Flower Freshness Model Commit After Rebase

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)
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