<jupyter_start><jupyter_text>{{cookiecutter.project_name}} Version 0.1  Author: {{cookiecutter.author_name}} email: {{cookiecutter.author_email}} Date: {% now 'local', '%d-%b-%Y' %} Contributors:   Project ReviewAssess what when right and what went wrong, what was done well and what needs to be improved. Experience DocumentationTODO: Summarize important experience gained during the project. For example, pitfalls, misleading approaches,or hints for selecting the best suited data mining techniques in similar situations could be part of thisdocumentation. In ideal projects, experience documentation also covers any reports that have beenwritten by individual project members during previous phases of the project.TODO: Summarize important experience gained during the project. For example, pitfalls, misleading approaches,or hints for selecting the best suited data mining techniques in similar situations could be part of thisdocumentation. In ideal projects, experience documentation also covers any reports that have beenwritten by individual project members during previous phases of the project.  Final Report Deployment PlanThis task takes the evaluation results and determines a strategy for deployment. If a general procedure hasbeen identified to create the relevant model(s), this procedure is documented here for later deployment. Deployment steps1. Create a pull request to have your project development history merged into the organization Github repository.2. From your top level project directory, at a command line run the command:     `docker build -t /{{ cookiecutter.project_name.lower().replace(' ', '_') }}`.    3. If the container builds sucessfully, run an instance of the container locally.3. Test that you can run the model locally.4. If you are satisfied that the model is giving as expected results, push the containerized image to your configured docker repository.<jupyter_code># Example code to test the rest api of the model

import requests
import pandas as pd


# Example of 
new_dict = {'sepal_length': 5.0, 'sepal_width': 3.4, 'petal_length': 1.5, 'petal_width': 0.2}

payload = pd.DataFrame([new_dict]).to_json()

r = requests.post("http://localhost:5000", data=payload)
r.status_code

r.json()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.06625441696113074, 'fraction_numerical': 0.008392226148409895, 'mean_word_length': 5.489971346704871, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '265623', 'license': 'MIT', 'n_tokens_mistral': 553, 'n_tokens_neox': 530, 'n_words': 280, 'path': '{{cookiecutter.repo_name}}/deliver/4_{{cookiecutter.repo_name}}_Final_Report.ipynb', 'repo_name': 'jupyterdayATL/cookiecutter_template', 'size': '2264'}