Add files via upload

app.py

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+import torch
+import torch.nn as nn
+import torchvision.models as models
+import torchvision.transforms as transforms
+from torch.autograd import Variable
+from PIL import Image
+from torchvision import transforms
+import os
+from glob import glob
+import json
+from json import JSONEncoder
+import numpy
+from sklearn.neighbors import NearestNeighbors
+import streamlit as st
+
+class NumpyArrayEncoder(JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, numpy.ndarray):
+            return obj.tolist()
+        return JSONEncoder.default(self, obj)
+
+with open('sample.json') as json_file:
+    data = json.load(json_file)
+
+resnet=models.resnet50(pretrained=True)
+layer = resnet._modules.get('avgpool')
+#grab all images in the lfw folder
+import os
+from glob import glob
+path="./lfw"
+
+result = [y for x in os.walk(path) for y in glob(os.path.join(x[0], '*.jpg'))]
+resnet.eval
+
+#d={}
+
+preprocess=transforms.Compose([transforms.Resize(256),
+                               transforms.CenterCrop(224),
+                               transforms.ToTensor(),
+                               transforms.Normalize(mean=[.485,.456,.406],std=[.229,.224,.225])
+                               ])
+
+def get_vector(image):
+    # Create a PyTorch tensor with the transformed image
+    t_img = preprocess(image)
+    my_embedding = torch.zeros(2048)
+
+    # Define a function that will copy the output of a layer
+    def copy_data(m, i, o):
+        my_embedding.copy_(o.flatten())                 # <-- flatten
+
+    # Attach that function to our selected layer
+    h = layer.register_forward_hook(copy_data)
+    # Run the model on our transformed image
+    with torch.no_grad():                               # <-- no_grad context
+        resnet(t_img.unsqueeze(0))                       # <-- unsqueeze
+    # Detach our copy function from the layer
+    h.remove()
+    # Return the feature vector
+    return my_embedding
+
+#for image in result:
+  #d[image]=get_vector(Image.open(image).convert('RGB')).numpy()
+
+image=st.file_uploader(label="upload your own file",type="jpg")
+if image is None:
+    st.write("upload an image")
+else:
+    input=get_vector(Image.open(image).convert('RGB')).numpy()
+    featurelist=[]
+    for img in data:
+        featurelist.append(data[img])
+    neighbors = NearestNeighbors(n_neighbors=10, algorithm='brute',metric='euclidean').fit(featurelist)
+    distances, indices = neighbors.kneighbors(input.reshape(1,-1))
+    simular=[]
+    for i in range(10):
+        simular.append(result[indices[0][i]])
+    st.image(simular,caption=simular)