import gradio as gr from tensorflow.keras.models import load_model import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) from sklearn.metrics import roc_curve,auc,classification_report,confusion_matrix import matplotlib.pyplot as plt import matplotlib.image as mpimg from PIL import Image import cv2 import keras from keras.utils import np_utils from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint,ReduceLROnPlateau from tensorflow.keras.models import Model from tensorflow.keras.optimizers import Adam,SGD,RMSprop,Adamax from tensorflow.keras.models import Model, Sequential from tensorflow.keras.callbacks import ReduceLROnPlateau from sklearn.model_selection import StratifiedKFold from tensorflow.keras.applications import MobileNetV2 from random import shuffle from tqdm import tqdm import scipy import skimage from skimage.transform import resize import random import os from io import BytesIO import h5py model_file_path = "mobile_net_occ.h5" plt. figure(figsize=(10,9)) def occ_predict(img_content): im = [] image=cv2.imread(img_content) print(type(image)) imgplot = plt.imshow(image) plt.show() img = Image.fromarray(image, 'RGB') resize_image = img.resize((50, 50)) im.append(np.array(resize_image)) fv = np.array(im) np_array_img = fv.astype('float32') / 255 model_gcs = h5py.File(model_file_path, 'r') print(model_file_path) myModel = load_model(model_gcs) prediction = myModel.predict(np_array_img) score = prediction[0][0].item() thresh = 0.5 if score > thresh: return "The house is "+"Not Occluded "+" and the occlusion Score is "+str(round(score,2)) else: return "The house is "+"Occluded "+"and the occlusion Score is "+str(round(score,2)) #predicted_label, score = occ_predict("img1.jpg") inputs = gr.inputs.Image(type = 'filepath') #label = gr.outputs.Label(num_top_classes=2) EXAMPLES = ["img1.png","img2.png","img3.png","img10.png","img8.png","img9.png"] DESCRIPTION = "Occlusion means the act of closing, blocking or shutting something or the state of being closed or blocked" demo_app = gr.Interface( fn=occ_predict, inputs=inputs, outputs= "text", title = "Prediction of Occluded or not Occluded Image", description = DESCRIPTION, examples = EXAMPLES, #cache_example = True, #live = True, theme = 'huggingface' ) demo_app.launch()