Create app.py

app.py

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+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 Image is  "+"Not Occluded  "+"with a Score of  "+str(round(score,2))
+    else:
+        return "The Image is  "+"Occluded  "+"with a Score of  "+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","img4.png","img7.png","img8.png","img9.png"]
+
+
+demo_app = gr.Interface(
+    fn=occ_predict, 
+    inputs=inputs,
+    outputs= "text",
+    title = "Prediction of Occulded or not Occulded Image",
+    examples = EXAMPLES,
+    cache_example = True,
+    live = True,
+    theme = 'huggingface'
+)
+demo_app.launch(debug=True, enable_queue = True)