app.py

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

# Keras
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from keras.models import load_model
from keras.utils import load_img, img_to_array

# importing tensorflow for prediction
import tensorflow as tf
from tensorflow.keras import models, layers
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image


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

# Define a flask app
app = Flask(__name__)

# Model saved with Keras model.save()
MODEL_PATH = 'models/hackmodel.h5'

# Load your trained model
model = load_model(MODEL_PATH)


def model_predict(img_path, model):
    class_names=['Grassy Shoots', 'Healthy', 'Mites', 'Ring Spot', 'YLD']
    

    img = Image.open(img_path)

    img=img.resize((256,256))
    img_array = tf.keras.preprocessing.image.img_to_array(img)

    img_array = tf.expand_dims(img_array, 0)

    predictions = model.predict(img_array)

    predicted_class = class_names[np.argmax(predictions[0])]
    confidence = round(100 * (np.max(predictions[0])), 2)
    return predicted_class, confidence


@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
        predicted_class, confidence = model_predict(file_path, model)
    
        return predicted_class
    return None

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