app.py

import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow as tf
import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

NN = keras.models.load_model('/content/drive/MyDrive/Final_Project/model1.keras')
CNN = keras.models.load_model('/content/drive/MyDrive/Final_Project/model3.keras')

# Specify image module
input_module1 = gr.File(label = "Input Image File (Must be 128x128 Greyscale)")

# Specify method dropdown menu
input_module2 = gr.Dropdown(choices=['Convolutional Neural Network', 'Neural Network'], label = "Method")

# Specify output 1
output_module1 = gr.Plot(label = "Predicted Box")

def multi_inputs(input1, input2):
    import numpy as np
    ## processing inputs
    #input1= input1[:,:,0]
    photo = plt.imread(input1)
    Dcells = 128*128
    if input2 == "Neural Network":
      flattened = photo.reshape(1,Dcells)
      predicted_box = NN.predict((flattened))
      box_x = predicted_box[0,0]
      box_y = predicted_box[0,1]
      box_width = predicted_box[0,2]
      box_height = predicted_box[0,3]
      plt.imshow(photo, cmap='gray')
      plt.gca().add_patch(matplotlib.patches.Rectangle((box_x, box_y), box_width, box_height, ec='r', fc='none'))
      plt.gca().annotate("NN", xy = (0,0), xytext = (box_x, box_y+box_height+4), color='r',fontsize = 8)
    else:
      shaped = photo.reshape(1,128,128)
      predicted_box = CNN.predict(shaped)
      box_x = predicted_box[0,0]
      box_y = predicted_box[0,1]
      box_width = predicted_box[0,2]
      box_height = predicted_box[0,3]
      plt.imshow(photo, cmap='gray')
      plt.gca().add_patch(matplotlib.patches.Rectangle((box_x, box_y), box_width, box_height, ec='r', fc='none'))
      plt.gca().annotate("CNN", xy = (0,0), xytext = (box_x, box_y+box_height+4), color='r',fontsize = 8)
    return plt

gr.Interface(fn=multi_inputs,
             inputs=[input_module1, input_module2],
             outputs=[output_module1]
            ).launch(debug = True)