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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model1.keras filter=lfs diff=lfs merge=lfs -text
+model3.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,55 @@
+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)

model1.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c646d8f0b0e9dedf7196b506cfbba5604809c7ff56394ca3a37b4e5d16b76e71
+size 82544785

model3.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db1913e473cee2b629c10fa35475e39868da97fba0c07c6db0a687d011fd036e
+size 209843323

requirements.txt

@@ -0,0 +1,5 @@
+numpy
+keras
+pandas
+tensorflow
+matplotlib