load Example",
+                )
+            input_text = gr.Textbox(
+                lines=6,
+                label="Input Text (for summarization)",
+                placeholder="Enter text to summarize, the text will be cleaned and truncated on Spaces. Narrative, academic (both papers and lecture transcription), and article text work well. May take a bit to generate depending on the input text :)",
+            )
+            gr.Markdown("Upload your own file:")
+            with gr.Row():
+                uploaded_file = gr.File(
+                    label="Upload a text file",
+                    file_count="single",
+                    type="file",
+                )
+                load_file_button = gr.Button("Load Uploaded File")
+
+            gr.Markdown("---")
+
+        with gr.Column():
+            gr.Markdown("## Generate Summary")
+            gr.Markdown(
+                "Summary generation should take approximately 1-2 minutes for most settings."
+            )
+            summarize_button = gr.Button(
+                "Summarize!",
+                variant="primary",
+            )
+
+            output_text = gr.HTML("<p><em>Output will appear below:</em></p>")
+            gr.Markdown("### Summary Output")
+            summary_text = gr.Textbox(
+                label="Summary", placeholder="The generated summary will appear here"
+            )
+            gr.Markdown(
+                "The summary scores can be thought of as representing the quality of the summary. less-negative numbers (closer to 0) are better:"
+            )
+            summary_scores = gr.Textbox(
+                label="Summary Scores", placeholder="Summary scores will appear here"
+            )
+
+            gr.Markdown("---")
+
+        with gr.Column():
+            gr.Markdown("## About the Model")
+            gr.Markdown(
+                "- [This model](https://huggingface.co/pszemraj/led-large-book-summary) is a fine-tuned checkpoint of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) on the [BookSum dataset](https://arxiv.org/abs/2105.08209).The goal was to create a model that can generalize well and is useful in summarizing lots of text in academic and daily usage."
+            )
+            gr.Markdown(
+                "- The two most important parameters-empirically-are the `num_beams` and `token_batch_length`. However, increasing these will also increase the amount of time it takes to generate a summary. The `length_penalty` and `repetition_penalty` parameters are also important for the model to generate good summaries."
+            )
+            gr.Markdown(
+                "- The model can be used with tag [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). See the model card for details on usage & a notebook for a tutorial."
+            )
+            gr.Markdown("---")
+
+        load_examples_button.click(
+            fn=load_single_example_text, inputs=[example_name], outputs=[input_text]
+        )
+
+        load_file_button.click(
+            fn=load_uploaded_file, inputs=uploaded_file, outputs=[input_text]
+        )
+
+        summarize_button.click(
+            fn=proc_submission,
+            inputs=[
+                input_text,
+                model_size,
+                num_beams,
+                token_batch_length,
+                length_penalty,
+                repetition_penalty,
+                no_repeat_ngram_size,
+            ],
+            outputs=[output_text, summary_text, summary_scores],
+        )
+
+    demo.launch(enable_queue=True, share=True)