Notebook file is prepared to work with the parameter.

Pipfile

@@ -9,6 +9,7 @@ verify_ssl = true
 jupyter = "*"
 fastapi = "*"
 nbconvert = "*"
+nbparameterise = "*"
 uvicorn = "*"
 pandas = "*"
 requests = "*"

README.md

@@ -20,7 +20,7 @@ pipenv shell
 
 
 ### Add your notebook(s) to "src/notebooks"
-There's an example notebook already in there called `scrape.ipynb`. 
+There's an example notebook already in there called `hello.ipynb` and `scrape.ipynb`. 
 
 
 ### Run locally
@@ -35,12 +35,20 @@ chmod +x run.sh
 ./run.sh
 ```
 
-## Trigger a notebook.
-With the server running, trigger notebook (relative path) like `notebooks/scrape.ipynb`
+## Run a notebook.
+With the server running, run notebook file like `scrape.ipynb`
 
 ```python
 import requests
-r = requests.post("http://localhost:8000/trigger/notebooks/scrape.ipynb")
+r = requests.post("http://localhost:8000/notebook/scrape.ipynb")
+print(r.json())
+```
+
+You can also change the variables in the notebook from the parameter. For example;
+
+```python
+import requests
+r = requests.post("http://localhost:8000/notebook/scrape.ipynb?start_year=2021&years_ago=7")
 print(r.json())
 ```
 
@@ -74,4 +82,4 @@ heroku container:login
 docker build -t -f Dockerfile.web .
 heroku container:push web --recursive
 heroku container:release web 
-```
+```

requirements.txt

@@ -35,6 +35,7 @@ MarkupSafe==1.1.1
 mistune==0.8.4
 nbconvert==5.6.1
 nbformat==5.0.4
+nbparameterise==0.5
 notebook==6.0.3
 numpy==1.18.2
 pandas==1.0.3

src/notebooks/hello.ipynb

@@ -0,0 +1,38 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "to = \"World\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"Hello, {to}!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "36cf16204b8548560b1c020c4e8fb5b57f0e4c58016f52f2d4be01e192833930"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.4 64-bit",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": ""
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

src/notebooks/scrape.ipynb

@@ -1,28 +1,15 @@
 {
- "nbformat": 4,
- "nbformat_minor": 2,
- "metadata": {
-  "language_info": {
-   "name": "python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "version": "3.6.8-final"
-  },
-  "orig_nbformat": 2,
-  "file_extension": ".py",
-  "mimetype": "text/x-python",
-  "name": "python",
-  "npconvert_exporter": "python",
-  "pygments_lexer": "ipython3",
-  "version": 3,
-  "kernelspec": {
-   "name": "python36864bitjupyterapipipenv100023b4210342f0b1028e92ab62ef71",
-   "display_name": "Python 3.6.8 64-bit ('jupyter-api': pipenv)"
-  }
- },
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start_year = 2014\n",
+    "years_ago = 0"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -131,13 +118,15 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
-     "text": "Finished 2020\n"
+     "output_type": "stream",
+     "text": [
+      "Finished 2020\n"
+     ]
     }
    ],
    "source": [
-    "run()"
+    "run(int(start_year), int(years_ago))"
    ]
   },
   {
@@ -147,5 +136,27 @@
    "outputs": [],
    "source": []
   }
- ]
+ ],
+ "metadata": {
+  "file_extension": ".py",
+  "interpreter": {
+   "hash": "36cf16204b8548560b1c020c4e8fb5b57f0e4c58016f52f2d4be01e192833930"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.4 64-bit",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": ""
+  },
+  "mimetype": "text/x-python",
+  "name": "python",
+  "npconvert_exporter": "python",
+  "orig_nbformat": 2,
+  "pygments_lexer": "ipython3",
+  "version": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }

src/server.py

@@ -1,24 +1,26 @@
-from fastapi import FastAPI
+from fastapi import FastAPI, Request
 from pprint import pprint
+
 app = FastAPI()
+
 import inspect
 import os
 
 from .trigger import trigger
 
 filename     = inspect.getframeinfo(inspect.currentframe()).filename
 BASE_DIR     = os.path.dirname(os.path.abspath(filename))
+NOTEBOOKS_DIR = BASE_DIR + '/notebooks'
 
 @app.get("/")
 async def read_root():
     return {"Hello": "World"}
 
-# @app.get("/trigger/{filepath:path}")
-@app.post("/trigger/{filepath:path}")
-async def read_item(filepath):
-    filepath = os.path.join(BASE_DIR, filepath)
-    ep = trigger(notebook_filename=filepath)
-    # data = vars(ep)
+@app.get("/notebook/{filepath:path}")
+async def read_item(filepath, request: Request):
+    filepath = os.path.join(NOTEBOOKS_DIR, filepath)
+    ep = trigger(notebook_filename=filepath, params=request.query_params)
+
     cells = ep[0]['cells']
     all_outputs = []
     for i, cell in enumerate(cells):

src/trigger.py

@@ -1,9 +1,16 @@
 import nbformat
 from nbconvert.preprocessors import ExecutePreprocessor
+from nbparameterise import (
+    extract_parameters, replace_definitions, parameter_values
+)
 
-def trigger(notebook_filename='chp-traffic.ipynb'):
+def trigger(notebook_filename='hello.ipynb', params={}):
     with open(notebook_filename) as f:
         nb = nbformat.read(f, as_version=4)
+
+    orig_parameters = extract_parameters(nb)
+    new_nb = replace_definitions(nb, parameter_values(orig_parameters, **params))
+
     ep = ExecutePreprocessor(timeout=600, kernel_name='python3')
-    r = ep.preprocess(nb)
+    r = ep.preprocess(new_nb)
     return r