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Image-Blur-Prediction / app.py

Priyanka-Kumavat-At-TE

Update app.py

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	import warnings
	warnings.filterwarnings("ignore", category=UserWarning)
	import streamlit as st
	import os

	from PIL import Image
	import tempfile
	from tempfile import NamedTemporaryFile
	from io import BytesIO

	import pickle
	import cv2
	import numpy as np
	from sklearn.ensemble import RandomForestClassifier

	st.title("Image Blur Prediction System")

	st.write("""Image Bluriness Prediction Model allows users to analyze the bluriness of images.
	It utilizes a pre-trained random forest classifier model to predict whether an image is blurry or not.
	The application provides two options for image selection:
	users can either upload their own image or choose from a set of sample images.
	Once an image is selected, the application calculates the Variance of Laplacian (VoL) score,
	a metric used to measure image bluriness. The classifier model then predicts whether the image is blurry or not based
	on the VoL score. The prediction result and the VoL score are displayed to the user.
	The application also includes a sidebar that showcases sample images for quick testing.""")

	# Load the saved random forest classifier model
	with open('image_blur_model.pkl', 'rb') as f:
	clf = pickle.load(f)

	# For sample images as a sidebar
	images = ["test2.jpg","test1.jpg","test4.jpg","test5.jpg","test6.jpg","download1.jpg","download2.jpg","sample1.jpg",
	"download3.jpg","download4.jpg","download.png","img1.jpg","img17.jpg"]
	with st.sidebar:
	st.write("Choose an image")
	st.image(images)


	# Function to predict bluriness
	def predict_bluriness(image):
	# Convert the image to grayscale and compute the VoL metric
	gray = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY)
	vol = cv2.Laplacian(gray, cv2.CV_64F).var()

	# Make a prediction using the loaded model
	prediction = clf.predict([[vol]])

	# Return the prediction result and VoL value
	return prediction, vol


	# CSS code for changing color of the button
	st.markdown("""
	<style>
	.stButton button {
	background-color: #668f45;
	color: white;
	}
	</style>
	""", unsafe_allow_html=True)

	# File uploader
	uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

	# Predict button
	if st.button("Click to Predict"):
	image = None

	# Read the uploaded image if available
	if uploaded_file is not None:
	image = Image.open(uploaded_file)
	st.image(image, caption="Uploaded Image", use_column_width=True)

	# Perform prediction if image is available
	if image is not None:
	# Perform prediction
	prediction, vol = predict_bluriness(image)

	# Display prediction result and VoL value
	st.write("Prediction:", "The image is not blurry." if prediction == 1 else "The image is blurry.")
	st.write("Variance of Laplacian Score:", vol)


	st.write("""
	For the detailed Description of the Model, Project and Technologies used, please go through our Documentation
	""")

	url = 'https://huggingface.co/spaces/ThirdEyeData/Image-Blur-Prediction/blob/main/Documentation.md'

	st.markdown(f'''
	<a href={url}><button style="background-color: #668f45;">Click to Read Model's Documentation</button></a>
	''',
	unsafe_allow_html=True)