import pandas as pd import numpy as np # Make numpy values easier to read. np.set_printoptions(precision=3, suppress=True) import tensorflow as tf from tensorflow.keras import layers

abalone_train = pd.read_csv( "https://storage.googleapis.com/download.tensorflow.org/data/abalone_train.csv", names=["Length", "Diameter", "Height", "Whole weight", "Shucked weight", "Viscera weight", "Shell weight", "Age"]) abalone_train.head()

abalone_features = abalone_train.copy() abalone_labels = abalone_features.pop('Age')

abalone_features = np.array(abalone_features) abalone_features

abalone_model = tf.keras.Sequential() abalone_model.add(layers.Dense(64)) abalone_model.add(layers.Dense(32)) abalone_model.add(layers.Dense(1))

abalone_model.compile(optimizer=tf.optimizers.Adam(), loss=tf.losses.MeanSquaredError())

abalone_model.fit(abalone_features, abalone_labels, epochs=100)

abalone_model.summary()

abalone_test = np.array([[0.545,0.130,0.120,0.3515,0.1145,0.0665,0.1600]]) abalone_test

def predict_age_test(in_data): if in_data is not None: abalone_age = abalone_model.predict(in_data) else: print("The input is empty...") return abalone_age print("The age is " + str(predict_age_test(abalone_test)[[0]])+ "years")

#install gradio # if you are running on a local machine then only once. # if running on colab then every single time you reset or open the notebook. ! pip install gradio

import gradio as gr

# a wrapper function builds input and output for the model def predict_age(f1,f2,f3,f4,f5,f6,f7): input_feature = np.array([[f1,f2,f3,f4,f5,f6,f7]]) if input_feature is not None: abalone_age = abalone_model.predict(input_feature) else: pass return "The approx age of the abalone is" + str(abalone_age[[0]])

#Build inputs slider optins inputs_app = [gr.inputs.Slider(0,1, step=0.001, label='Length', default=0.545), gr.inputs.Slider(0,1, step=0.001, label='Diameter', default=0.13), gr.inputs.Slider(0,1, step=0.001, label='Height', default=0.12), gr.inputs.Slider(0,1, step=0.001, label='Whole weight', default=0.351), gr.inputs.Slider(0,1, step=0.001, label='Stucked weight', default=0.115), gr.inputs.Slider(0,1, step=0.001, label='Viscera weight', default=0.067), gr.inputs.Slider(0,1, step=0.001, label='Shell weight', default=0.16)] #Build output outputs_app = ["text"] #Build the interface age_predictor_app = gr.Interface(fn=predict_age, inputs=inputs_app, outputs=outputs_app, live=True, theme="dark-peach", description="Enter parameters using sliders provided to predict the age of abalone." )

#Launch the app age_predictor_app.launch(share=True)