import gradio as gr

def caption(image,input_module1):
  instances_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
  "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] 
  image=image.reshape(1,28*28)
  if input_module1=="KNN":
    KNN_classifier = KNeighborsClassifier(n_neighbors=5, metric = 'euclidean')
    output1=KNN_classifier.predict(image)[0]
    predictions=KNN_classifier.predict_proba(image)[0]
    
  elif input_module1==("Linear discriminant analysis"):
    clf = LinearDiscriminantAnalysis()
    output1=clf.predict(image)[0]
    predictions=clf.predict_proba(image)[0]
    
  elif input_module1==("Quadratic discriminant analysis"):
    qda = QuadraticDiscriminantAnalysis()
    output1=qda.predict(image)[0]
    predictions=qda.predict_proba(image)[0]
    
  elif input_module1=="Naive Bayes classifier":
    gnb = GaussianNB()
    output1=gnb.predict(image)[0]
    predictions=gnb.predict_proba(image)[0]
  
  output2 = {}

  for i in range(len(predictions)):
    output2[instances_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)