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app.py

@@ -7,21 +7,6 @@ Original file is located at
     https://colab.research.google.com/drive/1f7VybSnYLPbUVLRLMNQboxQkCYaBCXMs
 """
 
-from google.colab import files
-
-files.upload()
-
-! mkdir ~/.kaggle
-
-! cp kaggle.json ~/.kaggle/
-
-! chmod 600 ~/.kaggle/kaggle.json
-
-! kaggle datasets list
-
-!kaggle datasets download -d andrewmvd/lung-and-colon-cancer-histopathological-images
-
-!unzip /content/lung-and-colon-cancer-histopathological-images.zip
 
 # This Python 3 environment comes with many helpful analytics libraries installed
 # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
@@ -34,9 +19,7 @@ import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
 # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
 
 import os
-for dirname, _, filenames in os.walk('/kaggle/content'):
-    for filename in filenames:
-        print(os.path.join(dirname, filename))
+
 
 # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
 # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
@@ -55,7 +38,7 @@ import numpy as np
 from glob import glob
 import matplotlib.pyplot as plt
 
-image_set =  "../content/lung_colon_image_set/lung_image_sets"
+image_set =  "./lung_image_sets"
 
 SIZE_X = SIZE_Y = 224
 
@@ -79,8 +62,6 @@ validate_set = datagen.flow_from_directory(image_set,
                                        subset='validation',
                                        seed = 42)
 
-from google.colab import drive
-drive.mount('/content/drive')
 
 IMAGE_SIZE = [224, 224]
 
@@ -142,23 +123,23 @@ import pickle
 with open('model_pkl', 'wb') as files:
     pickle.dump(model, files)
 
-img = tf.keras.utils.load_img('/content/lung_colon_image_set/lung_image_sets/lung_aca/lungaca1.jpeg', target_size=(224, 224))
-img_array = tf.keras.utils.img_to_array(img)
-img_array = tf.expand_dims(img_array, 0)
+# img = tf.keras.utils.load_img('/content/lung_colon_image_set/lung_image_sets/lung_aca/lungaca1.jpeg', target_size=(224, 224))
+# img_array = tf.keras.utils.img_to_array(img)
+# img_array = tf.expand_dims(img_array, 0)
 
-# load saved model
-with open('model_pkl' , 'rb') as f:
-    lr = pickle.load(f)
-    predi=lr.predict(img_array)
-    print(predi)
-    image_output_class=target_names[np.argmax(predi)]
+# # load saved model
+# with open('model_pkl' , 'rb') as f:
+#     lr = pickle.load(f)
+#     predi=lr.predict(img_array)
+#     print(predi)
+#     image_output_class=target_names[np.argmax(predi)]
 
-print("The predicted class is", image_output_class)
+# print("The predicted class is", image_output_class)
 
 import gradio as gd
 from PIL import Image
 
-target_names = ['aca','n', 'scc']
+# target_names = ['aca','n', 'scc']
 
 def greet_user(name):
     image=gd.inputs.Image()
@@ -166,7 +147,7 @@ def greet_user(name):
     pil_image_resized = pil_image.resize((224,224))
     img_array = tf.keras.utils.img_to_array(pil_image_resized)
     img_array = tf.expand_dims(img_array, 0)
-    with open('/content/model_pkl' , 'rb') as f:
+    with open('model_pkl' , 'rb') as f:
         lr = pickle.load(f)
         predi=lr.predict(img_array)
         image_output_class=target_names[np.argmax(predi)]