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Main.py
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Main.py
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import streamlit as st
from PIL import Image
import tensorflow
from tensorflow.keras.preprocessing import image
from tensorflow.keras.layers import GlobalMaxPool2D
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
import numpy as np
from numpy.linalg import norm
import os
import pickle
from sklearn.neighbors import NearestNeighbors
# ------------------ DEPLOYING PROJECT ON STREAMLIT -------------------
st.title("Fashion Reverse Image Search")
# -------------------- BUILDING MODEL ------------------------
model = ResNet50(weights="imagenet",
include_top=False,
input_shape=(224, 224, 3))
model.trainable = False
model = tensorflow.keras.Sequential([
model,
GlobalMaxPool2D()
])
feature_list = pickle.load(open("Embedding.pkl", "rb"))
filenames = pickle.load(open("Filenames.pkl", "rb"))
# ----------------- CREATING FUNCTIONS --------------------
# creating function to save uploaded image to specific location
def Save_file(uploaded_file):
try:
with open(os.path.join("uploads", uploaded_file.name), "wb") as f:
f.write(uploaded_file.getbuffer())
return 1
except:
return 0
# function for extracting features from image
def Extract_features(img_path, model):
img = image.load_img(img_path, target_size=(224, 224))
img_array = image.img_to_array(img)
expanded_img_array = np.expand_dims(img_array, axis=0)
preprossed_img = preprocess_input(expanded_img_array)
result = model.predict(preprossed_img).flatten()
return result / norm(result)
# recommending 5 nearest images which is relevant to input image
def Recommend(features, features_list):
neighbours = NearestNeighbors(n_neighbors=5,
algorithm="brute",
metric="euclidean")
neighbours.fit(feature_list)
distances, indices = neighbours.kneighbors([features])
return indices
# ------------------------------------
# save uploaded file
uploaded_file = st.file_uploader("Upload An Image")
if uploaded_file is not None:
if Save_file(uploaded_file):
# displaying file
display_img = Image.open(uploaded_file)
st.image(display_img)
# feature extraction
features = Extract_features(os.path.join("uploads", uploaded_file.name), model)
# recommendation
indices = Recommend(features, feature_list)
# show
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.image(filenames[indices[0][0]])
with col2:
st.image(filenames[indices[0][1]])
with col3:
st.image(filenames[indices[0][2]])
with col4:
st.image(filenames[indices[0][3]])
with col5:
st.image(filenames[indices[0][4]])
else:
st.header("Fail To Upload File Please Try Again")