app2.py

import os
import uuid
import urllib
from flask import Flask, render_template, request

app = Flask(__name__)
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
model = load_model(os.path.join(BASE_DIR , 'model.hdf5'))


ALLOWED_EXT = set(['jpg' , 'jpeg' , 'png' , 'jfif'])
def allowed_file(filename):
    return '.' in filename and \
           filename.rsplit('.', 1)[1] in ALLOWED_EXT

classes = ['airplane' ,'automobile', 'bird' , 'cat' , 'deer' ,'dog' ,'frog', 'horse' ,'ship' ,'truck']


def predict(filename, model):
    img = load_img(filename, target_size=(32, 32))
    img = img_to_array(img)
    img = img.reshape(1, 32, 32, 3)

    img = img.astype('float32')
    img = img/255.0
    result = model.predict(img)

    dict_result = {}
    for i in range(10):
        dict_result[result[0][i]] = classes[i]

    res = result[0]
    res.sort()
    res = res[::-1]
    prob = res[:3]
    
    prob_result = []
    class_result = []
    for i in range(3):
        prob_result.append((prob[i]*100).round(2))
        class_result.append(dict_result[prob[i]])

    return class_result, prob_result

@app.route('/')
def home():
        return render_template("index.html")

@app.route('/success' , methods = ['GET' , 'POST'])
def success():
    error = ''
    target_img = os.path.join(os.getcwd(), 'static/images')
    if request.method == 'POST':
        if request.form:
            link = request.form.get('link')
            try:
                resource = urllib.request.urlopen(link)
                unique_filename = str(uuid.uuid4())
                filename = unique_filename+".jpg"
                img_path = os.path.join(target_img , filename)
                output = open(img_path, "wb")
                output.write(resource.read())
                output.close()
                img = filename

                class_result, prob_result = predict(img_path, model)

                predictions = {
                      "class1": class_result[0],
                      "class2": class_result[1],
                      "class3": class_result[2],
                      "prob1": prob_result[0],
                      "prob2": prob_result[1],
                      "prob3": prob_result[2],
                }

            except Exception as e:
                print(str(e))
                error = 'This image from this site is not accessible or inappropriate input'

            if len(error) == 0:
                return render_template('success.html', img=img, predictions=predictions)
            else:
                return render_template('index.html', error=error)

            
        elif request.files:
            file = request.files['file']
            if file and allowed_file(file.filename):
                file.save(os.path.join(target_img, file.filename))
                img_path = os.path.join(target_img, file.filename)
                img = file.filename

                class_result, prob_result = predict(img_path, model)

                predictions = {
                      "class1": class_result[0],
                      "class2": class_result[1],
                      "class3": class_result[2],
                      "prob1": prob_result[0],
                      "prob2": prob_result[1],
                      "prob3": prob_result[2],
                }

            else:
                error = "Please upload images of jpg , jpeg and png extension only"

            if len(error) == 0:
                return render_template('success.html', img=img, predictions=predictions)
            else:
                return render_template('index.html', error=error)

    else:
        return render_template('index.html')

if __name__ == "__main__":
    app.run(debug=True)