Create app.py

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 Bluriness Prediction")
+
+# 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","test3.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("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)