main.py

import os
from app import app
import urllib.request
from flask import Flask, flash, request, redirect, url_for, render_template
from werkzeug.utils import secure_filename
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from keras.models import load_model
from keras.preprocessing import image
import numpy as np
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])

def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
    
@app.route('/')
def upload_form():
    return render_template('upload.html')

@app.route('/', methods=['POST'])
def upload_image():
    if 'file' not in request.files:
        flash('No file part')
        return redirect(request.url)
    file = request.files['file']
    if file.filename == '':
        flash('No image selected for uploading')
        return redirect(request.url)
    if file and allowed_file(file.filename):
        filename = secure_filename(file.filename)
        file.save(os.path.join(app.config['UPLOAD_FOLDER'], '1.jpg'))
        #print('upload_image filename: ' + filename)
        flash('Image successfully uploaded and displayed below')
        return render_template('upload.html', filename='1.jpg')
    else:
        flash('Allowed image types are -> png, jpg, jpeg, gif')
        return redirect(request.url)

@app.route('/display/<filename>')
def display_image(filename):
    #print('display_image filename: ' + filename)
    return redirect(url_for('static', filename='uploads/' + filename), code=301)
@app.route('/my-link/')
def my_link():
    print ('I got clicked!')
    MODEL_PATH = 'model.h5'
    
    # Load your trained model
    model = load_model(MODEL_PATH)
    new_img = image.load_img('C:/Users/ASUS/Desktop/New folder/static/uploads/1.jpg', target_size=(256, 256))
    
    # Preprocessing the image
    img = image.img_to_array(new_img)
    # x = np.true_divide(x, 255)
    img = np.expand_dims(img, axis=0)
    img = img/255
    # Be careful how your trained model deals with the input
    # otherwise, it won't make correct prediction!
    
    
    prediction = model.predict(img)
    
    d = prediction.flatten()
    j = d.max()
    li = ['Apple___Apple_scab','Apple___Black_rot','Apple___Cedar_apple_rust','Apple___healthy','Blueberry___healthy','Cherry_(including_sour)___Powdery_mildew','Cherry_(including_sour)___healthy','Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot','Corn_(maize)___Common_rust_','Corn_(maize)___Northern_Leaf_Blight','Corn_(maize)___healthy','Grape___Black_rot','Grape___Esca_(Black_Measles)','Grape___Leaf_blight_(Isariopsis_Leaf_Spot)','Grape___healthy','Orange___Haunglongbing_(Citrus_greening)','Peach___Bacterial_spot','Peach___healthy','Pepper,_bell___Bacterial_spot','Pepper,_bell___healthy','Potato___Early_blight','Potato___Late_blight','Potato___healthy','Raspberry___healthy','Soybean___healthy','Squash___Powdery_mildew','Strawberry___Leaf_scorch','Strawberry___healthy','Tomato___Bacterial_spot','Tomato___Early_blight','Tomato___Late_blight','Tomato___Leaf_Mold','Tomato___Septoria_leaf_spot','Tomato___Spider_mites Two-spotted_spider_mite','Tomato___Target_Spot','Tomato___Tomato_Yellow_Leaf_Curl_Virus','Tomato___Tomato_mosaic_virus','Tomato___healthy','background']
    for index,item in enumerate(d):
        if item == j:
            class_name = li[index]
            print("Following is our prediction:")
    return class_name



@app.route("/", methods=['GET', 'POST'])
def index():
    if request.method == 'POST':
        if request.form.get('action1') == 'VALUE1':
            pass # do something
        elif  request.form.get('action2') == 'VALUE2':
            pass # do something else
        else:
            pass # unknown
    elif request.method == 'GET':
        return render_template('index.html', form=form)
    
    return render_template("index.html")

if __name__ == "__main__":
    app.run(port=4996)