Rebase Flower Freshness

app_disease.py

-import base64   
-import datetime
-import cv2 as cv
-import numpy as np
-import pandas as pd
-from tkinter import *
-import firebase_admin
-import tensorflow as tf
-import tkinter.filedialog
-from firebase_admin import db
-from PIL import ImageTk, Image
-from firebase_admin import credentials
-
-node = 'flower_disease'
-model = tf.keras.models.load_model('models/flower_disease_detector.h5')
-cred = credentials.Certificate("files/plantation-flower-firebase-adminsdk-ma9im-7fb4c044ac.json")
-default_app = firebase_admin.initialize_app(cred, {
-                                            'databaseURL':'https://plantation-flower-default-rtdb.firebaseio.com/'
-                                            })
-
-ref_node = db.reference(node)
-
-def inference_disease(
-                    image_path,
-                    target_size = (224, 224),
-                    class_dict = {0: 'Black Spot', 1: 'Downy Mildew', 2: 'Fresh Leaf'}
-                    ):
-    class_dict_rev = dict((v,k) for k,v in class_dict.items())
-    img_file = image_path.split('/')[-2]
-    image = cv.imread(image_path)
-    image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
-    image = cv.resize(image, target_size)
-    image = np.expand_dims(image, axis=0)
-    image = tf.keras.applications.xception.preprocess_input(image)
-    pred = model.predict(image, verbose=0).squeeze()
-    pred = np.argmax(pred)
-    pred = class_dict[class_dict_rev[img_file]]
-    return pred
-
-def write_data_firebase(
-                        image_path,
-                        disease,
-                        ):
-    
-    image_path = image_path.replace('\\', '/')
-    file_name = image_path.split('/')[-1].split('.')[0].strip()
-    presentDate = datetime.datetime.now()
-    unix_timestamp = int(datetime.datetime.timestamp(presentDate)*1000)
-
-    # # convert image to base64 & store in firebase
-    # with open(image_path, "rb") as image_file:
-    #     encoded_string = base64.b64encode(image_file.read())
-
-    prefix = 'data:image/jpeg;base64,'
-    image_np = cv.imread(image_path)
-    image_np = cv.cvtColor(image_np, cv.COLOR_BGR2RGB)
-
-    image_np = cv.resize(image_np, (500, 500))
-    _, buffer = cv.imencode('.jpg', image_np)
-    encoded_string = base64.b64encode(buffer)
-    encoded_string = encoded_string.decode('utf-8')
-
-    data = {
-            "image": f"{prefix}{encoded_string}",
-            "disease": f"{disease}",
-            "timestamp": unix_timestamp
-            }
-    ref_node.push(data)
-
-
-image_path = tkinter.filedialog.askopenfilename(
-                                                initialdir='data/flower_disease_type_dataset/',
-                                                title='Select Image',
-                                                filetypes=(('JPG Files', '*.JPG'), ('All Files', '*.*'))
-                                                )
-disease = inference_disease(image_path)
-write_data_firebase(image_path, disease)
-image = Image.open(image_path)
-image = image.resize((400, 400), Image.Resampling.LANCZOS)
-image = ImageTk.PhotoImage(image)
-image_label = Label(image=image)
-image_label.image = image
-image_label.grid(row=0, column=0, columnspan=3)
-
-print('Disease: ', disease)
\ No newline at end of file