skinapp.py

from __future__ import division, print_function
# coding=utf-8
import sys
import os
import glob
import re
import numpy as np
from PIL import Image as pil_image

# Keras

from tensorflow.keras.applications.imagenet_utils import preprocess_input, decode_predictions
from tensorflow.keras.models import Model , load_model
from keras.preprocessing import image

# Flask utils
from flask import Flask, redirect, url_for, request, render_template
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer

app = Flask(__name__,template_folder='template')


model1= load_model('E:/project/model.h5')     

lesion_classes_dict = {
     0:'Melanocytic nevi',
     1:'Melanoma',
     2:'Benign keratosis-like lesions ',
     3:'Basal cell carcinoma',
     4:'Actinic keratoses',
     5:'Vascular lesions',
     6:'Dermatofibroma'
}


def model_predict(img_path, model1):
    img = image.load_img(img_path, target_size=(128,128,3))
  
    #img = np.asarray(pil_image.open('img').resize((120,120)))
    #x = np.asarray(img.tolist())

    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)

    # Be careful how your trained model deals with the input
    # otherwise, it won't make correct prediction!
    #x = preprocess_input(x, mode='caffe')

    preds = model1.predict(x.reshape(1,128,128,3))
    return preds


@app.route('/', methods=['GET'])
def index():
    # Main page
    return render_template('index1.html')


@app.route('/predict', methods=['GET', 'POST'])
def upload():
    if request.method == 'POST':
        # Get the file from post request
        f = request.files['file']

        # 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)

        # Make prediction
        preds = model_predict(file_path , model1)
        
        top3 = np.argsort(preds[0])[:-4:-1]
        result =[]
        for i in range(2):
           a = ("{}".format(lesion_classes_dict[top3[i]]))
       
        result.append(a) 
        result = str(result)
        # Process your result for human
        #pred_class = preds.argmax(axis=-1)            # Simple argmax
        #pred_class = decode_predictions(preds, top=1)   
        #pr = lesion_classes_dict[pred_class[0]]
        #result =str(pr)         
        return result
    return None


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