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from gradio.outputs import Label | |
import gradio as gr | |
import cv2 | |
import tensorflow as tf | |
def caption(image,input_module1): | |
class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", | |
"Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] | |
image=image.reshape(1,28*28) | |
if input_module1=="KNN": | |
output1=KNN_classifier.predict(image)[0] | |
predictions=KNN_classifier.predict_proba(image)[0] | |
elif input_module1==("Linear discriminant analysis"): | |
output1=clf.predict(image)[0] | |
predictions=clf.predict_proba(image)[0] | |
elif input_module1==("Quadratic discriminant analysis"): | |
output1=qda.predict(image)[0] | |
predictions=qda.predict_proba(image)[0] | |
elif input_module1=="Naive Bayes classifier": | |
output1=gnb.predict(image)[0] | |
predictions=gnb.predict_proba(image)[0] | |
output2 = {} | |
for i in range(len(predictions)): | |
output2[class_names[i]] = predictions[i] | |
return output1 ,output2 | |
input_module = gr.inputs.Image(label = "Input Image",image_mode="L",shape=(28,28)) | |
input_module1 = gr.inputs.Dropdown(choices=["KNN","Linear discriminant analysis", "Quadratic discriminant analysis","Naive Bayes classifier"], label = "Method") | |
output1 = gr.outputs.Textbox(label = "Predicted Class") | |
output2=gr.outputs.Label(label= "probability of class") | |
gr.Interface(fn=caption, inputs=[input_module,input_module1], outputs=[output1,output2]).launch(debug=True) | |