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| import openai | |
| import gradio as gr | |
| from langchain_openai import OpenAIEmbeddings | |
| from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter | |
| from langchain.vectorstores.faiss import FAISS | |
| from langchain.chains.question_answering import load_qa_chain | |
| from langchain.chains import LLMChain | |
| from langchain_community.llms import OpenAI | |
| from langchain.prompts import PromptTemplate | |
| from langchain.docstore.document import Document | |
| import pandas as pd | |
| import os | |
| import scipdf ## You need a Gorbid service available | |
| import tabula ## You need to have the Java Tabula installed in the environment | |
| from gradio import DataFrame | |
| import asyncio | |
| from transformers import pipeline | |
| from dotenv import load_dotenv | |
| import json | |
| from src.extractor import Extractor | |
| load_dotenv() | |
| ## You api key from vendors or hugginface | |
| #openai.api_key=os.getenv("OPEN_AI_API_KEY") | |
| #LLMClient = OpenAI(model_name='text-davinci-003', openai_api_key=openai.api_key,temperature=0) | |
| extractor = Extractor() | |
| print(os.getenv("OPEN_AI_API_KEY")) | |
| # Define function to handle the Gradio interface | |
| async def extraction(input_file, apikey, dimension): | |
| # Build the chains | |
| chain_incontext, chain_table = extractor.build_chains(apikey) | |
| # Prepare the data | |
| docsearch = await extractor.prepare_data(input_file, chain_table, apikey) | |
| # Extract dimensions | |
| if (dimension == "annotation"): | |
| results, completeness_report = await extractor.get_annotation_dimension(docsearch,chain_incontext, retrieved_docs=10) | |
| elif (dimension == "gathering"): | |
| results, completeness_report = await extractor.get_gathering_dimension(docsearch,chain_incontext, retrieved_docs=10) | |
| elif (dimension == "uses"): | |
| results, completeness_report = await extractor.get_uses_dimension(docsearch,chain_incontext, retrieved_docs=10) | |
| elif (dimension == "contrib"): | |
| results, completeness_report = await extractor.get_contributors_dimension(docsearch,chain_incontext, retrieved_docs=10) | |
| elif (dimension == "comp"): | |
| results, completeness_report = await extractor.get_composition_dimension(docsearch,chain_incontext, retrieved_docs=10) | |
| elif (dimension == "social"): | |
| results, completeness_report = await extractor.get_social_concerns_dimension(docsearch,chain_incontext, retrieved_docs=10) | |
| elif (dimension == "dist"): | |
| results, completeness_report = await extractor.get_distribution_dimension(docsearch,chain_incontext, retrieved_docs=10) | |
| # Get completeness report | |
| #completeness_report = extractor.postprocessing(results) | |
| return results, completeness_report | |
| async def ui_extraction(input_file, apikey, dimension): | |
| if (input_file == None): | |
| raise gr.Error("Please upload a data paper") | |
| if (input_file.name.split(".")[-1] != "pdf"): | |
| raise gr.Error("This is not a data paper!, please upload it in .pdf format") | |
| if (len(apikey) == 0): | |
| raise gr.Error("Please inform your OpenAI Apikey") | |
| file_name = input_file.name.split("/")[-1] | |
| results, completeness_report = await extractor.extraction(file_name, input_file.name, apikey, dimension) | |
| # Build results in the correct format for the Gradio front-end | |
| results = pd.DataFrame(results, columns=['Dimension', 'Results']) | |
| return results, gr.update(value=pd.DataFrame(completeness_report['report'],columns=['Completeness report: '+str(completeness_report['completeness'])+'%']), visible=True) | |
| async def complete(input_file): | |
| file_name = input_file.name.split("/")[-1] | |
| # Build the chains | |
| chain_incontext, chain_table = extractor.build_chains(apikey=os.getenv("OPEN_AI_API_KEY")) | |
| # Prepare the data | |
| docsearch = await extractor.prepare_data(file_name, input_file.name, chain_table, apikey=os.getenv("OPEN_AI_API_KEY")) | |
