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requirements.txt

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+blinker==1.7.0
+click==8.1.7
+contourpy==1.2.1
+cycler==0.12.1
+Flask==3.0.3
+fonttools==4.51.0
+imageio==2.34.0
+itsdangerous==2.1.2
+Jinja2==3.1.3
+joblib==1.4.0
+kiwisolver==1.4.5
+lazy_loader==0.4
+MarkupSafe==2.1.5
+matplotlib==3.8.4
+networkx==3.3
+numpy==1.26.4
+packaging==24.0
+pandas==2.2.1
+pillow==10.3.0
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+pytz==2024.1
+scikit-image==0.22.0
+scikit-learn==1.4.1.post1
+scipy==1.13.0
+six==1.16.0
+threadpoolctl==3.4.0
+tifffile==2024.2.12
+tzdata==2024.1
+Werkzeug==3.0.2

run.py

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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')

svm_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5484efe9e12996dd67e4933ff05ee8221821b4ad1c9059b03eb2fe11b42dea6e
+size 4806131