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#Import necessary libraries
from flask import Flask, render_template, request
import webbrowser
import numpy as np
import os
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.models import load_model
#load model
model =load_model("model/v4_1_pred_stra_dis.h5")
print('@@ Model loaded')
def open_url_in_browser(url):
webbrowser.open_new(url)
def pred_cot_dieas(cott_plant):
test_image = load_img(cott_plant, target_size=(150, 150)) # load image
print("@@ Got Image for prediction")
test_image = img_to_array(test_image)/255 # convert image to np array and normalize
test_image = np.expand_dims(test_image, axis=0) # change dimension 3D to 4D
result = model.predict(test_image).round(3) # predict diseased plant or not
print('@@ Raw result = ', result)
pred = np.argmax(result) # get the index of max value
if pred == 0:
url = 'http://localhost/typroject/diseases-anthracnose-fruit-rot/' # if index 0 burned leaf
elif pred == 1:
url = 'grey_mold.html' # if index 1
elif pred == 2:
url = 'leaf_spot.html' # if index 2 fresh leaf
else:
url = 'powdery_mildew_leaf.html' # if index 3
open_url_in_browser(url)
return "Diseased Strawberry Plant", url
#------------>>pred_cot_dieas<<--end
# Create flask instance
app = Flask(__name__)
# render index.html page
@app.route("/", methods=['GET', 'POST'])
def home():
return render_template('index1.html')
# get input image from client then predict class and render respective .html page for solution
@app.route("/predict", methods = ['GET','POST'])
def predict():
if request.method == 'POST':
file = request.files['image'] # fet input
filename = file.filename
print("@@ Input posted = ", filename)
file_path = os.path.join('static/user uploaded', filename)
file.save(file_path)
print("@@ Predicting class......")
pred, output_page = pred_cot_dieas(cott_plant=file_path)
return render_template(output_page, pred_output = pred, user_image = file_path)
# For local system & cloud
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860)
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