Upload app.py

app.py

@@ -0,0 +1,37 @@
+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)