app.py

import pandas as pd
import cv2
import numpy as np
from joblib import load
import gradio as gr


#Create user inputs
input_modules = [gr.components.Image(label = "Input Image"), 
                 gr.components.Dropdown(label = "Pick a Model",
                                        choices = ["Quadratic Disciminat Analysis",
                                                   "Gaussian Naive Bayes Classifier",
                                                   "K-Nearest-Neighbors",
                                                   "Linear discriminant Analysis"])]

#Create outputs
output_modules = [gr.components.Textbox(label = "Prediction"),
                  gr.components.Label(label = "Prediction Probs")]

#Gradio function
def classifier_picker(input_img, input_model):
  #initalizes some starting vars
  class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]
  output1 = 0
  output2 = dict([(class_name, 0) for class_name in class_names])

  #Takes the chosen model and loads it
  if input_model == "Quadratic Disciminat Analysis":
    loaded_model = load('QDA_save.joblib')
      
  elif input_model == "Gaussian Naive Bayes Classifier":
    loaded_model = load('GNB_save.joblib')

  elif input_model == "Linear discriminant Analysis":
    loaded_model = load('fashionMNIST_LDA.joblib')
  else:
    loaded_model = load('KNN_fashionMNIST.joblib')

  
  #shapes the image into the input size
  reshaped_img = cv2.resize(input_img, (28,28))

  #since our model works with gray images, we need to convert the input image to gray image
  grayscale_img = cv2.cvtColor(reshaped_img, cv2.COLOR_BGR2GRAY)

  #we need to flatten the image to work with out model
  flattened_img = np.array(grayscale_img).reshape(784)
  

  #prediction of the image
  output1 = loaded_model.predict([flattened_img])
  output2 = dict([(class_name, prob) for class_name, prob in zip(class_names, loaded_model.predict_proba([flattened_img])[0])])

  return class_names[output1[0]], output2  



#Launching the module
gr.Interface(fn=classifier_picker, inputs=input_modules, outputs=output_modules,).launch()