import spaces # necessary to run on Zero. from spaces.zero.client import _get_token import time print(f"Starting up: {time.strftime('%Y-%m-%d %H:%M:%S')}") # Standard library imports import os from pathlib import Path from datetime import datetime from itertools import chain # Third-party imports import numpy as np import pandas as pd import torch import gradio as gr print(f"Gradio version: {gr.__version__}") from fastapi import FastAPI from fastapi.staticfiles import StaticFiles import uvicorn import matplotlib.pyplot as plt import tqdm import colormaps import matplotlib.colors as mcolors from matplotlib.colors import Normalize import opinionated # for fonts plt.style.use("opinionated_rc") from sklearn.neighbors import NearestNeighbors def is_running_in_hf_space(): return "SPACE_ID" in os.environ #if is_running_in_hf_space(): import spaces # necessary to run on Zero. #print(f"Spaces version: {spaces.__version__}") import datamapplot import pyalex # Local imports from openalex_utils import ( openalex_url_to_pyalex_query, get_field, process_records_to_df, openalex_url_to_filename ) from styles import DATAMAP_CUSTOM_CSS from data_setup import ( download_required_files, setup_basemap_data, setup_mapper, setup_embedding_model, ) from network_utils import create_citation_graph, draw_citation_graph # Configure OpenAlex pyalex.config.email = "maximilian.noichl@uni-bamberg.de" print(f"Imports completed: {time.strftime('%Y-%m-%d %H:%M:%S')}") # Instead of FastAPI setup, just use Gradio's file serving static_dir = Path("static") static_dir.mkdir(exist_ok=True) # Create the static directory if it doesn't exist gr.set_static_paths(paths=["static/"]) # Resource configuration REQUIRED_FILES = { "100k_filtered_OA_sample_cluster_and_positions_supervised.pkl": "https://huggingface.co/datasets/m7n/intermediate_sci_pickle/resolve/main/100k_filtered_OA_sample_cluster_and_positions_supervised.pkl", "umap_mapper_250k_random_OA_discipline_tuned_specter_2_params.pkl": "https://huggingface.co/datasets/m7n/intermediate_sci_pickle/resolve/main/umap_mapper_250k_random_OA_discipline_tuned_specter_2_params.pkl" } BASEMAP_PATH = "100k_filtered_OA_sample_cluster_and_positions_supervised.pkl" MAPPER_PARAMS_PATH = "umap_mapper_250k_random_OA_discipline_tuned_specter_2_params.pkl" MODEL_NAME = "m7n/discipline-tuned_specter_2_024" # Initialize models and data start_time = time.time() print("Initializing resources...") download_required_files(REQUIRED_FILES) basedata_df = setup_basemap_data(BASEMAP_PATH) mapper = setup_mapper(MAPPER_PARAMS_PATH) model = setup_embedding_model(MODEL_NAME) print(f"Resources initialized in {time.time() - start_time:.2f} seconds") # Setting up decorators for embedding on HF-Zero: def no_op_decorator(func): """A no-op (no operation) decorator that simply returns the function.""" def wrapper(*args, **kwargs): # Do nothing special return func(*args, **kwargs) return wrapper # # Decide which decorator to use based on environment # decorator_to_use = spaces.GPU() if is_running_in_hf_space() else no_op_decorator # #duration=120 # @decorator_to_use @spaces.GPU(duration=4*60) def create_embeddings(texts_to_embedd): """Create embeddings for the input texts using the loaded model.""" return model.encode(texts_to_embedd, show_progress_bar=True, batch_size=192) def predict(request: gr.Request, text_input, sample_size_slider, reduce_sample_checkbox, sample_reduction_method, plot_time_checkbox, locally_approximate_publication_date_checkbox, download_csv_checkbox, download_png_checkbox, citation_graph_checkbox, progress=gr.Progress()): """ Main prediction pipeline that processes OpenAlex queries and creates visualizations. Args: request (gr.Request): Gradio request object text_input (str): OpenAlex query URL sample_size_slider (int): Maximum number of samples to process reduce_sample_checkbox (bool): Whether to reduce sample size sample_reduction_method (str): Method for sample reduction ("Random" or "Order of Results") plot_time_checkbox (bool): Whether