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app.py
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app.py
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from flask import Flask, request, render_template, jsonify, send_from_directory, current_app, redirect
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer
from botnoi import cv
import requests
import joblib
import numpy as np
import os
from train_model_from_google_image_search import run
app = Flask(__name__)
@app.route('/', methods=['GET'])
def index():
# Main page
return render_template('index.html')
@app.route('/predict_url')
def classifier():
try:
img_url = request.values['p_image_url']
# resize to 224x224x3
img = cv.image(img_url)
# extract features
feat = img.getresnet50()
# load trained model
model = joblib.load('vehicle-classification.p')
#Predict classes and probabilities with LinearSVC
probList = model.predict_proba([feat])[0]
maxprobind = np.argmax(probList)
prob = probList[maxprobind]
outclass = model.classes_[maxprobind]
result = {'img_url': img_url,
'prediction': outclass,
'probability': prob
}
return jsonify(result)
except Exception as e:
print(e)
@app.route('/predict_image', methods=['GET', 'POST'])
def upload():
if request.method == 'POST':
if 'file' not in request.files:
print('no file')
return redirect(request.url)
# Get the file from post request
f = request.files['file']
# load trained model
model = joblib.load('example_model/vehicle-classification.p')
# Save the file to ./uploads
basepath = os.path.dirname(__file__)
file_path = os.path.join(
basepath, 'uploads', secure_filename(f.filename))
f.save(file_path)
# resize to 224x224x3
img = cv.image(file_path)
# extract features
feat = img.getresnet50()
probList = model.predict_proba([feat])[0]
maxprobind = np.argmax(probList)
prob = probList[maxprobind]
outclass = model.classes_[maxprobind]
return outclass
return None
# model classifier builder
@app.route('/model_builder', methods=['GET', 'POST'])
def c_predict():
message = ''
if request.method == 'POST':
keywords = request.form.getlist('field[]')
print(keywords)
# input keywords to function and return a mod
_, modFile = run(keywords)
# model name
model_name = modFile
return render_template('download.html', value=model_name)
# message = "Succesfully Register"
return render_template('custom_predict.html', message=message)
# download file
@app.route('/return-files/<filename>')
def return_files(filename):
# path to model
path = os.path.join(current_app.root_path, 'classifier_model')
return send_from_directory(directory=path, filename=filename, as_attachment=True)
if __name__=='__main__':
app.run(host='127.0.0.1', port=5002)