| #Retrieve dimensions | |
| results = await asyncio.gather(extractor.get_annotation_dimension(docsearch,chain_incontext, retrieved_docs=10), | |
| extractor.get_gathering_dimension(docsearch,chain_incontext, retrieved_docs=10), | |
| extractor.get_uses_dimension(docsearch,chain_incontext, retrieved_docs=10), | |
| extractor.get_contributors_dimension(docsearch,chain_incontext, retrieved_docs=10), | |
| extractor.get_composition_dimension(docsearch,chain_incontext, retrieved_docs=10), | |
| extractor.get_social_concerns_dimension(docsearch,chain_incontext, retrieved_docs=10), | |
| extractor.get_distribution_dimension(docsearch,chain_incontext, retrieved_docs=10)) | |
| # Get completeness report from the results | |
| warnings = [] | |
| extracts = [] | |
| for result in results: | |
| extracts.append(result[0]) | |
| warnings.append(gr.update(value=pd.DataFrame(result[1]['report'],columns=['Completeness report: '+str(result[1]['completeness'])+'%']), visible=True)) | |
| extracts.extend(warnings) | |
| return extracts | |
| ## Building the layout of the app | |
| css = """.table-wrap.scroll-hide.svelte-8hrj8a.no-wrap { | |
| white-space: normal; | |
| } | |
| #component-7 .wrap.svelte-xwlu1w { | |
| min-height: var(--size-40); | |
| } | |
| div#component-2 h2 { | |
| color: var(--block-label-text-color); | |
| text-align: center; | |
| border-radius: 7px; | |
| text-align: center; | |
| margin: 0 15% 0 15%; | |
| } | |
| div#component-5 { | |
| border: 1px solid var(--border-color-primary); | |
| border-radius: 0 0px 10px 10px; | |
| padding: 20px; | |
| } | |
| .gradio-container.gradio-container-3-26-0.svelte-ac4rv4.app { | |
| max-width: 850px; | |
| } | |
| div#component-6 { | |
| min-height: 150px; | |
| } | |
| button#component-17 { | |
| color: var(--block-label-text-color); | |
| } | |
| .gradio-container.gradio-container-3-26-0.svelte-ac4rv4.app { | |
| max-width: 1100px; | |
| } | |
| #component-9 .wrap.svelte-xwlu1w { | |
| min-height: var(--size-40); | |
| } | |
| div#component-11 { | |
| height: var(--size-40); | |
| } | |
| div#component-9 { | |
| border: 1px solid grey; | |
| border-radius: 10px; | |
| padding: 3px; | |
| text-align: center; | |
| } | |
| """ | |
| with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo: | |
| with gr.Row(): | |
| gr.Markdown("## DataDoc Analyzer") | |
| with gr.Row(): | |
| gr.Markdown("""Extract, in a structured manner, the **[general guidelines](https://knowingmachines.org/reading-list#dataset_documentation_practices)** from the ML community about dataset documentation practices from its scientific documentation. Study and analyze scientific data published in peer-review journals such as: **[Nature's Scientific Data](https://www.nature.com/sdata/)** and **[Data-in-Brief](https://www.data-in-brief.com)**. Here you have a **[complete list](https://zenodo.org/record/7082126#.ZDaf-OxBz0p)** of data journals suitable to be analyzed with this tool. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| fileinput = gr.File(label="Upload the dataset documentation"), | |
| with gr.Column(): | |
| gr.Markdown(""" <h4 style=text-align:center>Instructions: </h4> | |
| <b> ⤵ Try the examples </b> at the bottom | |
| <b> then </b> | |
| <b> ⇨ Set your API key </b> of OpenAI | |
| <b> ⇦ Upload </b> your data paper (in PDF or TXT) | |
| <b> ⇩ Click in get insights </b> in one tab! | |
| """) | |
| with gr.Column(): | |
| apikey_elem = gr.Text(label="OpenAI API key", type="password") | |
| # gr.Markdown(""" | |
| # <h3> Improving your data and assesing your dataset documentation </h3> | |
| # The generated warning also allows you quicly check the completeness of the documentation, and spotting gaps in the document | |
| # <h3> Performing studies studies over scientific data </h3> | |
| # If you need to analyze a large scale of documents, we provide an <strong>API</strong> that can be used programatically. Documentation on how to use it is at the bottom of the page. """) | |
