add python code
Browse files
app.py
CHANGED
@@ -1,7 +1,9 @@
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import ast
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import glob
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from itertools import islice
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from functools import partial
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from typing import Optional, Type
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import gradio as gr
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@@ -26,6 +28,7 @@ from datatrove.utils.typeshelper import Languages
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nltk.download('punkt_tab')
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DUMP_TO_PROCESS = "CC-MAIN-2023-50"
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make_gallery_image_buttons_js = """
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function load() {
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@@ -281,9 +284,7 @@ with gr.Blocks(css=css, js=make_gallery_image_buttons_js) as demo:
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gopher_filtering_quality_checkbox.change(lambda visible: gr.Accordion(visible=visible), inputs=gopher_filtering_quality_checkbox, outputs=acc)
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gopher_filtering_quality_parameters_components = [language_dropdown2, min_doc_words_slider, max_doc_words_slider, min_avg_word_length_slider, max_avg_word_length_slider, max_symbol_word_ratio_slider, max_bullet_lines_ratio_slider, max_ellipsis_lines_ratio_slider, max_non_alpha_words_ratio_slider, min_stop_words_slider, stop_words_textbox]
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view_pipeline_results_button = gr.Button("Run Pipeline & Stream Results", variant="primary", scale=4)
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stop_button = gr.Button("Stop")
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steps = [
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URLFilter,
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@@ -340,7 +341,6 @@ with gr.Blocks(css=css, js=make_gallery_image_buttons_js) as demo:
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pii_removal_checkbox
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] + sum(steps_parameters_components, [])
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@view_pipeline_results_button.click(inputs=inputs, outputs=[output_tab, output_dataframe, excluded_tab] + list(excluded_dataframes.values()) + list(excluded_tabs.values()))
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def view_pipeline_results(*args):
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enable_steps, steps_parameters = args[:len(steps)], args[len(steps):]
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steps_parameters_iter = iter(steps_parameters)
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@@ -358,6 +358,43 @@ with gr.Blocks(css=css, js=make_gallery_image_buttons_js) as demo:
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}
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for step_parameters_components in steps_parameters_components
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]
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class ExclusionWriter:
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@@ -380,19 +417,28 @@ with gr.Blocks(css=css, js=make_gallery_image_buttons_js) as demo:
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]
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output_docs: list[Document] = []
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num_warc_samples = 0
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def increment_num_warc_samples(data, rank, world_size, num_warc_samples_per_doc=1):
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nonlocal num_warc_samples
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for x in data:
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num_warc_samples += num_warc_samples_per_doc
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yield x
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if steps_parameters[:2] == default_steps_parameters[:2] and all(enable_steps[:2]):
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pipeline_executor = LocalPipelineExecutor(
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pipeline=[
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JsonlReader(data_folder=f"output_text_extraction-full/base_processing/output/{DUMP_TO_PROCESS}", glob_pattern="*.jsonl.gz"),
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partial(increment_num_warc_samples, num_warc_samples_per_doc=2000 / 1687)
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] + steps_to_run[2:] + [
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lambda data, rank, world_size: islice(data, 100),
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lambda data, rank, world_size: map(output_docs.append, data)
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@@ -404,7 +450,8 @@ with gr.Blocks(css=css, js=make_gallery_image_buttons_js) as demo:
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pipeline_executor = LocalPipelineExecutor(
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pipeline=[
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WarcReader(data_folder="data", glob_pattern="*.warc.gz"),
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increment_num_warc_samples
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] + steps_to_run + [
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lambda data, rank, world_size: islice(data, 100),
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lambda data, rank, world_size: map(output_docs.append, data)
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@@ -465,7 +512,7 @@ with gr.Blocks(css=css, js=make_gallery_image_buttons_js) as demo:
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},
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}
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-
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if __name__ == "__main__":
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demo.launch()
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import ast
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import glob
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import time
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from itertools import islice
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from functools import partial
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from textwrap import dedent
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from typing import Optional, Type
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import gradio as gr
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nltk.download('punkt_tab')
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DUMP_TO_PROCESS = "CC-MAIN-2023-50"
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TIMEOUT = 600
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make_gallery_image_buttons_js = """
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function load() {
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gopher_filtering_quality_checkbox.change(lambda visible: gr.Accordion(visible=visible), inputs=gopher_filtering_quality_checkbox, outputs=acc)
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gopher_filtering_quality_parameters_components = [language_dropdown2, min_doc_words_slider, max_doc_words_slider, min_avg_word_length_slider, max_avg_word_length_slider, max_symbol_word_ratio_slider, max_bullet_lines_ratio_slider, max_ellipsis_lines_ratio_slider, max_non_alpha_words_ratio_slider, min_stop_words_slider, stop_words_textbox]
