Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from tensorflow.keras.applications.resnet50 import ResNet50
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from sklearn.neighbors import NearestNeighbors
+from PIL import Image
+import numpy as np
+import glob
+import os
+
+resnet_model = ResNet50(weights='imagenet')
+
+st.title("CS634 - Assignment 3")
+
+user_image_input = st.file_uploader("Upload Images", type=["jpg"])
+
+
+path='lfw/V*'
+photos=[]
+for fold in glob.glob(path, recursive=True):
+    for subdir, dirs, files in os.walk(fold):
+        for file in files:
+            #st.write(file)
+            photos.append(os.path.join(subdir, file))
+            
+photos.insert(0,"")
+celebrity_photo = st.selectbox("Select Photo",photos)
+
+            
+            
+def extract_features(photos, resnet_model):
+    features = {}
+    for photo in photos:
+        if(photo!=""):
+            img = image.load_img(photo, target_size=(224, 224))
+            x = image.img_to_array(img)
+            x = np.expand_dims(x, axis=0)
+            x = preprocess_input(x)
+
+            features_vector = resnet_model.predict(x)
+            features_vector = features_vector.flatten()
+            features[photo] = features_vector
+
+    return features
+            
+
+            
+            
+if(len(celebrity_photo) != 0):
+    #st.image(user_image_input, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
+    user_input_image = None
+    st.write(celebrity_photo)
+    #st.write(user_image_input.read())
+    size=len(photos)
+    #st.write(size)
+    st.write("Query Image: ")
+    st.image(celebrity_photo)
+    features = extract_features(photos, resnet_model)
+    
+    features_array = np.array(list(features.values()))
+
+    nn_model = NearestNeighbors(n_neighbors=11, metric='euclidean')
+    nn_model.fit(features_array)
+
+    query_image_path = photos[size-1]
+    query_image_feature = features[query_image_path].reshape(1, -1)
+    distances, indices = nn_model.kneighbors(query_image_feature)
+    
+    
+    st.write("Similar Images:")
+    for i in range(1,11):
+        similar_image_path = photos[indices[0][i]]
+        similar_image_distance = distances[0][i]
+        st.write("Similar Image #{}: Distance: {}".format(i, similar_image_distance))
+        st.image(similar_image_path)
+        
+
+if(user_image_input != None):
+    celebrity_photo = []
+    #st.image(user_image_input, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
+    im = Image.open(user_image_input)
+    im=im.resize((224,224))
+    im.save("input_image.jpg", "JPEG")
+    photos.append("input_image.jpg")
+    #st.write(user_image_input.read())
+    size=len(photos)
+    #st.write(size)
+    st.write("Query Image: ")
+    st.image(photos[size-1])
+    features = extract_features(photos, resnet_model)
+    
+    features_array = np.array(list(features.values()))
+
+    nn_model = NearestNeighbors(n_neighbors=11, metric='euclidean')
+    nn_model.fit(features_array)
+
+    query_image_path = photos[size-1]
+    query_image_feature = features[query_image_path].reshape(1, -1)
+    distances, indices = nn_model.kneighbors(query_image_feature)
+    
+    
+    st.write("Similar Images:")
+    for i in range(1,11):
+        similar_image_path = photos[indices[0][i]]
+        similar_image_distance = distances[0][i]
+        st.write("Similar Image #{}: Distance: {}".format(i, similar_image_distance))
+        st.image(similar_image_path)
+#else:
+#    size=len(photos)
+#    st.write(size)
+#    st.image(photos[size-1])
+#    features = extract_features(photos, resnet_model)
+            
+            
+            
+
+