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Browse files- Cats_vs_Dogs.model/fingerprint.pb +3 -0
- Cats_vs_Dogs.model/keras_metadata.pb +3 -0
- Cats_vs_Dogs.model/saved_model.pb +3 -0
- Cats_vs_Dogs.model/variables/variables.data-00000-of-00001 +3 -0
- Cats_vs_Dogs.model/variables/variables.index +0 -0
- app.py +38 -0
- index.html +20 -0
- main.js +34 -0
- requirements.txt +0 -0
Cats_vs_Dogs.model/fingerprint.pb
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version https://git-lfs.github.com/spec/v1
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oid sha256:134787d145d753dacf4ca852746b711e7f442c2a8956ba84dd83de85e14a2f60
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size 56
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Cats_vs_Dogs.model/keras_metadata.pb
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version https://git-lfs.github.com/spec/v1
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oid sha256:a5a9283a185b338044e3dcdb2313de9bef6b609d8be8578a8a7576a5b6fb9bd9
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size 23939
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Cats_vs_Dogs.model/saved_model.pb
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version https://git-lfs.github.com/spec/v1
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oid sha256:184a97c4d95983961884d94fd34261bc41c5dc331e96dd9f1e7a37080a4b423f
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size 201981
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Cats_vs_Dogs.model/variables/variables.data-00000-of-00001
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version https://git-lfs.github.com/spec/v1
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oid sha256:7a54e9eeeb79d4e0830e2f69be5ed40f1f35246204febefaddde6f449e6fcbfc
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size 2237712
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Cats_vs_Dogs.model/variables/variables.index
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Binary file (3 kB). View file
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app.py
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from flask import Flask, request, render_template, jsonify
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from flask_cors import CORS
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import keras
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import numpy as np
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from keras.preprocessing import image
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import io
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app = Flask(__name__)
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CORS(app)
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model = keras.models.load_model('Cats_vs_Dogs.model')
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@app.route('/')
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def index():
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with open('index.html', 'r') as file:
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html_content = file.read()
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return html_content
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@app.route('/predict', methods=['POST'])
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def predict():
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imagefile = request.files['imagefile']
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# Read the image file into memory
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img_stream = imagefile.read()
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# Convert the image to grayscale and resize
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img = image.load_img(io.BytesIO(img_stream), color_mode='grayscale', target_size=(60, 60))
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img_array = image.img_to_array(img)
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img_array = np.expand_dims(img_array, axis=0)
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img_array /= 255.0
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prediction = model.predict(img_array)
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predicted_class = "Dog" if prediction[0][1] > prediction[0][0] else "Cat"
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return jsonify({'prediction': predicted_class})
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if __name__ == '__main__':
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app.run(debug=True)
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index.html
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<!DOCTYPE html>
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<html>
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<head>
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<title>Image Classifier</title>
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<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta2/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BmbxuPwQa2lc/FVzBcNJ7UAyJxM6wuqIj61tLrc4wSX0szH/Ev+nYRRuWlolflfl" crossorigin="anonymous">
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</head>
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<body>
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<h1 class="text-center">Image Classifier</h1>
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<form class="p-3 text-center" enctype="multipart/form-data">
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<input class="form-control" type="file" id="imageFile" accept="image/*">
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<button class="btn btn-primary mt-3" type="button" id="predictButton">Predict Image</button>
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</form>
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<p class="text-center mt-3" id="predictionResult"></p>
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<script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
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<script src="main.js"></script>
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</body>
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</html>
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main.js
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$(document).ready(function() {
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var imageFileInput = document.getElementById('imageFile');
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var predictButton = document.getElementById('predictButton');
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var predictionResult = document.getElementById('predictionResult');
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predictButton.addEventListener('click', async function() {
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var file = imageFileInput.files[0];
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if (!file) {
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predictionResult.textContent = 'Please select an image.';
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return;
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}
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var formData = new FormData();
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formData.append('imagefile', file);
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try {
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var response = await fetch('http://127.0.0.1:5000/predict', {
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method: 'POST',
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body: formData
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});
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if (response.ok) {
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var prediction = await response.text();
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predictionResult.textContent = 'Prediction: ' + prediction;
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} else {
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var errorMessage = await response.text();
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predictionResult.textContent = 'Error: ' + errorMessage;
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}
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} catch (error) {
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predictionResult.textContent = 'An error occurred.';
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console.error('Error:', error);
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}
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});
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});
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requirements.txt
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Binary file (140 Bytes). View file
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