Create app.py

app.py

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+import tensorflow as tf
+tf.__version__
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tensorflow.keras.applications import vgg16
+#from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D, Dropout, Input, Dense, Flatten
+from tensorflow.keras.utils import load_img, img_to_array
+from sklearn.metrics import confusion_matrix
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tensorflow.keras.applications import vgg16
+#from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D, Dropout, Input, Dense, Flatten
+from tensorflow.keras.utils import load_img, img_to_array
+from sklearn.metrics import confusion_matrix
+
+
+model = tf.keras.models.load_model('my_model.keras')
+
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+
+def sepia(input_img_path):
+    img = load_img(input_img_path,target_size=(224,224))
+    img = img_to_array(img)
+    img = img / 255
+    img = img.reshape(1,224,224,3)
+    p = (model.predict(img)>=0.5).astype(int)[0][0]
+    if p==0:
+        return "Men"
+    else:
+        return "women"
+
+demo = gr.Interface(fn=sepia,inputs= gr.Image(type="filepath",height=200,width=300),outputs="text")
+demo.launch()