|
|
import os
|
|
|
import numpy as np
|
|
|
import tensorflow as tf
|
|
|
from flask import Flask, request, render_template, jsonify
|
|
|
from tensorflow.keras.utils import load_img, img_to_array
|
|
|
from werkzeug.utils import secure_filename
|
|
|
from datetime import datetime
|
|
|
|
|
|
app = Flask(__name__)
|
|
|
|
|
|
|
|
|
MODEL_PATH = r"model.keras"
|
|
|
model = tf.keras.models.load_model(MODEL_PATH)
|
|
|
|
|
|
|
|
|
UPLOAD_FOLDER = os.path.join('static', 'uploads')
|
|
|
ALLOWED_EXTENSIONS = {'jpg', 'jpeg', 'png'}
|
|
|
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
|
|
|
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
|
|
|
|
|
|
def allowed_file(filename):
|
|
|
return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
|
|
|
|
|
|
def preprocess_image(image_path):
|
|
|
img = load_img(image_path, target_size=(224, 224))
|
|
|
img_array = img_to_array(img) / 255.0
|
|
|
return np.expand_dims(img_array, axis=0)
|
|
|
|
|
|
@app.route('/')
|
|
|
def index():
|
|
|
return render_template('home.html')
|
|
|
|
|
|
@app.route('/tool')
|
|
|
def tool():
|
|
|
return render_template('tool.html')
|
|
|
|
|
|
@app.route('/about')
|
|
|
def about():
|
|
|
return render_template('about.html')
|
|
|
|
|
|
@app.route('/contact')
|
|
|
def contact():
|
|
|
return render_template('contact.html')
|
|
|
|
|
|
@app.route('/predict', methods=['POST'])
|
|
|
def predict():
|
|
|
if 'file' not in request.files:
|
|
|
return jsonify({'error': 'No files uploaded'}), 400
|
|
|
|
|
|
files = request.files.getlist('file')
|
|
|
if not files or all(f.filename == '' for f in files):
|
|
|
return jsonify({'error': 'No files selected'}), 400
|
|
|
|
|
|
results = []
|
|
|
for file in files:
|
|
|
if file and allowed_file(file.filename):
|
|
|
filename = secure_filename(file.filename)
|
|
|
timestamp = datetime.now().strftime("%Y%m%d%H%M%S%f")
|
|
|
unique_filename = f"{timestamp}_{filename}"
|
|
|
file_path = os.path.join(app.config['UPLOAD_FOLDER'], unique_filename)
|
|
|
file.save(file_path)
|
|
|
|
|
|
try:
|
|
|
img_array = preprocess_image(file_path)
|
|
|
prediction = model.predict(img_array)[0][0]
|
|
|
label = "Dirty" if prediction > 0.5 else "Clean"
|
|
|
confidence = prediction if label == "Dirty" else 1 - prediction
|
|
|
|
|
|
results.append({
|
|
|
'label': label,
|
|
|
'confidence': f"{confidence:.2%}",
|
|
|
'image_url': f"/static/uploads/{unique_filename}"
|
|
|
})
|
|
|
except Exception as e:
|
|
|
results.append({
|
|
|
'label': 'Error',
|
|
|
'confidence': 'N/A',
|
|
|
'image_url': None,
|
|
|
'error': str(e)
|
|
|
})
|
|
|
else:
|
|
|
results.append({
|
|
|
'label': 'Error',
|
|
|
'confidence': 'N/A',
|
|
|
'image_url': None,
|
|
|
'error': f"Invalid file type: {file.filename}"
|
|
|
})
|
|
|
|
|
|
|
|
|
return render_template('results.html', results=results)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
app.run(debug=True)
|
|
|
|