to color points by publication date locally_approximate_publication_date_checkbox (bool): Whether to approximate publication date locally before plotting. progress (gr.Progress): Gradio progress tracker Returns: tuple: (link to visualization, iframe HTML) """ # Get the authentication token token = _get_token(request) print(f"Token: {token}") print(f"Request: {request}") # Check if input is empty or whitespace print(f"Input: {text_input}") if not text_input or text_input.isspace(): error_message = "Error: Please enter a valid OpenAlex URL in the 'OpenAlex-search URL'-field" return [ error_message, # iframe HTML gr.DownloadButton(label="Download Interactive Visualization", value='html_file_path', visible=False), # html download gr.DownloadButton(label="Download CSV Data", value='csv_file_path', visible=False), # csv download gr.DownloadButton(label="Download Static Plot", value='png_file_path', visible=False), # png download gr.Button(visible=False) # cancel button state ] # Check if the input is a valid OpenAlex URL start_time = time.time() print('Starting data projection pipeline') progress(0.1, desc="Starting...") # Split input into multiple URLs if present urls = [url.strip() for url in text_input.split(';')] records = [] total_query_length = 0 # Use first URL for filename first_query, first_params = openalex_url_to_pyalex_query(urls[0]) filename = openalex_url_to_filename(urls[0]) print(f"Filename: {filename}") # Process each URL for i, url in enumerate(urls): query, params = openalex_url_to_pyalex_query(url) query_length = query.count() total_query_length += query_length print(f'Requesting {query_length} entries from query {i+1}/{len(urls)}...') target_size = sample_size_slider if reduce_sample_checkbox and sample_reduction_method == "First n samples" else query_length records_per_query = 0 should_break = False for page in query.paginate(per_page=200, n_max=None): for record in page: records.append(record) records_per_query += 1 progress(0.1 + (0.2 * len(records) / (total_query_length)), desc=f"Getting data from query {i+1}/{len(urls)}...") if reduce_sample_checkbox and sample_reduction_method == "First n samples" and records_per_query >= target_size: should_break = True break if should_break: break if should_break: break print(f"Query completed in {time.time() - start_time:.2f} seconds") # Process records processing_start = time.time() records_df = process_records_to_df(records) if reduce_sample_checkbox and sample_reduction_method != "All": sample_size = min(sample_size_slider, len(records_df)) if sample_reduction_method == "n random samples": records_df = records_df.sample(sample_size) elif sample_reduction_method == "First n samples": records_df = records_df.iloc[:sample_size] print(f"Records processed in {time.time() - processing_start:.2f} seconds") # Create embeddings embedding_start = time.time() progress(0.3, desc="Embedding Data...") texts_to_embedd = [f"{title} {abstract}" for title, abstract in zip(records_df['title'], records_df['abstract'])] embeddings = create_embeddings(texts_to_embedd) print(f"Embeddings created in {time.time() - embedding_start:.2f} seconds") # Project embeddings projection_start = time.time() progress(0.5, desc="Project into UMAP-embedding...") umap_embeddings = mapper.transform(embeddings) records_df[['x','y']] = umap_embeddings print(f"Projection completed in {time.time() - projection_start:.2f} seconds") # Prepare visualization data viz_prep_start = time.time() progress(0.6, desc="Preparing visualization data...") basedata_df['color'] = '#ced4d211' if not plot_time_checkbox: records_df['color'] = '#5e2784' else: cmap = colormaps.haline if not locally_approximate_publication_date_checkbox: # Create color mapping based on publication years years = pd.to_numeric(records_df['publication_year']) norm = mcolors.Normalize(vmin=years.min(), vmax=years.max()) records_df['color'] = [mcolors.to_hex(cmap(norm(year))) for year in years] else: n_neighbors = 10 # Adjust this value to control