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| ## Imported Modules ## | |
| import streamlit as st | |
| from tensorflow import keras | |
| from keras.models import load_model | |
| from keras.utils import load_img, img_to_array | |
| import numpy as np | |
| import cv2 | |
| from PIL import Image, ImageOps | |
| import matplotlib.pyplot as plt | |
| from matplotlib.image import imread | |
| ## Functions ## | |
| def load_VGG(): | |
| model= load_model('model.h5') | |
| return model | |
| def load_Inception(): | |
| model= load_model('inception_v3_model.h5') | |
| return model | |
| def import_and_predict(image_data, model): | |
| size = (224,224) | |
| image = ImageOps.fit(image_data, size, Image.ANTIALIAS) | |
| image = np.asarray(image) | |
| img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| img_reshape = img[np.newaxis,...] | |
| prediction = model.predict(img_reshape) | |
| if prediction[0]>=0.5: | |
| final_result= 'Real' | |
| else: | |
| final_result= 'AI Generated' | |
| return final_result,prediction[0] | |
| ## Layout## | |
| rad = st.sidebar.radio("Navigation",["Home","Predict AI/Real"]) | |
| if rad=="Home": | |
| st.title("AI_Image_Classifier") | |
| st.subheader("By Hardik Pahwa") | |
| st.markdown("""Differentiating between AI-generated content and real content is crucial in today's digital era.<br> As AI algorithms and deepfake technology advance, it becomes increasingly difficult to discern authenticity.<br> This ability is essential for preserving information integrity, combating misinformation, and maintaining trust in various fields.<br> Reliable tools and techniques are necessary to identify AI-generated content, ensuring transparency and protecting against manipulation.""",True) | |
| st.markdown("Infact the text above and image below was also generated using AI :wink:") | |
| st.image("sample.jpg") | |
| elif rad=="Predict AI/Real": | |
| st.write(""" | |
| # Image Classification | |
| """) | |
| v1 = st.radio("Choose Model",["VGG16","Inceptionv3"],index=0) | |
| with st.spinner("Loading Model..."): | |
| if v1=="VGG16": | |
| model = load_VGG() | |
| else: | |
| model = load_Inception() | |
| file = st.file_uploader("Upload an Image") | |
| st.write(file) | |
| st.set_option('deprecation.showfileUploaderEncoding', False) | |
| if file != None: | |
| image = Image.open(file) | |
| st.image(file,use_column_width=True) | |
| final_result= import_and_predict(image,model)[0] | |
| result = import_and_predict(image,model)[1] | |
| st.write("The image is :") | |
| st.write(final_result) | |
| st.write(result) | |
| else: | |
| st.write("No file uploaded") | |