Upload app.py

app.py

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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","CNN"], 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)
+
+    if input1 == "CNN":
+      loaded_model = pickle.load(open('CNN.sav', 'rb'))
+      img = img.reshape((1, -1))
+      y_test_pred = loaded_model.predict(img)
+      y_test_pred = np.argmax(y_test_pred,axis=1)
+    
+    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)