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view_pipeline_results_button = gr.Button("Run Pipeline & Stream Results", variant="primary", scale=4)
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steps = [
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URLFilter,
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pii_removal_checkbox
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] + sum(steps_parameters_components, [])
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def view_pipeline_results(*args):
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enable_steps, steps_parameters = args[:len(steps)], args[len(steps):]
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steps_parameters_iter = iter(steps_parameters)
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}
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for step_parameters_components in steps_parameters_components
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]
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yield {
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python_code_markdown: dedent(
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"""
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```python
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from datatrove.executor.local import LocalPipelineExecutor
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from datatrove.pipeline.extractors import Trafilatura
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from datatrove.pipeline.filters import (
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C4QualityFilter,
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FineWebQualityFilter,
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GopherQualityFilter,
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GopherRepetitionFilter,
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LanguageFilter,
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URLFilter,
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)
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from datatrove.pipeline.formatters import PIIFormatter
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from datatrove.pipeline.readers import WarcReader
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"""
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).strip() + (
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"\n\n"
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"pipeline_executor = LocalPipelineExecutor(\n"
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" pipeline=[\n"
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f' WarcReader("s3://commoncrawl/crawl-data/{DUMP_TO_PROCESS}/segments", glob_pattern="*/warc/*"),\n'
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) + ",\n".join([
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" " + step.__name__ + "(" + ", ".join(arg + "=" + str(value) for arg, value in step_parameters.items() if value != default_step_parameters[arg] and arg != "exclusion_writer") + ")"
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for step, step_parameters, default_step_parameters, enable_step in zip(steps, steps_parameters, default_steps_parameters, enable_steps)
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if enable_step
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]) + (
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"\n"
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" ]\n"
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")"
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) + dedent(
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"""
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pipeline_executor.run()
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```
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"""
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)
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}
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class ExclusionWriter:
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]
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output_docs: list[Document] = []
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num_warc_samples = 0
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timeout_time = time.time() + TIMEOUT
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def increment_num_warc_samples(data, rank, world_size, num_warc_samples_per_doc=1):
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nonlocal num_warc_samples
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for x in data:
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num_warc_samples += num_warc_samples_per_doc
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yield x
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def check_timeout(data, rank, world_size):
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for x in data:
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if time.time() > timeout_time:
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gr.Info("Pipeline timed out")
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break
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yield x
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if steps_parameters[:2] == default_steps_parameters[:2] and all(enable_steps[:2]):
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pipeline_executor = LocalPipelineExecutor(
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pipeline=[
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JsonlReader(data_folder=f"output_text_extraction-full/base_processing/output/{DUMP_TO_PROCESS}", glob_pattern="*.jsonl.gz"),
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partial(increment_num_warc_samples, num_warc_samples_per_doc=2000 / 1687),
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check_timeout
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] + steps_to_run[2:] + [
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lambda data, rank, world_size: islice(data, 100),
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lambda data, rank, world_size: map(output_docs.append, data)
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pipeline_executor = LocalPipelineExecutor(
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pipeline=[
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WarcReader(data_folder="data", glob_pattern="*.warc.gz"),
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increment_num_warc_samples,
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check_timeout
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] + steps_to_run + [
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lambda data, rank, world_size: islice(data, 100),
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lambda data, rank, world_size: map(output_docs.append, data)
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},
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}
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view_pipeline_results_button.click(view_pipeline_results, inputs=inputs, outputs=[output_tab, output_dataframe, excluded_tab, python_code_markdown] + list(excluded_dataframes.values()) + list(excluded_tabs.values()))
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if __name__ == "__main__":
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demo.launch()
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