smoothing nn = NearestNeighbors(n_neighbors=n_neighbors) nn.fit(umap_embeddings) distances, indices = nn.kneighbors(umap_embeddings) # Calculate local average publication year for each point local_years = np.array([ np.mean(records_df['publication_year'].iloc[idx]) for idx in indices ]) norm = mcolors.Normalize(vmin=local_years.min(), vmax=local_years.max()) records_df['color'] = [mcolors.to_hex(cmap(norm(year))) for year in local_years] stacked_df = pd.concat([basedata_df, records_df], axis=0, ignore_index=True) stacked_df = stacked_df.fillna("Unlabelled") stacked_df['parsed_field'] = [get_field(row) for ix, row in stacked_df.iterrows()] extra_data = pd.DataFrame(stacked_df['doi']) print(f"Visualization data prepared in {time.time() - viz_prep_start:.2f} seconds") if citation_graph_checkbox: citation_graph_start = time.time() citation_graph = create_citation_graph(records_df) graph_file_name = f"{filename}_citation_graph.jpg" graph_file_path = static_dir / graph_file_name draw_citation_graph(citation_graph,path=graph_file_path,bundle_edges=True, min_max_coordinates=[np.min(stacked_df['x']),np.max(stacked_df['x']),np.min(stacked_df['y']),np.max(stacked_df['y'])]) print(f"Citation graph created and saved in {time.time() - citation_graph_start:.2f} seconds") # Create and save plot plot_start = time.time() progress(0.7, desc="Creating interactive plot...") # Create a solid black colormap black_cmap = mcolors.LinearSegmentedColormap.from_list('black', ['#000000', '#000000']) plot = datamapplot.create_interactive_plot( stacked_df[['x','y']].values, np.array(stacked_df['cluster_2_labels']), np.array(['Unlabelled' if pd.isna(x) else x for x in stacked_df['parsed_field']]), hover_text=[str(row['title']) for ix, row in stacked_df.iterrows()], marker_color_array=stacked_df['color'], use_medoids=False, # Switch back once efficient mediod caclulation comes out! width=1000, height=1000, point_radius_min_pixels=1, text_outline_width=5, point_hover_color='#5e2784', point_radius_max_pixels=7, cmap=black_cmap, background_image=graph_file_name if citation_graph_checkbox else None, #color_label_text=False, font_family="Roboto Condensed", font_weight=600, tooltip_font_weight=600, tooltip_font_family="Roboto Condensed", extra_point_data=extra_data, on_click="window.open(`{doi}`)", custom_css=DATAMAP_CUSTOM_CSS, initial_zoom_fraction=.8, enable_search=False, offline_mode=False ) # Save plot html_file_name = f"{filename}.html" html_file_path = static_dir / html_file_name plot.save(html_file_path) print(f"Plot created and saved in {time.time() - plot_start:.2f} seconds") # Save additional files if requested csv_file_path = static_dir / f"{filename}.csv" png_file_path = static_dir / f"{filename}.png" if download_csv_checkbox: # Export relevant column export_df = records_df[['title', 'abstract', 'doi', 'publication_year', 'x', 'y','id','primary_topic']] export_df['parsed_field'] = [get_field(row) for ix, row in export_df.iterrows()] export_df['referenced_works'] = [', '.join(x) for x in records_df['referenced_works']] export_df.to_csv(csv_file_path, index=False) if download_png_checkbox: png_start_time = time.time() print("Starting PNG generation...") # Sample and prepare data sample_prep_start = time.time() sample_to_plot = basedata_df#.sample(20000) labels1 = np.array(sample_to_plot['cluster_2_labels']) labels2 = np.array(['Unlabelled' if pd.isna(x) else x for x in sample_to_plot['parsed_field']]) ratio = 0.6 mask = np.random.random(size=len(labels1)) < ratio combined_labels = np.where(mask, labels1, labels2) # Get the 30 most common labels unique_labels, counts = np.unique(combined_labels, return_counts=True) top_30_labels = set(unique_labels[np.argsort(counts)[-50:]]) # Replace less common labels with 'Unlabelled' combined_labels = np.array(['Unlabelled' if label not in top_30_labels else label for label in combined_labels]) #combined_labels = np.array(['Unlabelled' for label in combined_labels]) #if label not in top_30_labels else label colors_base = ['#536878' for _ in range(len(labels1))] print(f"Sample preparation completed in {time.time() - sample_prep_start:.2f} seconds") # Create main plot print(labels1) print(labels2) print(sample_to_plot[['x','y']].values) print(combined_labels) main_plot_start = time.time() fig, ax = datamapplot.create_plot( sample_to_plot[['x','y']].values, combined_labels, label_wrap_width=12, label_over_points=True, dynamic_label_size=True, use_medoids=False, # Switch back once efficient mediod caclulation comes out! point_size=2, marker_color_array=colors_base, force_matplotlib=True, max_font_size=12, min_font_size=4, min_font_weight=100, max_font_weight=300, font_family="Roboto Condensed", color_label_text=False, add_glow=False, highlight_labels=list(np.unique(labels1)), label_font_size=8, highlight_label_keywords={"fontsize": 12, "fontweight": "bold", "bbox":{"boxstyle":"circle", "pad":0.75,'alpha':0.}}, ) print(f"Main plot creation completed in {time.time() - main_plot_start:.2f} seconds") if citation_graph_checkbox: # Read and add the graph image graph_img = plt.imread(graph_file_path) ax.imshow(graph_img, extent=[np.min(stacked_df['x']),np.max(stacked_df['x']),np.min(stacked_df['y']),np.max(stacked_df['y'])], alpha=0.9, aspect='auto') # Time-based visualization scatter_start = time.time() if plot_time_checkbox: if locally_approximate_publication_date_checkbox: scatter = plt.scatter( umap_embeddings[:,0], umap_embeddings[:,1], c=local_years, cmap=colormaps.haline, alpha=0.8, s=5 ) else: years = pd.to_numeric(records_df['publication_year']) scatter = plt.scatter( umap_embeddings[:,0], umap_embeddings[:,1], c=years, cmap=colormaps.haline, alpha=0.8, s=5 ) plt.colorbar(scatter, shrink=0.5, format='%d') else: scatter = plt.scatter( umap_embeddings[:,0], umap_embeddings[:,1], c=records_df['color'], alpha=0.8, s=5 ) print(f"Scatter plot creation completed in {time.time() - scatter_start:.2f} seconds") # Save plot save_start = time.time() plt.axis('off') png_file_path = static_dir / f"{filename}.png" plt.savefig(png_file_path, dpi=300, bbox_inches='tight') plt.close() print(f"Plot saving completed in {time.time() - save_start:.2f} seconds") print(f"Total PNG generation completed in {time.time() - png_start_time:.2f} seconds") progress(1.0, desc="Done!") print(f"Total pipeline completed in {time.time() - start_time:.2f} seconds") iframe = f"""""" # Return iframe and download buttons with appropriate visibility return [ iframe, gr.DownloadButton(label="Download Interactive Visualization", value=html_file_path, visible=True, variant='secondary'), gr.DownloadButton(label="Download CSV Data", value=csv_file_path, visible=download_csv_checkbox, variant='secondary'), gr.DownloadButton(label="Download Static Plot", value=png_file_path, visible=download_png_checkbox, variant='secondary'), gr.Button(visible=False) # Return hidden state for cancel button ] predict.zerogpu = True theme = gr.themes.Monochrome( font=[gr.themes.GoogleFont("Roboto Condensed"), "ui-sans-serif", "system-ui", "sans-serif"], text_size="lg", ).set( button_secondary_background_fill="white", button_secondary_background_fill_hover="#f3f4f6", button_secondary_border_color="black", button_secondary_text_color="black", button_border_width="2px", ) # Gradio interface setup with gr.Blocks(theme=theme, css=""" .gradio-container a { color: black !important; text-decoration: none !important; /* Force remove default underline */ font-weight: bold; transition: color 0.2s ease-in-out, border-bottom-color 0.2s ease-in-out; display: inline-block; /* Enable proper spacing for descenders */ line-height: 1.1; /* Adjust line height */ padding-bottom: 2px; /* Add space for descenders */ } .gradio-container a:hover { color: #b23310 !important; border-bottom: 3px solid #b23310; /* Wider underline, only on hover */ } """) as demo: gr.Markdown("""

