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Browse files- .gitattributes +1 -0
- README.md +4 -4
- app.py +54 -0
- convnet_from_scratch_with_augmentation.keras +3 -0
- examples/cat.1502.jpg +0 -0
- examples/cat.1515.jpg +0 -0
- examples/dog.1508.jpg +0 -0
- examples/dog.1557.jpg +0 -0
- requirements.txt +3 -0
.gitattributes
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convnet_from_scratch_with_augmentation.keras filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: Image Classification
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colorFrom:
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sdk: gradio
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sdk_version: 3.
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app_file: app.py
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pinned: false
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license: mit
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---
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title: Image Classification
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emoji: π
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colorFrom: yellow
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colorTo: yellow
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sdk: gradio
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sdk_version: 3.41.2
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app_file: app.py
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pinned: false
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license: mit
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app.py
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# -*- coding: utf-8 -*-
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"""gradio_deploy.ipynb
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Automatically generated by Colaboratory.
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Original file is located at
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https://colab.research.google.com/drive/13X2E9v7GxryXyT39R5CzxrNwxfA6KMFJ
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"""
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import os
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import gradio as gr
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from PIL import Image
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from timeit import default_timer as timer
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from tensorflow import keras
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import numpy as np
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MODEL = keras.models.load_model("convnet_from_scratch_with_augmentation.keras")
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def predict(img):
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# Start the timer
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start_time = timer()
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# Reading the image and size transformation
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features = Image.open(img)
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features = features.resize((180, 180))
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features = np.array(features).reshape(1, 180,180,3)
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# Create a prediction label and prediction probability dictionary for each prediction class
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# This is the required format for Gradio's output parameter
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pred_labels_and_probs = {'dog':float(MODEL.predict(features))} if MODEL.predict(features)> 0.5 else {'cat':100-float(MODEL.predict(features))}
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# Calculate the prediction time
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pred_time = round(timer() - start_time, 5)
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# Return the prediction dictionary and prediction time
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return pred_labels_and_probs, pred_time
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# Create title, description and article strings
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example_list = [["examples/" + example] for example in os.listdir("examples")]
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title = "Classification Demo"
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description = "Cat/Dog classification Tensorflow model with Augmented small dataset"
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# Create the Gradio demo
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demo = gr.Interface(fn=predict, # mapping function from input to output
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inputs=gr.Image(type='filepath'), # what are the inputs?
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outputs=[gr.Label(label="Predictions"), # what are the outputs?
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gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
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examples=example_list,
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title=title,
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description=description,)
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# Launch the demo!
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demo.launch()
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convnet_from_scratch_with_augmentation.keras
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version https://git-lfs.github.com/spec/v1
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oid sha256:1646b4c5ba760dfb7f265dbfdaf7d37ef8743c6a44a5a77fe43aa22cd398434b
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size 7982872
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examples/cat.1502.jpg
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examples/cat.1515.jpg
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examples/dog.1508.jpg
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examples/dog.1557.jpg
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requirements.txt
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tensorflow==2.12.0
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numpy==1.23.5
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gradio==3.1.4
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