Created
June 28, 2020 16:53
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# importing the required libaries | |
from flask import Flask, render_template, request, redirect, url_for | |
from get_images import get_images, get_path, get_directory | |
from get_prediction import get_prediction | |
from generate_html import generate_html | |
from torchvision import models | |
import json | |
app = Flask(__name__) | |
# mapping | |
imagenet_class_mapping = json.load(open('imagenet_class_index.json')) | |
# use the pre-trained model | |
model = models.densenet121(pretrained=True) | |
model.eval() | |
# define the function to get the images from the url and predicted the class | |
def get_image_class(path): | |
# get images from the URL and store it in a given path | |
get_images(path) | |
# predict the image class of the images with provided directory | |
path = get_path(path) | |
images_with_tags = get_prediction(model, imagenet_class_mapping, path) | |
# generate html file to render once we predict the classes | |
generate_html(images_with_tags) |
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# by deafult render the "home.html" | |
@app.route('/') | |
def home(): | |
return render_template('home.html') | |
@app.route('/', methods=['POST', 'GET']) | |
def get_data(): | |
if request.method == 'POST': | |
user = request.form['search'] | |
# if search button hit, call the function get_image_class | |
get_image_class(user) | |
#render the image_class.html | |
return redirect(url_for('success', name=get_directory(user))) | |
@app.route('/success/<name>') | |
def success(name): | |
return render_template('image_class.html') | |
if __name__ == '__main__' : | |
app.run(debug=True) |
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