text: import os
  import time
  import cv2
  import sys
  import csv
  import matplotlib.pyplot as plt
  import matplotlib.image as mpimg
  import tensorflow as tf
  import keras_preprocessing
  import streamlit as st
  import numpy as np
  import pandas as pd
  from numpy import argmax
  from PIL import Image , ImageEnhance
  from resizeimage import resizeimage
  from utils import label_map_util
  from utils import visualization_utils as vis_util
  from keras.preprocessing import image
  from keras_preprocessing.image import ImageDataGenerator
  from pathlib import Path
  
  tf.executing_eagerly() #implemented by default in tensorflow2
  
  
  MODEL_NAME = './object_detection/inference_graph'
  IMAGE_NAME = './object_detection/images/out.jpg'
  
  CWD_PATH = os.getcwd()
  
  PATH_TO_CKPT = os.path.join('./object_detection/inference_graph/frozen_inference_graph.pb')
  PATH_TO_LABELS = os.path.join('./object_detection/training/labelmap.pbtxt')
  PATH_TO_IMAGE = os.path.join('./object_detection/images/out.jpg')
  
  NUM_CLASSES = 6
  
  
  label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
  categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
  category_index = label_map_util.create_category_index(categories)
  
  detection_graph = tf.Graph()
  with detection_graph.as_default():
      od_graph_def = tf.GraphDef()
      with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
          serialized_graph = fid.read()
          od_graph_def.ParseFromString(serialized_graph)
          tf.import_graph_def(od_graph_def, name='')
  
      sess = tf.Session(graph=detection_graph)
  
  # Input tensor is the image
  image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
  
  # Output tensors are the detection boxes, scores, and classes
  # Each box represents a part of the image where a particular object was detected
  detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
  
  # Each score represents level of confidence for each of the objects.
  # The score is shown on the result image, together with the class label.
  detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
  detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
  
  # Number of objects detected
  num_detections = detection_graph.get_tensor_by_name('num_detections:0')
  
  # Load image using OpenCV and
  # expand image dimensions to have shape: [1, None, None, 3]
  # i.e. a single-column array, where each item in the column has the pixel RGB value
  in_image = cv2.imread(PATH_TO_IMAGE)
  image_rgb = cv2.cvtColor(in_image, cv2.COLOR_BGR2RGB)
  image_expanded = np.expand_dims(
      image_rgb, axis=0)
  
  # Perform the actual detection by running the model with the image as input
  (boxes, scores, classes, num) = sess.run(
      [detection_boxes, detection_scores, detection_classes, num_detections],
      feed_dict={image_tensor: image_expanded})
  
  # Draw the results of the detection (aka 'visulaize the results')
  
  vis_util.visualize_boxes_and_labels_on_image_array(
      in_image,
      np.squeeze(boxes),
      np.squeeze(classes).astype(np.int32),
      np.squeeze(scores),
      category_index,
      use_normalized_coordinates=True,
      line_thickness=8,
      min_score_thresh=0.60)
  
  #@st.cache
  def read_markdown_file(markdown_file):
      return Path(markdown_file).read_text()
  	
  def classification():
      global path
      global cn
      model = tf.keras.models.Sequential([
      # Note the input shape is the desired size of the image 150x150 with 3 bytes color
      # This is the first convolution
      tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(150, 150, 3)),
      tf.keras.layers.MaxPooling2D(2, 2),
      # The second convolution
      tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
      tf.keras.layers.MaxPooling2D(2,2),
      # The third convolution
      tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
      tf.keras.layers.MaxPooling2D(2,2),
      # The fourth convolution
      tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
      tf.keras.layers.MaxPooling2D(2,2),
      # Flatten the results to feed into a DNN
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dropout(0.5),
      # 512 neuron hidden layer
      tf.keras.layers.Dense(512, activation='relu'),
      tf.keras.layers.Dense(27, activation='softmax')
      ])
      
