Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.models import load_model
+import tensorflow_addons as tfa
+import os
+import numpy as np
+
+
+# labels= {'Burger King': 0, 'KFC': 1,'McDonalds': 2,'Other': 3,'Starbucks': 4,'Subway': 5}
+HEIGHT,WIDTH=224,224
+NUM_CLASSES=6
+
+model=load_model('Models/best_model1.h5')
+
+# def classify_image(inp):
+#   np.random.seed(143)
+#   inp = inp.reshape((-1, HEIGHT,WIDTH, 3))
+#   inp = tf.keras.applications.nasnet.preprocess_input(inp) 
+#   prediction = model.predict(inp)
+#   ###label = dict((v,k) for k,v in labels.items())
+#   predicted_class_indices=np.argmax(prediction,axis=1)
+#   result = {}
+#   for i in range(len(predicted_class_indices)):
+#       if predicted_class_indices[i] < NUM_CLASSES:
+#           result[labels[predicted_class_indices[i]]]= float(predicted_class_indices[i])
+#   return result 
+
+def classify_image(inp):
+    np.random.seed(143)
+    labels = {'Cat': 0, 'Dog': 1}
+    NUM_CLASSES = 2
+    inp = inp.reshape((-1, HEIGHT, WIDTH, 3))
+    inp = tf.keras.applications.nasnet.preprocess_input(inp)
+    prediction = model.predict(inp)
+    predicted_class_indices = np.argmax(prediction, axis=1)
+
+    label_order = ["Cat","Dog"]
+
+    result = {label: float(f"{prediction[0][labels[label]]:.6f}") for label in label_order}
+
+    return result
+
+    
+image = gr.Image(shape=(HEIGHT,WIDTH),label='Input')
+label = gr.Label(num_top_classes=2)
+
+gr.Interface(fn=classify_image, inputs=image, outputs=label, title='Smart Pet Classifier').launch(debug=False)
+
+