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from flask import Flask, request, jsonify, render_template, send_from_directory
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import os
app = Flask(__name__)
# Loading the trained model
try:
model = load_model('model.h5') # Replacing with the path to your saved model
except Exception as e:
print("Error loading the model:", e)
def detect_image(image_path):
try:
# Function to detect image
img = Image.open(image_path).resize((256, 256)) # Resizing image
img_array = np.array(img) / 255.0 # Normalizing pixel values
img_array = np.expand_dims(img_array, axis=0) # Adding batch dimension
prediction = model.predict(img_array)[0][0]
probability_real = prediction * 100 # Converting prediction to percentage
probability_ai = (1 - prediction) * 100
# Determine the final output
if probability_real > probability_ai:
result = 'Input Image is Real'
confidence = probability_real
else:
result = 'Input Image is AI Generated'
confidence = probability_ai
return result, confidence
except Exception as e:
print("Error detecting image:", e)
return "Error detecting image", 0
@app.route('/')
def index():
return render_template('home.html')
@app.route('/detect', methods=['POST'])
def detect():
try:
if 'file' not in request.files:
return jsonify({'error': 'No file provided'})
file = request.files['file']
if file.filename.split('.')[-1].lower() not in ['jpg', 'jpeg', 'png']:
return jsonify({'error': 'Unsupported file type. Please provide an image in JPG, JPEG, or PNG format.'})
file_path = 'DetectionImage/' + file.filename # Specifying the directory where images will be saved
file.save(file_path)
result, confidence = detect_image(file_path)
response = {
'result': result,
'confidence': confidence,
'image_path': file_path
}
return jsonify(response)
except Exception as e:
print("Error processing request:", e)
return jsonify({'error': 'Error processing request'})
@app.route('/DetectionImage/<path:filename>')
def serve_image(filename):
return send_from_directory('DetectionImage', filename)
if __name__ == '__main__':
if not os.path.exists('DetectionImage'):
os.makedirs('DetectionImage')
app.run(debug=True)
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