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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
@app.route('/', methods=['GET'])
def index():
# Main page
return render_template('index.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, model)
result=preds
return result
return None
if __name__ == '__main__':
app.run(port=5001,debug=True)
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