Update instructions

README.md

@@ -1,55 +1,57 @@
-# EEA Corpus (alpha)
+# EEA Corpus (alpha stage)
 
-This docker image is based on spaCy, Textacy and pyLDAvis to analyse the 
-EEA Corpus (the collection of all published EEA documents). 
+This docker image is based on spaCy, Textacy, pyLDAvis & others to analyse the
+EEA Corpus (the collection of all published EEA documents) or any other CSV
+file with a column of text.
 
-It provides a number of Machine Learning and Natural Language Processing algorthims
-that can be run on top of the EEA Corpus or a subset of it.
+It provides a number of Machine Learning and Natural Language Processing
+algorthims that can be run on top of the EEA Corpus or a subset of it.
 
-The idea is to provide these methods over a REST API when possible. 
+~The idea is to provide these methods over a REST API when possible.~
 
 ## Current features
 
-Create and visualise topic models via pyLDAvis. 
+### Compose a text transformation pipeline to prepare a corpus
 
-The topics are found via a text-mining technique called [Topic Modeling](https://en.wikipedia.org/wiki/Topic_model).
+First upload a CSV file, then use the "Create a corpus" button to enter
+the pipeline composition page.
 
-In machine learning and natural language processing, a topic model is a 
+### C*reate and visualise topic models via pyLDAvis.
+
+The topics are found via a text-mining technique called
+[Topic Modeling](https://en.wikipedia.org/wiki/Topic_model).
+
+In machine learning and natural language processing, a topic model is a
 type of statistical model for discovering the abstract "topics" that occur in a
 collection of documents.
 
 [Video demonstration](https://www.youtube.com/watch?v=IksL96ls4o0&t=255s)
 
 ![LDA visualisation example](ldavis.png?raw=true "LDA visualisation example")
 
-How to run:
+## How to run:
 
 ```
 docker-compose build
 docker-compose up -d
 ```
 
-Enter the shell of eeacorpus container:
-
-```
-docker exec -it eeacorpus_shell_1 bash
-```
-
-Inside the shell you run: 
-
-```
-python load_eea_corpus.py --normalize --data data-small.csv
-```
-
-This will (after some time) start the visualisation browser on 0.0.0.0:8888
+This will (after some time) start the EEA Corpus application server on
+[localhost:8181]*(http://0.0.0.0:8181)
 
 ## EEA Corpus Data
 
-The latest EEA Corpus dataset can be produced by visiting 
-[global catalogue](http://search.apps.eea.europa.eu/)  > See all results > download csv.
+The latest EEA Corpus dataset can be produced by visiting [global
+catalogue](http://search.apps.eea.europa.eu/)  > See all results > download
+csv.
 
-Once the csv file is downloaded, you can pass it to this application to be analysed. Make sure your
-first column is the "document text" to be analysed. The other columns are considered metadata.
+Once the csv file is downloaded, you can pass it to this application to be
+analysed. Make sure your first column is the "document text" to be analysed.
+The other columns are considered metadata.
 
-You may download an already generated large EEA corpus data for testing like this:
+You may download an already generated large EEA corpus data for testing like
+this:
+
+```
 curl -L -o data.csv https://www.dropbox.com/s/sihmoc4wwpl0kr2/data_all.csv?dl=1
+```

docker-compose.yml

@@ -12,16 +12,16 @@ services:
       - corpus-data:/corpus
     command: sh -c "pserve production.ini"
 
-  redis:
-    image: redis
-
   worker:
-      image: eeacms/corpus:pyramid_service
+      image: eeacms/corpus:latest
       command: sh -c "worker production.ini"
       environment:
         - REDIS_URL=redis://redis:6379/0
       volumes:
         - corpus-data:/corpus
 
+  redis:
+    image: redis
+
 volumes:
   corpus-data: