app.py

from flask import Flask, render_template, request, jsonify, session
import os
import numpy as np
from werkzeug.utils import secure_filename
import pickle
import tensorflow as tf
from tensorflow.keras.applications import VGG16
from tensorflow.keras.preprocessing import image
from sklearn.cluster import KMeans

def preprocess(img_path):
    img = image.load_img(img_path, target_size=(224, 224))
    img_data = image.img_to_array(img)
    img_data = np.expand_dims(img_data, axis=0)
    img_data = tf.keras.applications.vgg16.preprocess_input(img_data)
    features = base_model.predict(img_data)
    return features.flatten()

def predict():
    try:
        with open("model/kmeans_model.pkl", "rb") as file:
            kmeans = pickle.load(file)
            cluster = kmeans.predict([preprocess(session["curfile"])])
            print("Class :",cluster[0])
            return cluster[0]
    except Exception as e:
        return str(e)

app = Flask(__name__)
app.secret_key = "sgdgwjkdbksjdgbkajbf"
UPLOAD_FOLDER = 'static/uploads'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/upload',methods=['POST'])
def upload():
    if 'file' not in request.files:
        return jsonify({'error': 'No file part'})
    file = request.files['file']
    if file.filename == '':
        return jsonify({'error': 'No selected file'})

    filename = secure_filename(file.filename)
    filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
    file.save(filepath)
    session["curfile"] = filepath

    return jsonify({"img_url": filepath})

@app.route('/detect', methods=['POST'])
def detect():
    pc = predict()
    if (pc == 0):
        output = "High Light Polution detected"
    elif(pc == 1):
        output = "High to Moderate Light Polution detected"
    elif(pc == 2):
        output = "Moderate to Low Light Polution detected"
    else:
        return jsonify({"error": pc})
    return jsonify({'message': output})

if __name__ == '__main__':
    print("Starting......")
    gpus = tf.config.experimental.list_physical_devices('GPU')
    if gpus:
        try:
            for gpu in gpus:
                tf.config.experimental.set_memory_growth(gpu, True)
            print("GPU memory growth enabled.")
        except RuntimeError as e:
            print(e)
    base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
    print("Loaded VGG16....")
    app.run(debug=True)
    print("Shutting down.")