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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 ##
@st.cache(allow_output_mutation = True)
def load_VGG():
model= load_model('model.h5')
return model
@st.cache(allow_output_mutation = True)
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")
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