# OpenAlex Mapper OpenAlex Mapper is a way of projecting search queries from the amazing OpenAlex database on a background map of randomly sampled papers from OpenAlex, which allows you to easily investigate interdisciplinary connections. OpenAlex Mapper was developed by [Maximilian Noichl](https://maxnoichl.eu) and [Andrea Loettgers](https://unige.academia.edu/AndreaLoettgers) at the [Possible Life project](http://www.possiblelife.eu/). To use OpenAlex Mapper, first head over to [OpenAlex](https://openalex.org/) and search for something that interests you. For example, you could search for all the papers that make use of the [Kuramoto model](https://openalex.org/works?page=1&filter=default.search%3A%22Kuramoto%20Model%22), for all the papers that were published by researchers at [Utrecht University in 2019](https://openalex.org/works?page=1&filter=authorships.institutions.lineage%3Ai193662353,publication_year%3A2019), or for all the papers that cite Wittgenstein's [Philosophical Investigations](https://openalex.org/works?page=1&filter=cites%3Aw4251395411). Then you copy the URL to that search query into the OpenAlex search URL box below and click "Run Query." It will download all of these records from OpenAlex and embed them on our interactive map. As the embedding step is a little expensive, computationally, it's often a good idea to play around with smaller samples, before running a larger analysis. After a little time, that map will appear and be available for you to interact with and download. You can find more explanations in the FAQs below.
""") with gr.Row(): with gr.Column(scale=1): with gr.Row(): run_btn = gr.Button("Run Query", variant='primary') cancel_btn = gr.Button("Cancel", visible=False, variant='secondary') # Create separate download buttons html_download = gr.DownloadButton("Download Interactive Visualization", visible=False, variant='secondary') csv_download = gr.DownloadButton("Download CSV Data", visible=False, variant='secondary') png_download = gr.DownloadButton("Download Static Plot", visible=False, variant='secondary') text_input = gr.Textbox(label="OpenAlex-search URL", info="Enter the URL to an OpenAlex-search.") gr.Markdown("### Sample Settings") reduce_sample_checkbox = gr.Checkbox( label="Reduce Sample Size", value=True, info="Reduce sample size." ) sample_reduction_method = gr.Dropdown( ["All", "First n samples", "n random samples"], label="Sample Selection Method", value="First n samples", info="How to choose the samples to keep." ) sample_size_slider = gr.Slider( label="Sample Size", minimum=500, maximum=20000, step=10, value=1000, info="How many samples to keep.", visible=True ) gr.Markdown("### Plot Settings") plot_time_checkbox = gr.Checkbox( label="Plot Time", value=True, info="Colour points by their publication date." ) locally_approximate_publication_date_checkbox = gr.Checkbox( label="Locally Approximate Publication Date", value=True, info="Colour points by the average publication date in their area." ) gr.Markdown("### Download Options") download_csv_checkbox = gr.Checkbox( label="Generate CSV Export", value=False, info="Export the data as CSV file" ) download_png_checkbox = gr.Checkbox( label="Generate Static PNG Plot", value=False, info="Export a static PNG visualization. This will make things slower!" ) gr.Markdown("### Citation graph") citation_graph_checkbox = gr.Checkbox( label="Add Citation Graph", value=False, info="Adds a citation graph of the sample to the plot." ) with gr.Column(scale=2): html = gr.HTML( value='

The visualization map will appear here after running a query

', label="", show_label=False ) gr.Markdown("""
