ipdeploy.py

# -*- coding: utf-8 -*-


import os

os.chdir('F:\main files\input')
import tensorflow as tf
from numpy.random import seed
seed(101)

tf.random.set_seed(101)

import pandas as pd
import numpy as np
#import keras
#from keras import backend as K

import tensorflow
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import categorical_crossentropy
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint



from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import itertools
import shutil
import matplotlib.pyplot as plt


# create a copy of a mobilenet model

mobile = tensorflow.keras.applications.mobilenet.MobileNet()


# CREATE THE MODEL ARCHITECTURE

# Exclude the last 5 layers of the above model.
# This will include all layers up to and including global_average_pooling2d_1
x = mobile.layers[-6].output

# Create a new dense layer for predictions
# 7 corresponds to the number of classes
x = Dropout(0.25)(x)
predictions = Dense(7, activation='softmax')(x)

# inputs=mobile.input selects the input layer, outputs=predictions refers to the
# dense layer we created above.

model = Model(inputs=mobile.input, outputs=predictions)

train_path = 'base_dir/train_dir'
valid_path = 'base_dir/val_dir'



model.load_weights('model.h5')

from PIL import Image
import numpy as np
from skimage import transform
def load(filename):
    np_image = Image.open(filename)
    np_image = np.array(np_image).astype('float32')/255
    np_image = transform.resize(np_image, (224, 224, 3))
    np_image = np.expand_dims(np_image, axis=0)
    return np_image

image = load('test6.jpg')
predictions=model.predict(image)
print("You have been diagnosed with asdsad#@R#AEF" )
print(predictions[0])
print(predictions.argmax(axis=1)[0])