Gunicorn support for prototype cluster browser with 2023 data

CHANGELOG.md

@@ -11,15 +11,19 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added support for python 3.10 in build and tests
 - Made dependency verions less restrictive, except when necessary to avoid deprecations (sklearn, numpy)
 - Unit tests updated to handle sklearn deprecations
+- Updated prototype cluster browser to display 2023 data
 
 ### Fixed
 - Upgraded DVC version from 2.10.0 to 3.33.1 to avoid https://github.com/iterative/dvc-objects/issues/241
 
 ### Added
 - Support for processing Reddit comments from manually downloaded archives
+- Data and models for Reddit comments in 2023 tracked in DVC
+- Instructions and support for running the prototype cluster browser with gunicorn
 
 ### Removed
 - Removed Unity documentation
+- Removed argparse from app.py so that it can be served with gunicorn
 
 ## [2.0.0]
 ### Changed

README.md

@@ -61,9 +61,9 @@ It expects a JSON configuration file with paths to trained community2vec models,
     }
 }
 ```
-Run `python app.py --config config.json` to start the application on port 8050, you will be able to navigate to http://localhost:8050/ to see the app running. You can also run using the `--debug` flag to have the application dynamically relaunch on code changes.
+Run `python app.py` to start the application on port 8050, you will be able to navigate to http://localhost:8050/ to see the app running. 
 
-The committed `config.json` is configured to load in the best models for each month over a year, from April 2021 through March 2022. To pull the models, run `dvc pull community2vec_models`, assuming you have access to the `s3://ihopmeag` bucket on AWS. See more details on DVC above.
+The committed `config.json` is configured to load in the best models for each month over a year, from December 2022 through December 2022. To pull the models and visualization files, run `dvc pull community2vec_models` and `dvc pull tsne_visualizations`, assuming you have access to the `s3://ihopmeag` bucket on AWS. See more details on DVC above.
 
 # Citation
 If you use this code, please cite [Here Be Livestreams: Trade-offs in Creating Temporal Maps of Reddit](https://arxiv.org/abs/2309.14259) as

app.py

@@ -1,16 +1,19 @@
 """Visualize subreddit clusters using a dash app.
-Run using `python app.py` and visit http://127.0.0.1:8050
+Run locally for development and debugging using `python app.py` and visit http://127.0.0.1:8050
 
-The app can be configured to accept a different model path by feeding a JSON format config file structured as:
+Can also be served with gunicorn: `gunicorn --bind 0.0.0.0:8050 app:server`
+
+The app can be configured to accept different model paths by changing config.json file structured as:
 {
     "logger": {<log config>},
-    "model_path": '<path to output of ihop.community2vec.py model training>'
+    "model_paths": {
+        "Identifier that will appear on the UI": "<path to output of ihop.community2vec.py model training>",
+        }
 }
 
 # TODO vector models should be configurable (one model for each available time range? )
 # TODO list of subreddits can be chosen from a collection with descriptions
 """
-import argparse
 import json
 import logging
 import pathlib
@@ -29,26 +32,9 @@
 
 logger = logging.getLogger(__name__)
 
-parser = argparse.ArgumentParser(
-    description="Runs a Dash application for browsing subreddit clusters"
-)
-# TODO Add application confiugration as needed
-parser.add_argument(
-    "--config",
-    default="config.json",
-    type=pathlib.Path,
-    help="JSON file used to override default logging and spark configurations",
-)
-parser.add_argument(
-    "-d",
-    "--debug",
-    action="store_true",
-    help="Use this flag to launch the application in 'hot-reload' mode",
-)
-
-args = parser.parse_args()
-spark_conf, logging_conf, conf = ihop.utils.parse_config_file(args.config)
-print("Configuration:", args.config)
+CONFIG = "config.json"
+spark_conf, logging_conf, conf = ihop.utils.parse_config_file(CONFIG)
+print("Configuration:", CONFIG)
 ihop.utils.configure_logging(logging_conf)
 logger.info("Logging configured")
 
@@ -58,7 +44,7 @@
 TSNE_CSV_NAME = "tsne.csv"
 
 app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
-
+server = app.server
 # APP DISPLAY CONSTANTS
 STARTING_NUM_CLUSTERS = 250
 STARTING_RANDOM_SEED = 100
@@ -99,7 +85,7 @@
         dash.html.H2("Select the time period"),
         dash.dcc.Markdown(MONTH_SELECTION_MD),
         dash.dcc.Dropdown(
-            list(MODEL_DIRS.keys()), id="month-dropdown", value="April 2021"
+            list(MODEL_DIRS.keys()), id="month-dropdown", value="December 2022"
         ),
         dash.html.Br(),
         dash.html.H2("Model Details"),
@@ -646,6 +632,6 @@ def get_display_table(
     try:
         # TODO Plotly handles logging strangely, so use logger.info or workaround to not silence logging,
         # see https://community.plotly.com/t/logging-debug-messages-suppressed-in-callbacks/17854
-        app.run_server(debug=args.debug)
+        app.run_server()
     except Exception as e:
         logger.error(e)

config.json

@@ -43,17 +43,18 @@
         }
     },
     "model_paths": {
-        "April 2021": "data/community2vec/RC_2021-04/best_model",
-        "May 2021": "data/community2vec/RC_2021-05/best_model",
-        "June 2021": "data/community2vec/RC_2021-06/best_model",
-        "July 2021": "data/community2vec/RC_2021-07/best_model",
-        "August 2021": "data/community2vec/RC_2021-08/best_model",
-        "September 2021": "data/community2vec/RC_2021-09/best_model",
-        "October 2021": "data/community2vec/RC_2021-10/best_model",
-        "November 2021": "data/community2vec/RC_2021-11/best_model",
-        "December 2021": "data/community2vec/RC_2021-12/best_model",
-        "January 2022": "data/community2vec/RC_2022-01/best_model",
-        "February 2022": "data/community2vec/RC_2022-02/best_model",
-        "March 2022": "data/community2vec/RC_2022-03/best_model"
+        "December 2022": "data/community2vec/RC_2022-12/best_model",
+        "January 2023": "data/community2vec/RC_2023-01/best_model",
+        "February 2023": "data/community2vec/RC_2023-02/best_model",
+        "March 2023": "data/community2vec/RC_2023-03/best_model",
+        "April 2023": "data/community2vec/RC_2023-04/best_model",
+        "May 2023": "data/community2vec/RC_2023-05/best_model",
+        "June 2023": "data/community2vec/RC_2023-06/best_model",
+        "July 2023": "data/community2vec/RC_2023-07/best_model",
+        "August 2023": "data/community2vec/RC_2023-08/best_model",
+        "September 2023": "data/community2vec/RC_2023-09/best_model",
+        "October 2023": "data/community2vec/RC_2023-10/best_model",
+        "November 2023": "data/community2vec/RC_2023-11/best_model",
+        "December 2023": "data/community2vec/RC_2023-12/best_model"
     }
 }

setup.cfg

@@ -25,15 +25,16 @@ install_requires =
 
 [options.extras_require]
 app =
-    plotly==5.6.0
     dash==2.3.1
     dash-bootstrap-components==1.1.0
     dash-core-components==2.0.0
     dash-daq==0.5.0
     dash-html-components==2.0.0
     dash-renderer==1.9.0
     dash-table==5.0.0
+    gunicorn
     matplotlib==3.5.0
+    plotly==5.6.0
 dev =
     black
     irrCAC