run.py

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')