11 | 
12 |
13 |
14 |
15 |
16 | Note: This is for educational purposes only, this may not be efficient or bug-free. Also, this is just a demo on how
17 | JS and Python can be used to interact together. This demo is *NOT* meant to show face detection or object detection.
18 |
19 | ## General Dependenices
20 | * Python
21 | * NodeJS
22 | * electron.js
23 | * python-shell
24 |
25 | ## Specific Dependencies
26 | * weather module:
27 | * requests
28 | * beautifulsoup4
29 |
30 | * object detection module
31 | * Flask
32 | * tensorflow
33 | * keras
34 |
35 | ## Usage
36 | * Clone the repo, and then
37 | ```sh
38 | $ cd electron-app-with-python-gui
39 | $ pip install -r requirements.txt
40 | $ npm install
41 | $ npm start
42 | ```
43 | * If you want to use the object detection module, make sure the flask server [object_detection.py] is up and running before starting the GUI.
44 | ```sh
45 | $ cd engine
46 | $ python object_detection.py
47 | ```
48 |
49 | ## Note:
50 |
51 | weather_engine.py uses web-scraping to pull data off the internet, from a particular website. If this site happens to be modified
52 | or changed in the future, the code *might* break. However, this can be fixed by analyzing the new layout of the site and adjusting
53 | the python code accordingly.
54 |
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/engine/.DS_Store:
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https://raw.githubusercontent.com/SouravJohar/python-app-with-electron-gui/99e2eb130ba66a5dbb1b139feba9f06edb385372/engine/.DS_Store
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/engine/boot.sh:
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1 | kill -9 $(lsof -t -i:5000)
2 | echo killed pre
3 | python object_detection.py &
4 | cd ..
5 | cd gui
6 | npm start
7 | kill -9 $(lsof -t -i:5000)
8 | echo killed
9 |
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/engine/object_detection.py:
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1 | from flask import Flask, render_template, request
2 | from keras.applications.inception_v3 import *
3 | from keras.preprocessing import image
4 | import tensorflow as tf
5 | import numpy as np
6 | import os
7 | import json
8 |
9 | app = Flask(__name__, template_folder=os.path.abspath("../gui/"))
10 |
11 | model = InceptionV3(weights='imagenet')
12 | graph = tf.get_default_graph()
13 |
14 |
15 | def predict(path):
16 | img = image.load_img(path, target_size=(299, 299))
17 | xy = image.img_to_array(img)
18 | xy = np.expand_dims(xy, axis=0)
19 | xy = preprocess_input(xy)
20 | global graph
21 | with graph.as_default():
22 | preds = model.predict(xy)
23 | preds = decode_predictions(preds, top=3)[0]
24 | acc = []
25 | classes = []
26 | for x in preds:
27 | acc.append(x[2])
28 | classes.append(x[1])
29 | return acc, classes
30 |
31 |
32 | @app.route("/detect", methods=["GET", "POST"])
33 | def detect():
34 | if request.method == "GET":
35 | return render_template("object.html")
36 |
37 | if request.method == "POST":
38 | path = request.form["path"]
39 |
40 | accuracies, classes = predict(path)
41 |
42 | return render_template("object.html", preds=accuracies,
43 | classes=json.dumps(classes), img=path)
44 |
45 | app.run()
46 |
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/engine/weather_engine.py:
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1 | import requests
2 | from bs4 import BeautifulSoup as bs
3 | import sys
4 |
5 | city = sys.argv[1]
6 |
7 |
8 | def get_weather(place):
9 | place = place.replace(" ", "-")
10 | url = "https://www.weather-forecast.com/locations/" + place + "/forecasts/latest"
11 | r = requests.get(url)
12 | soup = bs(r.content, "lxml")
13 | weather = soup.findAll("span", {"class": "phrase"})[0].text
14 | return weather
15 |
16 | print(get_weather(city))
17 | sys.stdout.flush()
18 |
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/gui/.DS_Store:
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/gui/gui.html:
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1 |
2 |
3 |
4 |
5 |
6 | Rendered by HTML and JavaScript
10 |
15 | 
22 | Rendered by HTML and JavaScript
65 |Rendered by HTML and JavaScript
16 |