A mvp (web app, through flask/dash/streamlit etc) that takes input (upload a skin image) and...

Question

A mvp (web app, through flask/dash/streamlit etc) that takes input (upload a skin image) and generate an output (recommend helpful products that works for their skin type after classifying the skin type).

Data source: amazon and Sephora reviews of different skin care products – users often upload photos with problems described. Dermnet website also contains pictures with skin problems such as facial acnes, eczema

To do: 1. Scrape those websites (e.g., Amazon, sephora) to get skin images with product reviews, as well as websites with labeled data (e.g., dermnet). Parse data, do image analysis and nlp and create labels (skin type) from the data, such that classification of those different skin types e.g., acne prone; redness; oily) can be done when input image is passed in. 2. Case-based retrieval from scraped and parsed data to recommend skin care products that work for the user input based on the stage 1 classification.

- only an mvp to have something up and working so do not need extensive data and labels, or elegant models – maybe 3 skin types for now and more data can be added for the next assignment, and models can be refined.

ds-a2oyma3g-h5idpqmr.docx

Ximi · Accepted Answer

from flask import Flask
from flask_cors import CORS, cross_origin
from flask import jsonify,request
import os
from sklearn.externals import joblib
from werkzeug.utils import secure_filename
from skimage import io
from skimage.transform import resize
import pandas as pd
import json
with open('All_Beauty_5.json', 'r') as f:
    data = [json.loads(d) for d in f.read().split('
') if d !='']
df = pd.

A mvp (web app, through flask/dash/streamlit etc) that takes input (upload a skin image) and generate an output (recommend helpful products that works for their skin type after classifying the skin...

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