app files added

.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
+*.psd filter=lfs diff=lfs merge=lfs -text
+convnet_from_scratch_with_augmentation.keras filter=lfs diff=lfs merge=lfs -text

app.py.py

@@ -0,0 +1,70 @@
+# -*- coding: utf-8 -*-
+"""gradio_deploy.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/13X2E9v7GxryXyT39R5CzxrNwxfA6KMFJ
+"""
+
+!pip install gradio
+
+import gradio as gr
+from PIL import Image
+from timeit import default_timer as timer
+from tensorflow import keras
+import numpy as np
+
+MODEL = keras.models.load_model(
+            "convnet_from_scratch_with_augmentation.keras")
+
+def predict(img):
+
+    # Start the timer
+    start_time = timer()
+
+    # Reading the image and size transformation
+    features = Image.open(img)
+    features = features.resize((180, 180))
+    features = np.array(features).reshape(1, 180,180,3)
+
+    # Create a prediction label and prediction probability dictionary for each prediction class
+    # This is the required format for Gradio's output parameter
+    pred_labels_and_probs = {'dog' if MODEL.predict(features)> 0.5 else 'cat':float(MODEL.predict(features))}
+
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+
+    # Return the prediction dictionary and prediction time
+    return pred_labels_and_probs, pred_time
+
+predict('/content/cat.1505.jpg')
+
+# Create title, description and article strings
+title = "Classification Demo"
+description = "Cat/Dog classification Tensorflow model with Augmentted small dataset"
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type='filepath'), # what are the inputs?
+                    outputs=[gr.Label(label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                    title=title,
+                    description=description,)
+
+# Launch the demo!
+demo.launch(debug=False, # print errors locally?
+            share=True) # generate a publically shareable URL?
+
+pip install tensorflow
+
+import PIL
+
+import tensorflow as tf
+
+import timeit
+
+print(gr.__version__)
+print(np.__version__)
+print(tf.__version__)
+print(PIL.__version__)

convnet_from_scratch_with_augmentation.keras

@@ -0,0 +1,3 @@
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