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Cats_vs_Dogs.model/fingerprint.pb

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+size 56

Cats_vs_Dogs.model/keras_metadata.pb

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+oid sha256:a5a9283a185b338044e3dcdb2313de9bef6b609d8be8578a8a7576a5b6fb9bd9
+size 23939

Cats_vs_Dogs.model/saved_model.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:184a97c4d95983961884d94fd34261bc41c5dc331e96dd9f1e7a37080a4b423f
+size 201981

Cats_vs_Dogs.model/variables/variables.data-00000-of-00001

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+oid sha256:7a54e9eeeb79d4e0830e2f69be5ed40f1f35246204febefaddde6f449e6fcbfc
+size 2237712

app.py

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+from flask import Flask, request, render_template, jsonify
+from flask_cors import CORS
+import keras
+import numpy as np
+from keras.preprocessing import image
+import io
+
+app = Flask(__name__)
+CORS(app)
+
+model = keras.models.load_model('Cats_vs_Dogs.model')
+
+@app.route('/')
+def index():
+    return render_template('index.html', prediction=None)
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    imagefile = request.files['imagefile']
+
+    # Read the image file into memory
+    img_stream = imagefile.read()
+
+    # Convert the image to grayscale and resize
+    img = image.load_img(io.BytesIO(img_stream), color_mode='grayscale', target_size=(60, 60))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0
+
+    prediction = model.predict(img_array)
+    predicted_class = "Dog" if prediction[0][1] > prediction[0][0] else "Cat"
+
+    return jsonify({'prediction': predicted_class})
+
+if __name__ == '__main__':
+    app.run(debug=True)