Download/clone all files in gist locally.
Install all requirements:
pip install -r requirements.txtStart Sanic server:
sanic main.app --reloadOpen the browser and check the url: http:\\127.0.0.1:8000
URL paths to test:
http://127.0.0.1:8000/
http://127.0.0.1:8000/population
http://127.0.0.1:8000/survival_rate
http://127.0.0.1:8000/survival_rate_plotly
| # YT link : https://www.youtube.com/watch?v=0qUWOzc0Mjo | |
| # Github link: https://github.com/AammarTufail/fast_api_tutorial_codanics/blob/main/09_titanic_app_plotly/main.py | |
| from datetime import datetime, timezone | |
| import logging | |
| # from fastapi import FastAPI | |
| # from fastapi.responses import HTMLResponse | |
| from sanic import Sanic, html, json as sanic_json | |
| # import seaborn as sns | |
| import pandas as pd | |
| import plotly.express as px | |
| from plotly.io import to_html | |
| # app = FastAPI() # Create a FastAPI instance | |
| app = Sanic("PlotlyTestAPP") # Create a Sanic instance | |
| @app.middleware("request") | |
| async def add_start_time(request): | |
| # adding start_time for benchmarking | |
| request.ctx.start_time = datetime.now(timezone.utc) | |
| @app.middleware("response") | |
| async def add_spent_time(request, response): | |
| request.ctx.spent_time = datetime.now(timezone.utc) - request.ctx.start_time | |
| # benchmarking info at the end of the response. | |
| # logging.info is not displayed by sanic server and so logging.warning is used | |
| logging.warning(f"Request to {request.url} took {request.ctx.spent_time}") | |
| # Load the Titanic dataset | |
| # df = sns.load_dataset("titanic") | |
| df = pd.read_csv( | |
| "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv" | |
| ) | |
| # Perform a simple data transformation | |
| survival_rate = df.groupby("class")["survived"].mean().reset_index() | |
| POLTLY_JS_CDN = "https://cdn.plot.ly/plotly-3.0.0.min.js" | |
| @app.get("/") | |
| async def root(request): | |
| return sanic_json( | |
| body={"message": "Welcome to the Titanic API, we are learning on codanics.com"} | |
| ) | |
| @app.get("/survival_rate") | |
| async def get_survival_rate(request): | |
| return sanic_json(body=survival_rate.to_dict(orient="records")) | |
| @app.get("/survival_rate_plotly") # , response_class=HTMLResponse) | |
| async def survival_rate_plotly(request): | |
| df["class"] = pd.Categorical(df["class"], ["First", "Second", "Third"]) | |
| df["deck"] = pd.Categorical(df["deck"], list("ABCDEFG")) | |
| # Generate the plot with Plotly | |
| fig = px.bar( | |
| df, | |
| x="class", | |
| y="survived", | |
| color="sex", | |
| title="Survival Rate by Class with Plotly", | |
| ) | |
| # Convert the plot to HTML | |
| plot_div = to_html( | |
| fig, full_html=False, include_plotlyjs=False, config={"displayModeBar": False} | |
| ) | |
| # Create the full HTML document | |
| html_content = f""" | |
| <html> | |
| <head> | |
| <title>Survival Rate by Class (Plotly)</title> | |
| <script src="{POLTLY_JS_CDN}"></script> | |
| </head> | |
| <body> | |
| {plot_div} | |
| </body> | |
| </html> | |
| """ | |
| # return HTMLResponse(content=html_content) | |
| return html(body=html_content) | |
| data = { | |
| "country": ["China", "India", "United States", "Indonesia", "Pakistan"], | |
| "population": [1444216107, 1393409038, 332915073, 276361783, 225199937], | |
| } | |
| @app.get("/population") # , response_class=HTMLResponse) | |
| async def get_world_population(request): | |
| # Generate a Plotly figure | |
| fig = px.bar(data, x="country", y="population", title="World Population by Country") | |
| # Convert the figure to HTML | |
| plot_html = to_html( | |
| fig, full_html=False, include_plotlyjs=False, config={"displayModeBar": False} | |
| ) | |
| # Embed the plot in a simple HTML page | |
| html_content = f""" | |
| <html> | |
| <head> | |
| <title>World Population Plot</title> | |
| <script src="{POLTLY_JS_CDN}"></script> | |
| </head> | |
| <body> | |
| {plot_html} | |
| </body> | |
| </html> | |
| """ | |
| # return HTMLResponse(content=html_content) | |
| return html(body=html_content) |
| # fastapi # can be tested in fastapi as in orginial youtube code. Just comment/uncomment appropriately | |
| sanic | |
| # seaborn # can bypass this requirement by using Pandas library | |
| pandas | |
| plotly | |
| # uvicorn | |
| # ipython |