Create app.py

app.py

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+import tensorflow as tf
+from tensorflow.keras.applications import EfficientNetB0
+
+efficient_net = EfficientNetB0(weights='imagenet',include_top=False,input_shape=(150, 150, 3))
+
+model = efficient_net.output
+model = tf.keras.layers.GlobalAveragePooling2D()(model)
+model = tf.keras.layers.Dense(64, activation='relu')(model)
+model = tf.keras.layers.Dropout(rate=0.1)(model)
+model = tf.keras.layers.Dense(32, activation='relu')(model)
+model = tf.keras.layers.Dropout(rate=0.1)(model)
+model = tf.keras.layers.Dense(2, activation='sigmoid')(model)
+model = tf.keras.models.Model(inputs=efficient_net.input, outputs=model)
+
+model.compile(loss='binary_crossentropy',optimizer = 'Adam', metrics= ['accuracy'])
+
+model.load_weights('/kaggle/working')
+
+import gradio as gr
+
+def cardiomegaly(img):
+    img = img.reshape(1, 150, 150, 3)
+    prediction = model.predict(img).tolist()[0]
+    class_names = ["False", "True"]
+    return {class_names[i]: prediction[i] for i in range(2)}
+#set the user uploaded image as the input array
+#match same shape as the input shape in the model
+im = gr.inputs.Image(shape=(150, 150), image_mode='RGB', invert_colors=False, source="upload")
+#setup the interface
+iface = gr.Interface(
+    fn = cardiomegaly, 
+    inputs = im, 
+    outputs = gr.outputs.Label(),
+)
+iface.launch(share=True)