| with gr.Row(): | |
| with gr.Tab("Annotation"): | |
| gr.Markdown("""In this dimension, you can get information regarding the annotation process of the data: Extract a description of the process and infer its type. Extract the labels and information about the annotation team, the infrastructure used to annotate the data, and the validation process applied to the labels.""") | |
| result_anot = gr.DataFrame(headers=["dimension","result"],type="array",label="Results of the extraction:") | |
| alerts_anot = gr.DataFrame(headers=["warnings"],type="array", visible=False) | |
| button_annotation = gr.Button("Get the annotation process insights!") | |
| with gr.Tab("Gathering"): | |
| gr.Markdown("""In this dimension, we get information regarding the collection process of the data: We provide a description of the process and we infer its type from the documentation. Then we extract information about the collection team, the infrastructure used to collect the data and the sources. Also we get the timeframe of the data collection and its geolocalization.""") | |
| result_gather = gr.DataFrame(headers=["dimension","result"],type="array",label="Results of the extraction:") | |
| alerts_gather = gr.DataFrame(headers=["warnings"],type="array", visible=False) | |
| button_gathering = gr.Button("Get the gathering process insights!") | |
| with gr.Tab("Uses"): | |
| gr.Markdown("""In this dimension, we extract the design intentios of the authors, we extract the purposes, gaps, and we infer the ML tasks (extracted form hugginface) the dataset is inteded for. Also we get the uses recomendation and the ML Benchmarks if the dataset have been tested with them""") | |
| result_uses = gr.DataFrame(headers=["dimension","result"],type="array",label="Results of the extraction:") | |
| alerts_uses = gr.DataFrame(headers=["warnings"],type="array", visible=False) | |
| button_uses = gr.Button("Get the uses of the dataset!") | |
| with gr.Tab("Contributors"): | |
| gr.Markdown("""In this dimension, we extract all the contributors, funding information and maintenance of the dataset""") | |
| result_contrib = gr.DataFrame(headers=["dimension","result"],type="array",label="Results of the extraction:") | |
| alerts_contrib = gr.DataFrame(headers=["warnings"],type="array", visible=False) | |
| button_contrib = gr.Button("Get the contributors of the dataset!") | |
| with gr.Tab("Composition"): | |
| gr.Markdown("""In this dimension, we extract the file structure, we identify the attributes of the dataset, the recommneded trainig splits and the relevant statistics (if provided in the documentation) """) | |
| result_comp = gr.DataFrame(headers=["dimension","result"],type="array",label="Results of the extraction:") | |
| alerts_comp = gr.DataFrame(headers=["warnings"],type="array", visible=False) | |
| button_comp = gr.Button("Get the composition of the dataset!") | |
| with gr.Tab("Social Concerns"): | |
| gr.Markdown("""In this dimension, we extract social concerns regarding the representativeness of social groups, potential biases, sensitivity issues, and privacy issues. """) | |
| result_social = gr.DataFrame(headers=["dimension","result"],type="array",label="Results of the extraction:") | |
| alerts_social = gr.DataFrame(headers=["warnings"],type="array", visible=False) | |
| button_social = gr.Button("Get the Social Cocerns!") | |
| with gr.Tab("Distribution"): | |
| gr.Markdown("""In this dimension, we aim to extract the legal conditions under the dataset is released) """) | |
| result_distri = gr.DataFrame(headers=["dimension","result"],type="array",label="Results of the extraction:") | |
| alerts_distribution = gr.DataFrame(headers=["warning"],type="array", visible=False) | |
| button_dist = gr.Button("Get the Distribution!") | |
| with gr.Row(): | |
| examples = gr.Examples( | |
| examples=["sources/Nature-Scientific-Data/A whole-body FDG-PET:CT.pdf","sources/Nature-Scientific-Data/Lontar-Manuscripts.pdf"], | |
| inputs=[fileinput[0]], | |
| fn=complete, | |
