added files

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+datasets/lfw.tar filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,90 @@
+import io
+import tarfile
+import numpy as np
+import random as RAND
+import torch
+import torchvision.transforms as TRNSFM
+import torchvision.models as MDLS
+from PIL import Image as IMG
+from scipy.spatial.distance import cosine
+import streamlit as st
+
+def similar(image): # Function for Streamlit App
+    pict = form(IMG.open(image).convert('RGB'))
+    pictFeats = mod(pict.unsqueeze(0)).detach().numpy().squeeze()
+
+    for na, feat in feats.items():
+        s = 1 - cosine(pictFeats, feat)
+        simScores.append((na, s))
+
+    simScores.sort(key=lambda x: x[1], reverse=True)
+
+    st.write("### Selected Image")
+    test = IMG.open(image)
+    test.show()
+    print('\n')
+
+    # 10 Most Similar Images from Dictionary
+    st.write("### 10 Most Similar Images")
+    for na in simScores[:10]:
+        for x in range(10):
+            st.write(f"### {x}")
+
+        new_na = na[:3] + "images/" + na[3:]
+        new_path = "http://vis-www.cs.umass.edu/" + new_na
+        simImages = IMG.open(new_path)
+        simImages.show()
+
+mod = MDLS.resnet50(pretrained=True)
+mod.eval()
+mod = torch.nn.Sequential(*list(mod.children())[:-1])
+inFile = tarfile.open('/datasets/lfw.tar', 'r')
+
+feats = {}
+simScores = [] # Similarity Scores for Later
+
+form = TRNSFM.Compose([
+    TRNSFM.Resize(256),
+    TRNSFM.CenterCrop(224),
+    TRNSFM.ToTensor(),
+    TRNSFM.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])
+]) # Image Pre-processing
+
+stuffs = inFile.getmembers()
+
+for stuff in stuffs: # Going through the TAR file
+    f = inFile.extractfile(stuff)
+
+    if stuff.isdir():
+        continue
+
+    if stuff.name.lower().endswith(('.jpg', '.jpeg', '.png')): 
+        n = stuff.name
+        pic = form(IMG.open(io.BytesIO(f.read())).convert('RGB')) # Pre-processes the image before feeding it into the model
+
+        feats[n] = mod(pic.unsqueeze(0)).detach().numpy().squeeze()
+
+# Stuff for App
+st.title("Similar Image Finder")
+
+upload = st.file_uploader("Upload an Image...", type=['.jpg', '.jpeg', '.png'])
+
+if upload is not None:
+    similar(upload)
+
+st.write("## OR")
+
+# Random Image Selector from 5 Pictures
+randImages = [
+    '/datasets/random-images/img1.jpg',
+    '/datasets/random-images/img2.jpg',
+    '/datasets/random-images/img3.jpg',
+    '/datasets/random-images/img4.jpg',
+    '/datasets/random-images/img5.jpg'
+]
+
+if st.button("Surprise Me!"): # Button
+    imageOptOne = RAND.choice(randImages)
+    similar(imageOptOne)
+        
+inFile.close()

datasets/lfw.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e7dd83504865735bc0c48fef4f803a337136fcf5ce14cbf6b32e6287ed95531
+size 201461760

requirements.txt

@@ -0,0 +1,8 @@
+io
+tarfile
+numpy
+random
+torch
+torchvision
+PIL
+scipy