      LABELS = ['Apple___Apple_scab','Apple___Black_rot','Apple___Cedar_apple_rust','Apple___healthy','Blueberry___healthy',
  		  'Cherry___healthy','Cherry___Powdery_mildew','Grape___Black_rot','Grape___Esca_Black_Measles','Grape___healthy',
  		  'Grape___Leaf_blight_Isariopsis_Leaf_Spot','Orange___Haunglongbing','Peach___Bacterial_spot','Peach___healthy',
  		  'Pepper_bell___Bacterial_spot','Pepper_bell___healthy','Potato___Early_blight','Potato___healthy','Potato___Late_blight',
  		  'Raspberry___healthy','Soybean___healthy','Squash___Powdery_mildew','Strawberry___healthy','Strawberry___Leaf_scorch']
  
      model.load_weights("./object_classification/rps.h5") #load pretrained model
      path = './object_classification/images/out.jpg' # path for image to be processed
      img = image.load_img(path,target_size=(150,150))  
      x = image.img_to_array(img)
      x = np.expand_dims(x , axis=0)
      images = np.vstack([x])
      classes = model.predict(images , batch_size =10) # predicting images
      result = argmax(classes)
      cn = LABELS[result]
  
  classification()
  
  def main():
      st.set_option('deprecation.showfileUploaderEncoding', False)
      st.markdown('<style>body{-webkit-app-region: drag;}</style>', unsafe_allow_html=True)
      st.title("Plant Disease Detection & Classification")
      st.text("Build with Streamlit and Tensorflow")
      activities = ["About" ,"Plant Disease"]
      choice = st.sidebar.selectbox("Select Activty",activities)
      enhance_type = st.sidebar.radio("Type",["Upperside of Leaves Only", "Underside of Leaves Only", "Fruits Only", "All Classes Combined"])
      
  
      if choice == 'About':
          intro_markdown = read_markdown_file("./doc/about.md")
          st.markdown(intro_markdown, unsafe_allow_html=True)
  
      if choice == 'Plant Disease' and enhance_type == 'Upperside of Leaves Only':
          st.header("Plant Disease Classification")
          image_input = st.file_uploader("Upload Image", type=['jpg'])
          st.markdown("* * *")
  
          if image_input is not None:
              some_image = Image.open(image_input)
              saved_image = some_image.save('./object_classification/images/out.jpg')
  
              if st.button('Classify'):
                  st.image(path, use_column_width=True)
                  with st.spinner('Your image is processing'):
                      time.sleep(5)
                  # st.write('**Plant Disease name**: ',cn)
                  st.success(cn)
                  st.balloons()
      if choice == 'Plant Disease' and enhance_type == 'Underside of Leaves Only':
          st.header("Plant Disease Classification")
          image_input = st.file_uploader("Upload Image", type=['jpg'])
          st.markdown("* * *")
  
          if image_input is not None:
              some_image = Image.open(image_input)
              saved_image = some_image.save('./object_classification/images/out.jpg')
  
              if st.button('Classify'):
                  st.image(path, use_column_width=True)
                  with st.spinner('Your image is processing'):
                      time.sleep(5)
                  # st.write('**Plant Disease name**: ',cn)
                  st.success(cn)
                  st.balloons()
      if choice == 'Plant Disease' and enhance_type == 'Fruits Only':
          st.header("Plant Disease Classification")
          image_input = st.file_uploader("Upload Image", type=['jpg'])
          st.markdown("* * *")
  
          if image_input is not None:
              some_image = Image.open(image_input)
              saved_image = some_image.save('./object_classification/images/out.jpg')
  
              if st.button('Classify'):
                  st.image(path, use_column_width=True)
                  with st.spinner('Your image is processing'):
                      time.sleep(5)
                  # st.write('**Plant Disease name**: ',cn)
                  st.success(cn)
                  st.balloons()
      if choice == 'Plant Disease' and enhance_type == 'All Classes Combined':
          st.header("Plant Disease Classification")
          image_input = st.file_uploader("Upload Image", type=['jpg'])
          st.markdown("* * *")
  
          if image_input is not None:
              some_image = Image.open(image_input)
              saved_image = some_image.save('./object_classification/images/out.jpg')
  
              if st.button('Classify'):
                  st.image(path, use_column_width=True)
                  with st.spinner('Your image is processing'):
                      time.sleep(5)
                  # st.write('**Plant Disease name**: ',cn)
                  st.success(cn)
                  st.balloons()
  
  
  
  main()
  