# FAQs ## Who made this? This project was developed by [Maximilian Noichl](https://maxnoichl.eu) (Utrecht University), in cooperation with Andrea Loettger and Tarja Knuuttila at the [Possible Life project](http://www.possiblelife.eu/), at the University of Vienna. If this project is useful in any way for your research, we would appreciate citation of **...** This project received funding from the European Research Council under the European Union's Horizon 2020 research and innovation programme (LIFEMODE project, grant agreement No. 818772). ## How does it work? The base map for this project is developed by randomly downloading 250,000 articles from OpenAlex, then embedding their abstracts using our [fine-tuned](https://huggingface.co/m7n/discipline-tuned_specter_2_024) version of the [specter-2](https://huggingface.co/allenai/specter2_aug2023refresh_base) language model, running these embeddings through [UMAP](https://umap-learn.readthedocs.io/en/latest/) to give us a two-dimensional representation, and displaying that in an interactive window using [datamapplot](https://datamapplot.readthedocs.io/en/latest/index.html). After the data for your query is downloaded from OpenAlex, it then undergoes the exact same process, but the pre-trained UMAP model from earlier is used to project your new data points onto this original map, showing where they would show up if they were included in the original sample. For more details, you can take a look at the method section of this paper: **...** ## I want to add multiple queries at once! That can be a good idea, e. g. if your interested in a specific paper, as well as all the papers that cite it. Just add the queries to the query box and separate them with a ";" without any spaces in between! ## I think I found a mistake in the map. There are various considerations to take into account when working with this map: 1. The language model we use is fine-tuned to separate disciplines from each other, but of course, disciplines are weird, partially subjective social categories, so what the model has learned might not always correspond perfectly to what you would expect to see. 2. When pressing down a really high-dimensional space into a low-dimensional one, there will be trade-offs. For example, we see this big ring structure of the sciences on the map, but in the middle of the map there is a overly stretchedstring of bioinformaticsthat stretches from computer science at the bottom up to the life sciences clusters at the top. This is one of the areas where the UMAP algorithm had trouble pressing our high-dimensional dataset into a low-dimensional space. For more information on how to read a UMAP plot, I recommend looking into ["Understanding UMAP"](https://pair-code.github.io/understanding-umap/) by Andy Coenen & Adam Pearce. 3. Finally, the labels we're using for the regions of this plot are created from OpenAlex's own labels of sub-disciplines. They give a rough indication of the papers that could be expected in this broad area of the map, but they are not necessarily the perfect label for the articles that are precisely below them. They are just located at the median point of a usually much larger, much broader, and fuzzier category, so they should always be taken with quite a big grain of salt.
""") def update_slider_visibility(method): return gr.Slider(visible=(method != "All")) sample_reduction_method.change( fn=update_slider_visibility, inputs=[sample_reduction_method], outputs=[sample_size_slider] ) def show_cancel_button(): return gr.Button(visible=True) def hide_cancel_button(): return gr.Button(visible=False) show_cancel_button.zerogpu = True hide_cancel_button.zerogpu = True predict.zerogpu = True # Update the run button click event run_event = run_btn.click( fn=show_cancel_button, outputs=cancel_btn, queue=False ).then( fn=predict, inputs=[ text_input, sample_size_slider, reduce_sample_checkbox, sample_reduction_method, plot_time_checkbox, locally_approximate_publication_date_checkbox, download_csv_checkbox, download_png_checkbox, citation_graph_checkbox ], outputs=[html, html_download, csv_download, png_download, cancel_btn] ) # Add cancel button click event cancel_btn.click( fn=hide_cancel_button, outputs=cancel_btn, cancels=[run_event], queue=False # Important to make the button hide immediately ) # Mount and run app # app = gr.mount_gradio_app(app, demo, path="/",ssr_mode=False) # app.zerogpu = True # Add this line if __name__ == "__main__": demo.launch(server_name="0.0.0.0", server_port=7860)