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# Step 6.1: Define different input components
import gradio as gr
# g. define Dropdown data type
input_module1 = gr.inputs.Dropdown(choices=["KNN", "Softmax", "Deep Neural", "SVM", "Decision Tree","Random Forest"], label = "Model Selection")
# b. define image data type
input_module2 = gr.inputs.Dropdown(choices=["image1", "image2", "image3", "image4","image5"], label = "Sample Image")
# b. define image data type
output_module1 = gr.outputs.Textbox(label = "Predicted Class")
# b. define image data type
output_module2 = gr.outputs.Image(label = "Image")
# Step 6.3: Define a new function that accommodates the input modules.
def multi_inputs(input1, input2):
import numpy as np
import pickle
import matplotlib as mpl
import matplotlib.pyplot as plt
import tensorflow as tf
import scipy.ndimage.interpolation
(X_train_full, y_train_full), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
X_valid, X_train = X_train_full[:5000], X_train_full[5000:]
y_valid, y_train = y_train_full[:5000], y_train_full[5000:]
if input2 == "image1":
img = X_test[1]
if input2 == "image2":
img = X_test[2]
if input2 == "image3":
img = X_test[3]
if input2 == "image4":
img = X_test[4]
if input2 == "image5":
img = X_test[5]
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
#img = mpimg.imread(img)
#imag = scipy.ndimage.rotate(input2, 0, reshape=False, cval=0)
#class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
#"Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]
test_shape = img.shape
test_width = test_shape[0]
test_height = test_shape[1]
image =img.reshape(test_width*test_height)
if input1 == "KNN":
loaded_model = pickle.load(open('KNN_Model.sav', 'rb'))
image = img.reshape((1, -1))
y_test_pred = loaded_model.predict(image)
if input1 == "Softmax":
loaded_model = pickle.load(open('Softmax_Model.sav', 'rb'))
image = img.reshape((1, -1))
y_test_pred = loaded_model.predict(image)
if input1 == "SVM":
loaded_model = pickle.load(open('svm_model.sav', 'rb'))
image = img.reshape((1, -1))
y_test_pred = loaded_model.predict(image)
if input1 == "Deep Neural":
loaded_model = pickle.load(open('dnn_model.sav', 'rb'))
image = img.reshape((1, -1))
y_test_pred = loaded_model.predict(image)
y_test_pred = np.argmax(y_test_pred,axis=1)
if input1 == "Decision Tree":
loaded_model = pickle.load(open('Decision_Tree_model.sav', 'rb'))
image = img.reshape((1, -1))
y_test_pred = loaded_model.predict(image)
if input1 == "Random Forest":
loaded_model = pickle.load(open('randomforest.sav', 'rb'))
image = img.reshape((1, -1))
y_test_pred = loaded_model.predict(image)
result = y_test_pred[0]
return result, img
# Step 6.4: Put all three component together into the gradio's interface function
gr.Interface(fn=multi_inputs,
inputs=[input_module1, input_module2],
outputs=[output_module1,output_module2]
).launch( debug = True)
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