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from flask import Flask, request, jsonify
import os
from skimage.transform import resize
from skimage.io import imread
import numpy as np
from sklearn import svm
from sklearn.model_selection import GridSearchCV
import joblib
app = Flask(__name__)
# Load the trained model
model = joblib.load('svm_nidek.pkl')
Categories = ['cats', 'dogs']
@app.route('/classify_image', methods=['POST'])
def classify_image():
# Receive the image file from the request
image_file = request.files['image']
# Save the image to a temporary location
temp_path = 'temp.jpg'
image_file.save(temp_path)
# Load and preprocess the image
img_array = imread(temp_path)
img_resized = resize(img_array, (50, 50, 3))
img_flattened = img_resized.flatten()
img_flattened = np.expand_dims(img_flattened, axis=0)
# Predict the class probabilities
probabilities = model.predict_proba(img_flattened)[0]
# Get the predicted class
predicted_class = Categories[np.argmax(probabilities)]
# Get the probability of the predicted class
confidence = probabilities[np.argmax(probabilities)]
# Delete the temporary image file
os.remove(temp_path)
# Return the result to the Flutter application
return jsonify({'predicted_class': predicted_class, 'confidence': confidence})
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0')
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