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Upload 3 files
Browse files- Requirements.txt +6 -0
- app.py +55 -0
- mymodel2.h5 +3 -0
Requirements.txt
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streamlit
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tensorflow
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keras
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opencv-python
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numpy
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pillow
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app.py
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import streamlit as st
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from PIL import Image
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import numpy as np
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from keras.models import load_model
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import tensorflow as tf
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@st.cache_resource
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def load_model():
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model=tf.keras.models.load_model('mymodel2.h5')
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return model
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with st.spinner('Model is being loaded..'):
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model=load_model()
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st.write("""
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# AI Image Classification
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"""
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)
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with open('style.css') as f:
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st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
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file = st.file_uploader('Please upload an image', type=["jpg", "png", "jpeg", "webm"],)
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import cv2
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from PIL import Image, ImageOps
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from tensorflow.keras.models import load_model
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from tensorflow.keras.preprocessing import image
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import numpy as np
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st.set_option('deprecation.showfileUploaderEncoding', False)
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def import_and_predict(image_data, model):
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size = (224,224)
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image = ImageOps.fit(image_data, size)
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img = np.asarray(image)
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img=img/255
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img=np.expand_dims(img,[0])
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prediction = model.predict(img)
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return prediction
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if file is None:
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st.text('Please upload an image file')
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else:
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image = Image.open(file)
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image = image.convert("RGB")
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st.image(image, use_column_width=True)
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try:
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predictions = import_and_predict(image, model)
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score = tf.nn.softmax(predictions[0])
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predictions = np.argmax(predictions, axis = 1)
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if(predictions == 0):
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st.write('<p class = "prediction">The image is most likely an AI Generated Image</p>', unsafe_allow_html=True)
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else:
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st.write('<p class = "prediction">The image is most likely a Real Image</p>', unsafe_allow_html=True)
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except Exception as e:
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st.write(f'An error occurred during prediction')
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mymodel2.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:fe6442e51cc85095216ff82f56943142876e898904b28dded0320b0253aa924f
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size 59541528
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