Image_to_text / app.py
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Update app.py
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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)