| outputs=[ | |
| result_anot, | |
| result_gather, | |
| result_uses, | |
| result_contrib, | |
| result_comp, | |
| result_social, | |
| result_distri, | |
| alerts_anot, | |
| alerts_gather, | |
| alerts_uses, | |
| alerts_contrib, | |
| alerts_comp, | |
| alerts_social, | |
| alerts_distribution], | |
| cache_examples=True) | |
| button_complete = gr.Button("Get all the dimensions", visible=False) | |
| allres = gr.Text(visible=False) | |
| ## Events of the apps | |
| button_annotation.click(ui_extraction,inputs=[fileinput[0],apikey_elem,gr.State(value="annotation")],outputs=[result_anot,alerts_anot]) | |
| button_gathering.click(ui_extraction,inputs=[fileinput[0],apikey_elem,gr.State("gathering") ],outputs=[result_gather,alerts_gather]) | |
| button_uses.click(ui_extraction,inputs=[fileinput[0],apikey_elem,gr.State("uses") ],outputs=[result_uses,alerts_uses]) | |
| button_contrib.click(ui_extraction,inputs=[fileinput[0],apikey_elem,gr.State("contrib") ],outputs=[result_contrib,alerts_contrib]) | |
| button_comp.click(ui_extraction,inputs=[fileinput[0],apikey_elem,gr.State("comp") ],outputs=[result_comp,alerts_comp]) | |
| button_social.click(ui_extraction,inputs=[fileinput[0],apikey_elem,gr.State("social") ],outputs=[result_social,alerts_social]) | |
| button_dist.click(ui_extraction,inputs=[fileinput[0],apikey_elem,gr.State("dist") ],outputs=[result_distri,alerts_distribution]) | |
| ## API endpoints | |
| #api_annotation = gr.Button(visible=False) | |
| #api_annotation.click(api_extraction,inputs=[fileinput[0],apikey_elem,gr.State(value="annotation")],outputs=[result_anot,alerts_anot], api_name="annotation") | |
| #api_gathering = gr.Button(visible=False) | |
| #api_gathering.click(api_extraction,inputs=[fileinput[0],apikey_elem,gr.State(value="gathering")],outputs=[result_anot,alerts_anot], api_name="gathering") | |
| #api_uses = gr.Button(visible=False) | |
| #api_uses.click(api_extraction,inputs=[fileinput[0],apikey_elem,gr.State(value="uses")],outputs=[result_anot,alerts_anot], api_name="uses") | |
| # api_contrib = gr.Button(visible=False) | |
| # api_contrib.click(api_extraction,inputs=[fileinput[0],apikey_elem,gr.State(value="contrib")],outputs=[result_anot,alerts_anot], api_name="contrib") | |
| #api_comp = gr.Button(visible=False) | |
| #api_comp.click(api_extraction,inputs=[fileinput[0],apikey_elem,gr.State(value="comp")],outputs=[result_anot,alerts_anot], api_name="composition") | |
| #api_social = gr.Button(visible=False) | |
| #api_social.click(api_extraction,inputs=[fileinput[0],apikey_elem,gr.State(value="social")],outputs=[result_anot,alerts_anot], api_name="social") | |
| #api_dist = gr.Button(visible=False) | |
| #api_dist.click(api_extraction,inputs=[fileinput[0],apikey_elem,gr.State(value="dist")],outputs=[result_anot,alerts_anot], api_name="dist") | |
| #button_complete.click(api_extraction,inputs=[fileinput[0],apikey_elem,"annotation"],outputs=allres, api_name="annotation") | |
| #button_complete.click(api_extraction,inputs=[fileinput[0],apikey_elem,"annotation"],outputs=allres, api_name="annotation") | |
| #button_complete.click(api_extraction,inputs=[fileinput[0],apikey_elem,"annotation"],outputs=allres, api_name="annotation") | |
| #button_complete.click(api_extraction,inputs=[fileinput[0],apikey_elem,"annotation"],outputs=allres, api_name="annotation") | |
| #button_complete.click(api_extraction,inputs=[fileinput[0],apikey_elem,"annotation"],outputs=allres, api_name="annotation") | |
| #button_complete.click(api_extraction,inputs=[fileinput[0],apikey_elem,"annotation"],outputs=allres, api_name="annotation") | |
| #button_complete.click(api_extraction,inputs=[fileinput[0],apikey_elem,"annotation"],outputs=allres, api_name="annotation") | |
| #button_complete.click(api_extraction,inputs=[fileinput[0],apikey_elem,"annotation"],outputs=allres, api_name="annotation") | |
| # Run the app | |
| #demo.queue(concurrency_count=5,max_size=20).launch() | |
| demo.launch(share=False,show_api=False) | |