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| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Thu Jun 11 22:34:20 2020 | |
| @author: Krish Naik | |
| """ | |
| from __future__ import division, print_function | |
| # coding=utf-8 | |
| import sys | |
| import os | |
| import glob | |
| import re | |
| import numpy as np | |
| import tensorflow as tf | |
| import tensorflow as tf | |
| from tensorflow.compat.v1 import ConfigProto | |
| from tensorflow.compat.v1 import InteractiveSession | |
| config = ConfigProto() | |
| config.gpu_options.per_process_gpu_memory_fraction = 0.2 | |
| config.gpu_options.allow_growth = True | |
| session = InteractiveSession(config=config) | |
| # Keras | |
| from tensorflow.keras.applications.resnet50 import preprocess_input | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.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 | |
| # Define a flask app | |
| app = Flask(__name__) | |
| # Model saved with Keras model.save() | |
| MODEL_PATH ='model_resnet152V2.h5' | |
| # Load your trained model | |
| model = load_model(MODEL_PATH) | |
| def model_predict(img_path, model): | |
| print(img_path) | |
| img = image.load_img(img_path, target_size=(224, 224)) | |
| # Preprocessing the image | |
| x = image.img_to_array(img) | |
| # x = np.true_divide(x, 255) | |
| ## Scaling | |
| x=x/255 | |
| 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) | |
| preds = model.predict(x) | |
| preds=np.argmax(preds, axis=1) | |
| if preds==0: | |
| preds="The leaf is diseased cotton leaf" | |
| elif preds==1: | |
| preds="The leaf is diseased cotton plant" | |
| elif preds==2: | |
| preds="The leaf is fresh cotton leaf" | |
| else: | |
| preds="The leaf is fresh cotton plant" | |
| return preds | |
| def index(): | |
| # Main page | |
| return render_template('index.html') | |
| 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, model) | |
| result=preds | |
| return result | |
| return None | |
| if __name__ == '__main__': | |
| app.run(port=5001,debug=True) | |